From 026080b40f1de07a09f21d313dfd847c247fd7d6 Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Sun, 28 Jun 2026 17:59:32 +0200 Subject: [PATCH 01/13] Add standardizer with Scopus mapping and WoS type contracts --- www/services/standardizer.py | 136 +++++++++++++++++++++++++++++++++++ 1 file changed, 136 insertions(+) create mode 100644 www/services/standardizer.py diff --git a/www/services/standardizer.py b/www/services/standardizer.py new file mode 100644 index 000000000..f53aa3ced --- /dev/null +++ b/www/services/standardizer.py @@ -0,0 +1,136 @@ +""" +standardizer.py — Source-agnostic ETL transform layer for bibliometrix-python. + +Maps heterogeneous bibliographic exports (Scopus, Dimensions, PubMed, OpenAlex) +onto the Web of Science (WoS) internal schema used by the analytical functions +in services/ and functions/. Replicates the conceptual role of convert2df() in +the R bibliometrix package. +""" + +import pandas as pd +import numpy as np + +# ---------------------------------------------------------------------- +# TYPE CONTRACTS +# ---------------------------------------------------------------------- +LIST_COLUMNS = ["AU", "AF", "C1", "CR", "DE", "ID"] +INT_COLUMNS = ["TC"] + +TARGET_SCHEMA = [ + "DB", "UT", "DI", "PMID", "TI", "SO", "JI", "PY", "DT", "LA", "TC", + "AU", "AF", "C1", "RP", "CR", "DE", "ID", "AB", "VL", "IS", "BP", "EP", "SR", +] + +# ---------------------------------------------------------------------- +# MAPPING DICTIONARIES: {source_column_name: WoS_tag} +# ---------------------------------------------------------------------- +SCOPUS_MAP = { + "Authors": "AU", + "Author full names": "AF", + "Title": "TI", + "Year": "PY", + "Source title": "SO", + "Abbreviated Source Title": "JI", + "Volume": "VL", + "Issue": "IS", + "Page start": "BP", + "Page end": "EP", + "Cited by": "TC", + "DOI": "DI", + "Affiliations": "C1", + "Correspondence Address": "RP", + "Abstract": "AB", + "Author Keywords": "DE", + "Index Keywords": "ID", + "References": "CR", + "PubMed ID": "PMID", + "Language of Original Document": "LA", + "Document Type": "DT", + "EID": "UT", +} + +DIMENSIONS_MAP = { + # filled later from a real Dimensions XLSX export +} + +OPENALEX_MAP = { + # filled in the Advanced API layer +} + +# Dispatcher: source name -> (mapping dict, DB label, multi-value delimiter) +SOURCE_REGISTRY = { + "scopus": (SCOPUS_MAP, "SCOPUS", ";"), + "dimensions": (DIMENSIONS_MAP, "DIMENSIONS", ";"), + "openalex": (OPENALEX_MAP, "OPENALEX", "|"), +} + + +def standardize(raw_df: pd.DataFrame, source: str) -> pd.DataFrame: + """ + Transform a raw source DataFrame into the standardized WoS schema. + + Args: + raw_df: The raw DataFrame as loaded from a source export or API. + source: Source key, one of SOURCE_REGISTRY (e.g. "scopus"). + + Returns: + A DataFrame conforming to TARGET_SCHEMA with enforced type contracts. + """ + if source not in SOURCE_REGISTRY: + raise ValueError(f"Unknown source '{source}'. Known: {list(SOURCE_REGISTRY)}") + + mapping, db_label, delimiter = SOURCE_REGISTRY[source] + + df = _rename_columns(raw_df, mapping) + df = _ensure_all_columns(df) + df = _enforce_types(df, delimiter) + df["DB"] = db_label + # Keep ONLY the standardized WoS schema; drop unmapped source columns + # (this is what removes the leftover NaN-bearing Scopus extras) + df = df[TARGET_SCHEMA] + return df + + + + + +def _rename_columns(df: pd.DataFrame, mapping: dict) -> pd.DataFrame: + """Rename source columns to WoS tags using the mapping dictionary.""" + return df.rename(columns=mapping) + + +def _ensure_all_columns(df: pd.DataFrame) -> pd.DataFrame: + """Add any missing target columns so the schema is always complete.""" + for col in TARGET_SCHEMA: + if col not in df.columns: + df[col] = np.nan + return df + + +def _enforce_types(df: pd.DataFrame, delimiter: str) -> pd.DataFrame: + """ + Apply type contracts: + - list columns -> list[str], nulls -> [] + - int columns -> int, nulls -> 0 + - scalar str -> str, nulls -> "" + """ + for col in LIST_COLUMNS: + df[col] = df[col].apply(lambda v: _to_list(v, delimiter)) + + for col in INT_COLUMNS: + df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0).astype(int) + + scalar_cols = [c for c in TARGET_SCHEMA if c not in LIST_COLUMNS + INT_COLUMNS] + for col in scalar_cols: + df[col] = df[col].fillna("").astype(str).replace("nan", "") + + return df + + +def _to_list(value, delimiter: str) -> list: + """Convert a delimited string / NaN / list into a clean list[str].""" + if isinstance(value, list): + return [str(x).strip() for x in value if str(x).strip()] + if value is None or (isinstance(value, float) and pd.isna(value)): + return [] + return [part.strip() for part in str(value).split(delimiter) if part.strip()] \ No newline at end of file From ec11f7e5634af8cf27528f01d4c2dc8a16e5e493 Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Mon, 29 Jun 2026 08:22:07 +0200 Subject: [PATCH 02/13] Cast PY to int in standardizer to support year arithmetic (CAGR, etc.) --- www/services/standardizer.py | 39 ++++++++++++++++++++++++++++++++++-- 1 file changed, 37 insertions(+), 2 deletions(-) diff --git a/www/services/standardizer.py b/www/services/standardizer.py index f53aa3ced..25d72d8d5 100644 --- a/www/services/standardizer.py +++ b/www/services/standardizer.py @@ -14,7 +14,7 @@ # TYPE CONTRACTS # ---------------------------------------------------------------------- LIST_COLUMNS = ["AU", "AF", "C1", "CR", "DE", "ID"] -INT_COLUMNS = ["TC"] +INT_COLUMNS = ["TC","PY"] TARGET_SCHEMA = [ "DB", "UT", "DI", "PMID", "TI", "SO", "JI", "PY", "DT", "LA", "TC", @@ -133,4 +133,39 @@ def _to_list(value, delimiter: str) -> list: return [str(x).strip() for x in value if str(x).strip()] if value is None or (isinstance(value, float) and pd.isna(value)): return [] - return [part.strip() for part in str(value).split(delimiter) if part.strip()] \ No newline at end of file + return [part.strip() for part in str(value).split(delimiter) if part.strip()] + +def validate(df: pd.DataFrame) -> pd.DataFrame: + """ + Validate that a standardized DataFrame conforms to the WoS schema contract. + + Checks: + - every column in TARGET_SCHEMA is present + - no NaN/None values remain + - all LIST_COLUMNS contain Python lists + - all INT_COLUMNS are integer-typed + + Raises: + ValueError: if any contract is violated. + + Returns: + The same DataFrame, unchanged, if all checks pass. + """ + missing = [c for c in TARGET_SCHEMA if c not in df.columns] + if missing: + raise ValueError(f"Missing mandatory columns: {missing}") + + if df.isna().any().any(): + bad = df.columns[df.isna().any()].tolist() + raise ValueError(f"NaN/None values remain in columns: {bad}") + + for col in LIST_COLUMNS: + nonlist = df[col].apply(lambda v: not isinstance(v, list)) + if nonlist.any(): + raise ValueError(f"Column '{col}' contains non-list values.") + + for col in INT_COLUMNS: + if not pd.api.types.is_integer_dtype(df[col]): + raise ValueError(f"Column '{col}' is not integer-typed.") + + return df \ No newline at end of file From 07898c0c1490c22a245209060d4764c1ed66c22f Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Wed, 1 Jul 2026 15:21:05 +0200 Subject: [PATCH 03/13] Patch 2: map C1 from author-affiliations, normalize country names; add test script --- .gitignore | 2 +- sources/samples/scopus_sample.csv | 11 ++++++++++ tests/test_pipeline.py | 36 +++++++++++++++++++++++++++++++ www/services/standardizer.py | 26 +++++++++++++++++++++- 4 files changed, 73 insertions(+), 2 deletions(-) create mode 100644 sources/samples/scopus_sample.csv create mode 100644 tests/test_pipeline.py diff --git a/.gitignore b/.gitignore index 23b99e089..facfd23e5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,4 @@ __pycache__/ bibliovenv/ Bibenv/ -.idea/ \ No newline at end of file +.idea/venv/ diff --git a/sources/samples/scopus_sample.csv b/sources/samples/scopus_sample.csv new file mode 100644 index 000000000..872ae1876 --- /dev/null +++ b/sources/samples/scopus_sample.csv @@ -0,0 +1,11 @@ +"Authors","Author full names","Author(s) ID","Title","Year","Source title","Volume","Issue","Art. No.","Page start","Page end","Cited by","DOI","Link","Affiliations","Authors with affiliations","Abstract","Author Keywords","Index Keywords","Molecular Sequence Numbers","Chemicals/CAS","Tradenames","Manufacturers","Funding Details","Funding Texts","References","Correspondence Address","Editors","Publisher","Sponsors","Conference name","Conference date","Conference location","Conference code","ISSN","ISBN","CODEN","PubMed ID","Language of Original Document","Abbreviated Source Title","Document Type","Publication Stage","Open Access","Source","EID" +"Debnath N.; Farías A.F.; Montejano G.A.; Garis A.G.; Riesco D.; Farías A.A.; Gheita S.M.N.H.","Debnath, Narayan (7003461872); Farías, Andrés Francisco (59030860600); Montejano, Germán Antonio (8321656700); Garis, Ana Gabriela (23004003400); Riesco, Daniel (8321656800); Farías, Andrés Alejandro (59033576600); Gheita, Sara Mohamed Naguib Hassan (60661043900)","7003461872; 59030860600; 8321656700; 23004003400; 8321656800; 59033576600; 60661043900","Applications of Homomorphic Encryption Towards Preserving Healthcare Privacy in the Context of Artificial Intelligence","2027","Communications in Computer and Information Science","2907 CCIS","","","106","115","0","10.1007/978-3-032-29003-8_8","https://www.scopus.com/pages/publications/105042276140?origin=resultslist","School of Computing and Information Technology, Easten International University, Binh Duong, Thu Dau Mot, Viet Nam; Academic Department of Physical, Mathematical and Natural Sciences, National University of La Rioja, La Rioja, Argentina; Department of Computer Science, Faculty of Physical-Mathematical and Natural Sciences, National University of San Luis, San Luis, Argentina; Faculty of Media Engineering and Technology, The German University in Cairo, Cairo, Egypt","Debnath N., School of Computing and Information Technology, Easten International University, Binh Duong, Thu Dau Mot, Viet Nam; Farías A.F., Academic Department of Physical, Mathematical and Natural Sciences, National University of La Rioja, La Rioja, Argentina; Montejano G.A., Department of Computer Science, Faculty of Physical-Mathematical and Natural Sciences, National University of San Luis, San Luis, Argentina; Garis A.G., Department of Computer Science, Faculty of Physical-Mathematical and Natural Sciences, National University of San Luis, San Luis, Argentina; Riesco D., Department of Computer Science, Faculty of Physical-Mathematical and Natural Sciences, National University of San Luis, San Luis, Argentina; Farías A.A., Academic Department of Physical, Mathematical and Natural Sciences, National University of La Rioja, La Rioja, Argentina; Gheita S.M.N.H., Faculty of Media Engineering and Technology, The German University in Cairo, Cairo, Egypt","The use of Artificial Intelligence (AI) in biomedicine and healthcare is inevitable, given its substantial advantages. In recent years, the healthcare industry has seen big improvements, with AI being very crucial in analyzing large amounts of data. The main objective of this analysis is to improve understanding and spot patterns and risks that might otherwise be hard to detect. This approach is successful not only in biomedical research, but also in the diagnosis of diseases and patient participation in hospital care. It's also significant to remember that using AI can breach data privacy. Since AI relies heavily on collecting personal data, it is crucial to maintain data confidentiality. Numerous researches have shown that AI techniques have prominent effect across biomedical and healthcare domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.","AI; Healthcare; Homomorphic Encryption; Privacy Preservation","Diagnosis; Hospitals; Privacy-preserving techniques; Biomedical research; Diagnoses of disease; Healthcare; Healthcare industry; Ho-momorphic encryptions; Homomorphic-encryptions; Hospital care; Large amounts of data; Privacy preservation; Spot pattern; Artificial intelligence; Health care","","","","","","","Binjubeir M.; Ahmed A.A.; Ismail M.A.B.; Sadiq A.S.; Khan M.K., Comprehensive survey on big data privacy protection, IEEE Access, 8, pp. 20067-20079, (2019); Zhang D., Big data security and privacy protection, 8th International Conference on Management and Computer Science (ICMCS 2018), pp. 275-278, (2018); Song L.; Mittal P., Systematic evaluation of privacy risks of machine learning models, 30th USENIX Security Symposium (USENIX Security 21), pp. 2615-2632, (2021); Ram Mohan Rao P.; Murali Krishna S.; Siva Kumar A.P., Privacy preservation techniques in big data analytics: a survey, J. Big Data, 5, 1, (2018); Zhang C.; Xie Y.; Bai H.; Yu B.; Li W.; Gao Y., A survey on federated learning, Knowl.-Based Syst, 216, (2021); Acar A.; Aksu H.; Uluagac A.S.; Conti M., A survey on homomorphic encryption schemes: theory and implementation, ACM Comput. Surv. (Csur), 51, 4, pp. 1-35, (2018); Zhao C., Secure multi-party computation: theory, practice and applications, Inf. Sci, 476, pp. 357-372, (2019); Yale A.; Dash S.; Dutta R.; Guyon I.; Pavao A.; Bennett K.P., Generation and evaluation of privacy preserving synthetic health data, Neurocomputing, 416, pp. 244-255, (2020); Cha S.C.; Hsu T.Y.; Xiang Y.; eh K.H., Privacy enhancing technologies in the Internet of Things: Perspectives and challenges, IEEE Internet Things J, 6, 2, pp. 2159-2187, (2018); Majeed A.; Lee S., Anonymization techniques for privacy preserving data publishing: a comprehensive survey, IEEE Access, 9, pp. 8512-8545, (2020); Kim B.; Hwang S.J., Preventing unintended memorization by covering with over-memorization; Alloghani M., A systematic review on the status and progress of homomorphic encryption technologies, J. Inform. Secur. Appl, 48, (2019)","","Bandi A.; Hossain M.; Mitra R.","Springer Science and Business Media Deutschland GmbH","International Society for Computers and Their Applications","41st International Conference on Computers and Their Applications, CATA 2026","23 March 2026 through 25 March 2026","Honolulu","354409","18650929","978-303229002-1","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-105042276140" +"Xu L.; Berisha V.; Liss J.","Xu, Lingfeng (57204030074); Berisha, Visar (13608786300); Liss, Julie (7004567917)","57204030074; 13608786300; 7004567917","Evaluating the interpretability of clinical speech AI models: Lessons from two user studies","2027","Computer Speech and Language","101","","101997","","","0","10.1016/j.csl.2026.101997","https://www.scopus.com/pages/publications/105037812335?origin=resultslist","College of Health Solutions, Arizona State University, 550 N 3rd Street, Phoenix, 85004, AZ, United States; School of Electrical, Computer and Energy Engineering, Arizona State University, 650 E. Tyler Mall, Tempe, 85287, AZ, United States","Xu L., College of Health Solutions, Arizona State University, 550 N 3rd Street, Phoenix, 85004, AZ, United States; Berisha V., College of Health Solutions, Arizona State University, 550 N 3rd Street, Phoenix, 85004, AZ, United States, School of Electrical, Computer and Energy Engineering, Arizona State University, 650 E. Tyler Mall, Tempe, 85287, AZ, United States; Liss J., College of Health Solutions, Arizona State University, 550 N 3rd Street, Phoenix, 85004, AZ, United States","The deployment of Artificial intelligence (AI) in clinical speech applications has been limited in large part by the lack of interpretability, which is essential for establishing clinician trust and enabling effective decision support. Although methods such as SHapley Additive exPlanations (SHAP) aim to improve transparency in many clinical domains, their applicability to clinical speech-language pathology practice is uncertain. Since these methods rely on data modalities like acoustic signal features and spectrograms, which are unfamiliar to clinicians and misaligned with clinical workflows, the resulting interpretations may introduce additional burden and bias rather than provide clinically meaningful insight. To better understand this challenge, we conducted two consecutive user studies to systematically evaluate a commonly used SHAP-based interpretation design (a bar chart showing the influence of acoustic features on AI decisions) in dysarthria detection. Building on our prior works, eight factors were examined: faithfulness, computational efficiency, cognitive load, human–AI task performance, mental model, user trust, clinical understandability, and decision relevance. The results reveal a previously unrecognized risk in current interpretation practices. The seemingly intuitive bar-chart design frequently misled participating speech-language pathology (SLP) students to interpret feature influence as an indicator of clinical severity. Other findings include difficulty understanding AI mechanisms, discrepancies between human and model reasoning, and the limited ability of interpretations to address clinical questions. Through this work, we highlight the need for interpretation designs that are more closely aligned with clinical reasoning patterns and suggest practical considerations for developing speech-based AI systems that can be meaningfully integrated into clinical practice. © 2026 Elsevier Ltd.","Interpretability; SHAP; Speech-language pathology; User study","Artificial intelligence; Pathology; Speech communication; Speech recognition; Bar chart; Decision supports; Intelligence models; Interpretability; Large parts; Shapley; Shapley additive explanation; Speech applications; Speech-language pathology; User study; Decision support systems","","","","","","","Berisha V.; Liss J.M., Responsible development of clinical speech AI: Bridging the gap between clinical research and technology, NPJ Digit. Med., 7, 1, (2024); Brooke J., SUS-a quick and dirty usability scale, Usability Eval. Ind., 189, 194, pp. 4-7, (1996); Chattopadhay A.; Sarkar A.; Howlader P.; Balasubramanian V.N., Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks, 2018 IEEE Winter Conference on Applications of Computer Vision, pp. 839-847, (2018); Di Martino F.; Delmastro F., Explainable AI for clinical and remote health applications: A survey on tabular and time series data, Artif. Intell. Rev., 56, 6, pp. 5261-5315, (2023); Doshi-Velez F.; Kim B., Towards a rigorous science of interpretable machine learning, (2017); Enderby P.; Palmer R., Frenchay Dysarthria Assessment, (2008); Epley N.; Waytz A.; Cacioppo J.T., On seeing human: A three-factor theory of anthropomorphism, Psychol Rev, 114, 4, (2007); Feng K.; Chaspari T., A pilot study on clinician-AI collaboration in diagnosing depression from speech, 2024 IEEE EMBS International Conference on Biomedical and Health Informatics, pp. 1-8, (2024); De la Fuente Garcia S.; Ritchie C.W.; Luz S., Artificial intelligence, speech, and language processing approaches to monitoring Alzheimer’s disease: A systematic review, J. Alzheimer’s Dis., 78, 4, pp. 1547-1574, (2020); Ghorayeb A.; Darbyshire J.L.; Wronikowska M.W.; Watkinson P.J., Design and validation of a new healthcare systems usability scale (HSUS) for clinical decision support systems: A mixed-methods approach, BMJ Open, 13, 1, (2023); Hajesmaeel-Gohari S.; Bahaadinbeigy K., The most used questionnaires for evaluating telemedicine services, BMC Med. Inform. Decis. Mak., 21, 1, pp. 1-11, (2021); Harari R.E.; Altaweel A.; Ahram T.; Keehner M.; Shokoohi H., A randomized controlled trial on evaluating clinician-supervised generative AI for decision support, Int. J. Med. Inform., 195, (2025); Hoffman R.R.; Mueller S.T.; Klein G.; Litman J., Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance, Front. Comput. Sci., 5, (2023); Hyzy M.; Bond R.; Mulvenna M.; Bai L.; Dix A.; Leigh S.; Hunt S., System usability scale benchmarking for digital health apps: Meta-analysis, JMIR MHealth UHealth, 10, 8, (2022); Jian J.-Y.; Bisantz A.M.; Drury C.G., Foundations for an empirically determined scale of trust in automated systems, Int. J. Cogn. Ergon., 4, 1, pp. 53-71, (2000); Jin W.; Li X.; Fatehi M.; Hamarneh G., Guidelines and evaluation of clinical explainable AI in medical image analysis, Med. Image Anal., 84, (2023); Kohn S.C.; De Visser E.J.; Wiese E.; Lee Y.-C.; Shaw T.H., Measurement of trust in automation: A narrative review and reference guide, Front. Psychol., 12, (2021); Kraus M.; Seldschopf P.; Minker W., Towards the development of a trustworthy chatbot for mental health applications, International Conference on Multimedia Modeling, pp. 354-366, (2021); Kumarakulasinghe N.B.; Blomberg T.; Liu J.; Leao A.S.; Papapetrou P., Evaluating local interpretable model-agnostic explanations on clinical machine learning classification models, 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, pp. 7-12, (2020); Lage I.; Chen E.; He J.; Narayanan M.; Kim B.; Gershman S.J.; Doshi-Velez F., Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 7, pp. 59-67, (2019); Lewis J.R.; Sauro J., Item benchmarks for the system usability scale, J. Usability Stud., 13, 3, (2018); Loh H.W.; Ooi C.P.; Seoni S.; Barua P.D.; Molinari F.; Acharya U.R., Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022), Comput. Methods Programs Biomed., 226, (2022); Lundberg S.M.; Lee S.-I., A unified approach to interpreting model predictions, Adv. Neural Inf. Process. Syst., 30, (2017); Lyell D.; Coiera E., Automation bias and verification complexity: A systematic review, J. Am. Med. Inform. Assoc., 24, 2, pp. 423-431, (2017); Maramba I.; Chatterjee A.; Newman C., Methods of usability testing in the development of eHealth applications: A scoping review, Int. J. Med. Inform., 126, pp. 95-104, (2019); Mohseni S.; Zarei N.; Ragan E.D., A multidisciplinary survey and framework for design and evaluation of explainable AI systems, ACM Trans. Interact. Intell. Syst. (TiiS), 11, 3-4, pp. 1-45, (2021); Nair K.; Malaeekeh R.; Schabort I.; Taenzer P.; Radhakrishnan A.; Guenter D., A clinical decision support system for chronic pain management in primary care: Usability testing and its relevance, BMJ Health & Care Inform., 22, 3, (2015); Placani A., Anthropomorphism in AI: Hype and fallacy, AI Ethics, 4, 3, pp. 691-698, (2024); Ramaswamy H.G., Ablation-CAM: Visual explanations for deep convolutional network via gradient-free localization, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 983-991, (2020); Ribeiro M.T.; Singh S.; Guestrin C., Why should I trust you?” Explaining the predictions of any classifier, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135-1144, (2016); Salahuddin Z.; Woodruff H.C.; Chatterjee A.; Lambin P., Transparency of deep neural networks for medical image analysis: A review of interpretability methods, Comput. Biol. Med., 140, (2022); Salih A.M.; Galazzo I.B.; Gkontra P.; Rauseo E.; Lee A.M.; Lekadir K.; Radeva P.; Petersen S.E.; Menegaz G., A review of evaluation approaches for explainable AI with applications in cardiology, Artif. Intell. Rev., 57, 9, (2024); Selvaraju R.R.; Cogswell M.; Das A.; Vedantam R.; Parikh D.; Batra D., Grad-CAM: Visual explanations from deep networks via gradient-based localization, Proceedings of the IEEE International Conference on Computer Vision, pp. 618-626, (2017); Tatano R.; Mastropietro A.; Busto E.; Vaccarino F., Sanity check for Shapley values-based explanations of deep neural networks predictions, 2022 IEEE 10th International Conference on Healthcare Informatics, pp. 644-646, (2022); Tbaishat D.; Al-Shafei R.; Odeh M., The role of AI in the diagnosis of speech and language disorders: A systematic mapping study, Digit. Health, 11, (2025); Tonekaboni S.; Joshi S.; McCradden M.D.; Goldenberg A., What clinicians want: Contextualizing explainable machine learning for clinical end use, Machine Learning for Healthcare Conference, pp. 359-380, (2019); White L.; Mattys S.L., Calibrating rhythm: First language and second language studies, J. Phon., 35, 4, pp. 501-522, (2007); Xu L.; Berisha V.; Utianski R.L.; Liss J., Evaluating model interpretability in speech-based clinical artificial intelligence systems, Perspect. ASHA Spec. Interes. Groups, 10, 5, pp. 1637-1648, (2025); Xu L.; Liss J.; Berisha V., Dysarthria detection based on a deep learning model with a clinically-interpretable layer, JASA Express Lett., 3, 1, (2023); Yang X.J.; Schemanske C.; Searle C., Toward quantifying trust dynamics: How people adjust their trust after moment-to-moment interaction with automation, Hum. Factors, 65, 5, pp. 862-878, (2023); Zhang Y.; Li Y.; Brown H.; Rezaei M.; Bischl B.; Torr P.; Khakzar A.; Kawaguchi K., AttributionLab: Faithfulness of feature attribution under controllable environments, (2023); Zhang Y.; Tino P.; Leonardis A.; Tang K., A survey on neural network interpretability, IEEE Trans. Emerg. Top. Comput. Intell., 5, 5, pp. 726-742, (2021); Zhou B.; Khosla A.; Lapedriza A.; Oliva A.; Torralba A., Learning deep features for discriminative localization, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921-2929, (2016)","L. Xu; College of Health Solutions, Arizona State University, Phoenix, 550 N 3rd Street, 85004, United States; email: lingfen3@asu.edu","","Academic Press","","","","","","08852308","","CSPLE","","English","Comput Speech Lang","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-105037812335" +"Ghimire K.; Yuan Q.","Ghimire, Keshav (60698372000); Yuan, Quan (56214636000)","60698372000; 56214636000","Demon Forge Hub: An AI-Enhanced Gamified NFT Marketplace on Polygon","2027","Communications in Computer and Information Science","2907 CCIS","","","548","561","0","10.1007/978-3-032-29003-8_35","https://www.scopus.com/pages/publications/105042281711?origin=resultslist","Department of Computer Science and Electrical Engineering, The University of Texas Permian Basin, Odessa, 79762, TX, United States","Ghimire K., Department of Computer Science and Electrical Engineering, The University of Texas Permian Basin, Odessa, 79762, TX, United States; Yuan Q., Department of Computer Science and Electrical Engineering, The University of Texas Permian Basin, Odessa, 79762, TX, United States","Non-Fungible Tokens (NFTs) are widely adopted across digital art, gaming, and decentralized applications. However, existing NFT marketplaces largely treat NFTs as static assets with limited interactivity and narrative depth. This paper presents Demon Forge Hub, a decentralized NFT marketplace designed to address these limitations through on-chain gamification, structured storytelling, and improved accessibility. Built using ERC-721 smart contracts deployed on the Polygon Mumbai network, the platform enables NFTs to function as evolving digital characters with persistent attributes such as levels, rarity, and battle outcomes. Metadata is stored using NFT.Storage and IPFS to ensure verifiable persistence. Additionally, an AI-assisted lore generation module produces structured narrative identities for each NFT. The proposed system demonstrates how NFT marketplaces can transition toward interactive, narrative-driven digital ecosystems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.","AI; Blockchain; GameFi; IPFS; Marketplace; NFT; Polygon; Smart Contracts; Web3","Arts computing; Commerce; Smart contract; Block-chain; Decentralised; Digital art; Gamefi; Gamification; Interactivity; IPFS; Non-fungible token; Polygon; Web3; Artificial intelligence","","","","","","","Benet J., IPFS: content addressed, versioned, P2P file system, (2014); Delmolino K.; Arnett M.; Kosba A.; Miller A.; Shi E., Step by step towards creating a safe smart contract: lessons and insights from a cryptocurrency lab, Proceedings of the 2016 ACM Workshop on Blockchain, Cryptocurrencies and Contracts, pp. 79-86, (2016); Frank R.; Ma W., Smart contract security: a practitioner’s perspective, IEEE Secur. Priv, 19, 5, pp. 45-51, (2021); Nomic Foundation: Hardhat: Ethereum development environment, (2020); Labs P., Polygon developer documentation, (2021); Wang Q.; Li R.; Wang Q.; Chen S., Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges, (2021); Wang W.; Wang Y., Usability evaluation of browser-based blockchain wallets, Int. J. Hum.-Comput. Interact, 34, 10, pp. 891-902, (2018); Wood G., Ethereum: a secure decentralized generalized transaction ledger, (2014); Xu X.; Weber I.; Staples M.; Zhu L.; Zou J., An architectural framework for decentralized applications, Softw. Pract. Exp, 49, 12, pp. 1689-1710, (2019); Yannakakis G.N.; Togelius J., Artificial intelligence and games: a survey, IEEE Trans. Games, 10, 2, pp. 99-118, (2018)","Q. Yuan; Department of Computer Science and Electrical Engineering, The University of Texas Permian Basin, Odessa, 79762, United States; email: yuan_q@utpb.edu","Bandi A.; Hossain M.; Mitra R.","Springer Science and Business Media Deutschland GmbH","International Society for Computers and Their Applications","41st International Conference on Computers and Their Applications, CATA 2026","23 March 2026 through 25 March 2026","Honolulu","354409","18650929","978-303229002-1","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-105042281711" +"Chang H.-J.; Çatal Y.; Wolman A.; Ibáñez A.; Smith D.; Su I.-W.; Cheng K.-Y.; Northoff G.","Chang, Han-Jen (60156632000); Çatal, Yasir (57820111900); Wolman, Angelika (57196391167); Ibáñez, Agustín (55172691400); Smith, David (58237963800); Su, I-Wen (60410267300); Cheng, Kai-Yuan (60410267200); Northoff, Georg (55004275200)","60156632000; 57820111900; 57196391167; 55172691400; 58237963800; 60410267300; 60410267200; 55004275200","The Dynamics of Human and AI-Generated Language: How Semantic Content Fluctuates Across Different Timescales","2027","Computer Speech and Language","101","","102013","","","0","10.1016/j.csl.2026.102013","https://www.scopus.com/pages/publications/105041644251?origin=resultslist","National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Mental Health Research, University of Ottawa, Ottawa, Canada; Mind, Brain Imaging and Neuroethics Unit, 1145 Carling Avenue, Ottawa, K1Z 7K4, ON, Canada; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland; Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, 34815, Turkey; Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, 08005, Spain; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Graduate Institute of Linguistics, National Taiwan University, Taipei, Taiwan","Chang H.-J., National Yang Ming Chiao Tung University, Taipei, Taiwan, Mind, Brain Imaging and Neuroethics Unit, 1145 Carling Avenue, Ottawa, K1Z 7K4, ON, Canada; Çatal Y., Institute of Mental Health Research, University of Ottawa, Ottawa, Canada, Mind, Brain Imaging and Neuroethics Unit, 1145 Carling Avenue, Ottawa, K1Z 7K4, ON, Canada; Wolman A., Institute of Mental Health Research, University of Ottawa, Ottawa, Canada, Mind, Brain Imaging and Neuroethics Unit, 1145 Carling Avenue, Ottawa, K1Z 7K4, ON, Canada; Ibáñez A., Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile, Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland, Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, 34815, Turkey, Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, 08005, Spain, Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Smith D., Institute of Mental Health Research, University of Ottawa, Ottawa, Canada; Su I.-W., Graduate Institute of Linguistics, National Taiwan University, Taipei, Taiwan; Cheng K.-Y., National Yang Ming Chiao Tung University, Taipei, Taiwan; Northoff G., Institute of Mental Health Research, University of Ottawa, Ottawa, Canada, Mind, Brain Imaging and Neuroethics Unit, 1145 Carling Avenue, Ottawa, K1Z 7K4, ON, Canada","Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content. However, we still lack simple, interpretable time-series features that capture how generic versus specific content is distributed over time, and that can be used to compare human and AI-generated speech. We introduce a semantic-timescale analysis pipeline that turns word-level transcripts with timestamps into semantic time-series. For each spoken narrative, we compute (i) semantic specificity using WordNet-based word depth and (ii) contextual similarity using SBERT embeddings and quantify their temporal dependence using autocorrelation-window measures (ACW-0 and related metrics). We then compare original speech to multiple shuffled controls that selectively disrupt lexical identity, temporal order, and word duration. Across human-read autobiographical narratives, text-to-speech (TTS)-rendered readings, and LLM-generated texts rendered with TTS, we find that segments with longer ACW-0 in the semantic time-series tend to contain more generic vocabulary, whereas segments with shorter ACW-0 are enriched in more specific words. These associations are strongly attenuated or abolished when word order and timing are randomized, indicating that ACW-based measures capture non-trivial temporal organization of semantic content beyond static lexical distributions. Our results suggest that ACW-based semantic timescales are a useful family of features for analyzing and comparing the temporal structure of human and AI-generated speech. © 2026 Elsevier Ltd.","Autocorrelation windows; Large language models; Semantics; Speech processing; Speech timescales; WordNet","Audio signal processing; Autocorrelation; Computational linguistics; Speech communication; Time series; Time series analysis; Auto correlation; Autocorrelation window; Language model; Large language model; Semantic content; Speech timescale; Text to speech; Time-scales; Times series; Wordnet; Semantics; Speech processing","","","","","","","Ao Y.; Klar P.; Catal Y.; Wang Y.; Northoff G., Author correction: infra-slow scale-free dynamics modulate the connection of neural and behavioral variability during attention, Commun. 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Chang; National Yang Ming Chiao Tung University, Taipei, Taiwan; email: changtommy16.md08@nycu.edu.tw","","Academic Press","","","","","","08852308","","CSPLE","","English","Comput Speech Lang","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-105041644251" +"Shi X.(.; Leung X.Y.; Bai B.; Buhalis D.","Shi, Xiaolin (Crystal) (57210945471); Leung, Xi Yu (37077607700); Bai, Billy (16479699100); Buhalis, Dimitrios (6603014980)","57210945471; 37077607700; 16479699100; 6603014980","Artificial intelligence-based employee selection in tourism and hospitality: Justice, inclusion, and gender differences in resume screening and interviews","2027","Tourism Management","118","","105478","","","0","10.1016/j.tourman.2026.105478","https://www.scopus.com/pages/publications/105042461607?origin=resultslist","School of Hotel and Tourism Management, The Hong Kong Polytechnic University, 17 Science Museum Road, TST East, Hong Kong; School of Consumer Sciences, College of Health and Human Sciences, Kansas State University, United States; Bournemouth University, United Kingdom","Shi X.(., School of Hotel and Tourism Management, The Hong Kong Polytechnic University, 17 Science Museum Road, TST East, Hong Kong; Leung X.Y., School of Consumer Sciences, College of Health and Human Sciences, Kansas State University, United States; Bai B., School of Hotel and Tourism Management, The Hong Kong Polytechnic University, 17 Science Museum Road, TST East, Hong Kong; Buhalis D., Bournemouth University, United Kingdom","As artificial intelligence (AI) becomes more prevalent in human resource management, its role in tourism and hospitality hiring remains underexplored. This study investigates job applicants’ perceptions of AI in employee selection, focusing on resume screening and interviews. Drawing on fairness heuristic theory and social role theory, we examine how AI affects perceived justice, inclusion, and organizational attractiveness. Four scenario-based experiments and one qualitative study were conducted with job applicants comparing AI-based and human-based selection methods. Results show that human recruiters are generally perceived as more just and inclusive, enhancing organizational attractiveness. However, gender moderates these effects: females favor AI-based resume screening, whereas males are more receptive to AI-based interviews. These findings provide insights for organizations seeking to integrate AI into employee selection while maintaining justice and inclusion. As AI becomes more prevalent in hiring, organizations should prioritize human oversight, ensuring that AI supports rather than replaces human. © 2026 Elsevier Ltd.","Artificial intelligence (AI); Employee selection; Fairness heuristic theory; Gender differences; Organizational attractiveness; Social role theory","","","","","","Hong Kong Polytechnic University, HKPU, (H-ZDJ5/P0057951)","Hotel ICON Research Funding Scheme, The Hong Kong Polytechnic University, Hong Kong (Grant#: H-ZDJ5/P0057951).","Acikgoz Y.; Davison K.H.; Compagnone M.; Laske M., Justice perceptions of artificial intelligence in selection, International Journal of Selection and Assessment, 28, 4, pp. 399-416, (2020); Adepoju O.D.; Tijani B.; Karera S., Artificial intelligence skepticism in career domains, International Journal for Digital Society, 15, 1, pp. 1880-1888, (2024); Aguinis H.; Beltran J.R.; Cope A., How to use generative AI as a human resource management assistant, Organizational Dynamics, 53, 1, (2024); Albaroudi E.; Mansouri T.; Alameer A., A comprehensive review of AI techniques for addressing algorithmic bias in job hiring, ai, 5, 1, pp. 383-404, (2024); Albassam W.A., The power of artificial intelligence in recruitment: An analytical review of current AI-based recruitment strategies, International Journal of Professional Business Review, 8, 6, pp. 1-25, (2023); Ambrose M.L.; Schminke M., The role of overall justice judgments in organizational justice research: A test of mediation, Journal of Applied Psychology, 94, 2, pp. 491-500, (2009); Balakrishnan J.; Dwivedi Y.K.; Hughes L.; Boy F., Enablers and inhibitors of AI-powered voice assistants: A dual-factor approach by integrating the status quo bias and technology acceptance model, Information Systems Frontiers, pp. 1-22, (2024); Balcioglu Y.S.; Artar M., Artificial intelligence in employee recruitment, Global Business and Organizational Excellence, 43, 5, pp. 56-66, (2024); Bishop J., AI in recruitment: How artificial intelligence helps hiring in 2026, Helioshr, (2026); Breaugh J.A., The contribution of job analysis to recruitment, The Wiley Blackwell Handbook of the psychology of recruitment, selection and employee retention, pp. 12-28, (2017); Buhalis D.; Efthymiou L.; Uzunboylu N.; Thrassou A., Charting the progress of technology adoption in tourism and hospitality in the era of industry 4.0, EuroMed Journal of Business, 19, 1, pp. 1-20, (2024); Buhalis D.; O'Connor P.; Leung R., Smart hospitality: From smart cities and smart tourism towards agile business ecosystems in networked destinations, International Journal of Contemporary Hospitality Management, 35, 1, pp. 369-393, (2022); Cai F.; Zhang J.; Zhang L., The impact of artificial intelligence replacing humans in making human resource management decisions on fairness: A case of resume screening, Sustainability, 16, 9, pp. 1-18, (2024); Chang K., Ethical and operational challenges in AI-empowered employee recruitment: Insights and suggestions to the managers and managerial practitioners, Innovations in Business and Strategic Management, 2, 1, pp. 29-32, (2024); Chapman D.S.; Uggerslev K.L.; Webster J., Applicant reactions to face-to-face and technology-mediated interviews: A field investigation, Journal of Applied Psychology, 88, pp. 944-953, (2003); Choi J.; Chao M.M., For me or against me? 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Shi; School of Hotel and Tourism Management, The Hong Kong Polytechnic University, 17 Science Museum Road, TST East, Hong Kong; email: crystal.shi@polyu.edu.hk","","Elsevier Ltd","","","","","","02615177","","","","English","Tour. Manage.","Article","Final","","Scopus","2-s2.0-105042461607" +"Dui H.; Wang J.; Xia W.; Bu S.; Li H.","Dui, Hongyan (25959546400); Wang, Jiafeng (59224071200); Xia, Wanyun (59113978900); Bu, Shaoyang (57224818403); Li, Helin (60702558000)","25959546400; 59224071200; 59113978900; 57224818403; 60702558000","AI-assisted reliability modeling and autonomous decision optimization of high-performance computing systems in data-rich environments","2027","Reliability Engineering and System Safety","277","","113028","","","0","10.1016/j.ress.2026.113028","https://www.scopus.com/pages/publications/105042430697?origin=resultslist","School of Management, Zhengzhou University, Zhengzhou, 450001, China; School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China; Tangzhi Technology Hunan Development Co., Ltd., Changsha, 410116, China","Dui H., School of Management, Zhengzhou University, Zhengzhou, 450001, China; Wang J., School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China; Xia W., School of Management, Zhengzhou University, Zhengzhou, 450001, China; Bu S., School of Management, Zhengzhou University, Zhengzhou, 450001, China; Li H., Tangzhi Technology Hunan Development Co., Ltd., Changsha, 410116, China","With the proliferation of exascale scientific computing, heterogeneous high-performance computing systems (HHCS) integrating CPU and GPU architectures have become critical infrastructure for modern computational ecosystems. However, extreme workloads simultaneously induce thermal stress-driven hardware degradation, voltage-scaling-triggered soft errors, and interference-caused network transmission failures, collectively threatening system reliability. Existing research suffers from three gaps: fragmented reliability modeling neglecting inter-dimensional coupling, optimization inefficiency from poor initialization in discrete scheduling spaces, and non-adaptive decision-making under varying operational priorities. To address these gaps, this paper proposes an AI-assisted reliability modeling and decision optimization framework for heterogeneous HHCS. First, a three-dimensional coupled reliability model integrates hardware wear, computational execution, and network transmission reliability. Second, an adaptive multi-objective decision framework enables flexible trade-offs among reliability, completion time, and energy consumption. Third, an Improved Elite-guided NSGA-II (IE-NSGA-II) incorporates HEFT-based elite initialization and adaptive mutation operators for discrete scheduling optimization. Simulation experiments on a 20-node heterogeneous cluster demonstrate that IE-NSGA-II achieves 2.77 × to 2.91 × faster convergence than standard NSGA-II, improving system reliability by 9.9% and reducing energy consumption by 10.4% under severe environmental stress. © 2026 Elsevier Ltd.","Computing system; Maintenance; Multi-objective optimization; Reliability","Behavioral research; Computer hardware; Decision making; Economic and social effects; Green computing; Maintenance; Multiobjective optimization; Program processors; Autonomous decision; Computing system; Heterogeneous high performance computing; High performance computing systems; Modeling decisions; Multi-objectives optimization; Network transmission; NSGA-II; Reliability modelling; System reliability; Reliability","","","","","","","Jouppi N.P.; Yoon D.H.; Kurian G., A domain-specific supercomputer for training deep neural networks, Commun. 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Bu; School of Management, Zhengzhou University, Zhengzhou, 450001, China; email: bushaoyangzzu@126.com","","Elsevier Ltd","","","","","","09518320","","RESSE","","English","Reliab Eng Syst Saf","Article","Final","","Scopus","2-s2.0-105042430697" +"Kurt H.I.; Bao L.","Kurt, Halil Ibrahim (57219786821); Bao, Lianzhang (56294614900)","57219786821; 56294614900","Analytical and numerical results for solutions in a mixed-type two-species chemotaxis-competition system: I. Global existence","2027","Nonlinear Analysis: Real World Applications","93","","104677","","","0","10.1016/j.nonrwa.2026.104677","https://www.scopus.com/pages/publications/105041310122?origin=resultslist","Department of Mathematics, Faculty of Science, Karadeniz Technical University, Trabzon, 61080, Turkey; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, 48824, MI, United States","Kurt H.I., Department of Mathematics, Faculty of Science, Karadeniz Technical University, Trabzon, 61080, Turkey; Bao L., Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, 48824, MI, United States","In the current series of research papers, we study the global dynamics of a two-species model that combines mixed-type chemotaxis sensitivities together with Lotka-Volterra competitive kinetics, (0.1) {ut=Δu−χ1∇·(uw∇w)+u(a1−b1u−c1v),x∈Ω,vt=Δv−χ2∇·(v∇z)+v(a2−b2v−c2u),x∈Ω,0=Δw−λ1w+μ1v,x∈Ω,0=Δz−λ2z+μ2u,x∈Ω, under no-flux boundary conditions in a smoothly bounded domain Ω⊂Rn with n ≥ 2, where the parameters χi, λi, μi, ai, bi, ci > 0 for i=1,2 are positive constants. We first wanted to emphasize that the system described by Eq. Eq. (0.1) marks the first comprehensive analysis of a mixed-type chemotaxis competition framework. This model involves two species and two stimuli, integrating both regular and singular sensitivities along with the competitive dynamics characterized by the Lotka-Volterra equations. The key novelty of this work lies in its incorporation of both sensitivity types within a single framework, allowing for a direct comparison and discussion of these two distinct mechanisms within a unified setting. This first installment of the series investigates the Lp -boundedness and global existence of classical solutions to system (0.1) . Under suitable parameter conditions, it is shown that any globally defined positive solution is Lp -bounded for some p > 1. Moreover, for all sufficiently smooth and positive initial data, system (0.1) admits a unique global classical solution in the two-dimensional case, without imposing any additional assumptions on the model parameters. For dimensions n ≥ 3, the existence of global classical solutions to system (0.1) is established under appropriate parameter restrictions. In addition, we present several numerical simulations in one, two, and three spatial dimensions to explore the dynamics of classical solutions. The computational simulations are fully consistent with the analytical results on global existence and further provide insight into the global dynamics of classical solutions such as boundedness (global and uniform sense), persistence (mass and pointwise sense), and stability (coexistence and extinction sense), which will be discussed in the second and third installment of the series. © 2026 Elsevier Ltd.","","Competition; Solution mining; Analytical results; Boundedness; Chemotaxi; Classical solutions; Competition systems; Global classical solution; Global dynamics; Global existence; Mixed type; Numerical results; Dynamics","","","","","","","Keller E.F.; Segel L.A., Initiation of slime mold aggregation viewed as an instability, J. Theor. Biol., 26, pp. 399-415, (1970); Keller E.F.; Segel L.A., Traveling bans of chemotactic bacteria: a theoretical analysis, J. Theor. 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B, 25, pp. 4383-4396, (2020); Liu X.; Zheng J., Convergence rates of solutions in a predator-prey system with indirect pursuit-evasion interaction in domains of arbitrary dimension, Discrete Contin. Dyn. Syst. B, 28, pp. 2269-2293, (2023); Amorim P.; Telch B., A Chemotaxis predator-prey model with indirect pursuit-evasion dynamics and parabolic signal, J. Math. Anal. Appl., 500, (2021); Qi D.; Ke Y., Large time behavior in a predator-prey system with pursuit-evasion interaction, Discrete Contin. Dyn. Syst. B, 27, pp. 4531-4549, (2022); Qiu S.; Mu C.; Yi H., Boundedness and asymptotic stability in a predator-prey Chemotaxis system with indirect pursuit-evasion dynamics, Acta Math. Sci., 42, pp. 1035-1057, (2022); Telch B., Global boundedness in a chemotaxis quasilinear parabolic predator prey system with pursuit-evasion, Nonlinear Anal. RWA, 59, (2021); Wang C.J.; Ke C.H., Global classical solutions to a predator-prey model with nonlinear indirect chemotaxis mechanism, Acta Appl. Math., 190, 1, pp. 1-14, (2024); Zheng J.; Zhang P.; Liu X., Some progress for global existence and boundedness in a multi-dimensional parabolic-elliptic two-species Chemotaxis system with indirect pursuit-evasion interaction, Appl. Math. Lett., 144, (2023); Zheng J.; Zhang P.; Liu X., Global existence and boundedness for an N-dimensional parabolic elliptic chemotaxis-fluid system with indirect pursuit-evasion, J. Differ. Equ., 367, pp. 199-228, (2023); Nagai T.; Senba T., Global existence and blow-up of radial solutions to a parabolic-elliptic system of chemotaxis, Adv. Math. Sci. Appl., 8, pp. 145-156, (1998); Fujie K.; Senba T., Global existence and boundedness in a parabolic-elliptic Keller-Segel system with general sensitivity, Discrete Contin. Dyn. Syst. B, 21, 1, pp. 81-102, (2016); Fujie K.; Winkler M.; Yokota T., Boundedness of solutions to parabolic-elliptic Keller-Segel systems with signal dependent sensitivity, Math. Methods Appl. Sci., 38, 6, pp. 1212-1224, (2015); Kurt H.I., Improvement of criteria for global boundedness in a minimal parabolic-elliptic chemotaxis system with singular sensitivity, Appl. Math. Lett., 167, (2025); Biler P., Global solutions to some parabolic-elliptic systems of chemotaxis, Adv. Math. Appl., 9, pp. 347-359, (1999); Black T., Global generalized solutions to a parabolic-elliptic Keller-Segel system with singular sensitivity, Discrete Contin. Dyn. Syst. S., 13, (2020); Fujie K.; Winkler M.; Yokota T., Blow-up prevention by logistic sources in a parabolic-elliptic Keller-Segel system with singular sensitivity, Nonlinear Anal., 109, pp. 56-71, (2014); Kurt H.I.; Shen W., Finite-time blow-up prevention by logistic source in chemotaxis models with singular sensitivity in any dimensional setting, SIAM J. Math. Anal., 53, 1, pp. 973-1003, (2021); Kurt H.I.; Shen W., Chemotaxis models with singular sensitivity and logistic source: boundedness, persistence, absorbing set, and entire solutions, Nonlinear Anal. Real World Appl., 69, (2023); Cao J.; Wang W.; Yu H., Asymptotic behavior of solutions to two-dimensional chemotaxis system with logistic source and singular sensitivity, J. Math. Anal. Appl., 436, 1, pp. 382-392, (2016); Kurt H.I.; Shen W.; Xue S., Stability, bifurcation and spikes of stationary solutions in a chemotaxis system with singular sensitivity and logistic source, Math. Models Methods Appl. Sci., 34, 9, pp. 1649-1700, (2024); Le M., Blow-up prevention by sub-logistic sources in 2D Keller–Segel chemotaxis systems with superlinear signal production, Z. Angew. Math. Phys., 75, 128, (2024); Le M., Boundedness in a chemotaxis system with weakly singular sensitivity in dimension two with arbitrary sub-quadratic degradation sources, J. Math. Anal. Appl., 542, (2025); Le M., Global existence of solutions in some chemotaxis systems with sub-logistic source under nonlinear Neumann boundary conditions in 2d, Nonlinear Anal., 241, (2024); Le M., Global existence of solutions to the chemotaxis system with logistic source under nonlinear Neumann boundary condition, J. Differ. Equ., 377, pp. 1-37, (2023); Le M.; Kurt H.I., Global boundedness in a chemotaxis-growth system with weak singular sensitivity in any dimensional setting, Nonlinear Anal. Real World Appl., 86, (2025); Le M.; Kurt H.I., Persistence of positive classical solutions in a logistic chemotaxis system with weak singular sensitivity, Discrete Contin. Dyn. Syst. - B, 33, pp. 188-199, (2026); Le M.; Kurt H.I.; Yaprak R., Analytical and numerical analysis of boundedness in a two-species Keller-Segel model with weak nonlinear sensitivity, Commun. Nonlinear Sci. Numer. Simul., 154, (2026); Le M.; Bao L.; Kurt H.I., Can logistic damping prevent blow-up in weak singular sensitivity chemotaxis systems with nonlinear boundary conditions?, Zeitschrift für angewandte Mathematik und Physik, 76, 164, (2025); Zhao X., Boundedness in a logistic chemotaxis system with weakly singular sensitivity in dimension two, Nonlinearity, 36, pp. 3909-3938, (2023); Zhao X., Global solvability in the parabolic-elliptic chemotaxis system with singular sensitivity and logistic source, Czechoslov. Math. J., pp. 1-25, (2023); Kurt H.I.; Shen W., Two-species chemotaxis-competition system with singular sensitivity: global existence, boundedness, and persistence, J. Differ. Equ., 355, pp. 248-295, (2023); Kurt H.I.; Shen W., Stabilization in two-species chemotaxis systems with singular sensitivity and Lotka-Volterra competitive kinetics, Discrete Contin. Dyn. Syst., 44, 4, pp. 882-904, (2024); Annamalai B.; Venugopal P., Finite element analysis of the nonlocal diffusion effect in a two-species chemotaxis system, Discrete Contin. Dyn. Syst. - S, (2024); Au V.V., Multispecies Lotka–Volterra competition–diffusion system with forcing terms depending on the variables, Math. Methods Appl. Sci., 47, 18, pp. 13493-14975, (2024); Bucur V.; Vasiev B., Formation of stationary periodic patterns in a model of two competing populations with chemotaxis, (2024); Li Y.; Mu C.; Pan X., Boundedness and asymptotic stability in a predator-prey system with density-dependent motilities, Discrete Contin. Dyn. Syst. - B, 29, 5, pp. 2192-2212, (2024); Kurt H.I.; Bao L., Analytical and numerical results for solutions in a mixed-type two-species chemotaxis-competition system: II. Global boundedness and persistence, J. Comput. Appl. Math., 488, (2026); Kurt H.I.; Bao L., Analytical and numerical results for solutions in a mixed-type two-species chemotaxis-competition system: III. Uniform boundedness, pointwise persistence, coexistence, and extinction, Preprint, (2026); Henry D., Geometric Theory of Semilinear Parabolic Equations, (1977); Winkler M., Aggregation vs. global diffusive behavior in the higher-dimensional Keller-Segel model, J. Differ. Equ., 248, pp. 2889-2905, (2010); Amann H., Existence and regularity for semilinear parabolic evolution equations, Ann. Scuola Norm. Sup. Pisa Cl Sci., 11, 4, pp. 593-676, (1984); Kurt H.I., Large-time dynamics of solutions in a logistic chemotaxis system with weak singular sensitivity: uniform boundedness, pointwise persistence, and stability, Discrete Contin. Dyn. Syst., 52, pp. 255-292, (2026); Kurt H.I., Boundedness in a chemotaxis system with weak singular sensitivity and logistic kinetics in any dimensional setting, J. Differ. Equ., 416, 2, pp. 1429-1461, (2025)","H.I. Kurt; Department of Mathematics, Faculty of Science, Karadeniz Technical University, Trabzon, 61080, Turkey; email: hkurt@ktu.edu.tr","","Elsevier Ltd","","","","","","14681218","","","","English","Nonlinear Anal. Real World Appl.","Article","Final","","Scopus","2-s2.0-105041310122" +"Primo A.","Primo, Abena (56403012200)","56403012200","Modeling Student AI Stances in Education with Text Mining and Unsupervised Clustering","2027","Communications in Computer and Information Science","2907 CCIS","","","302","313","0","10.1007/978-3-032-29003-8_19","https://www.scopus.com/pages/publications/105042287495?origin=resultslist","Huston-Tillotson University, Austin, 78702, TX, United States","Primo A., Huston-Tillotson University, Austin, 78702, TX, United States","As generative artificial intelligence (AI) becomes embedded in coursework, AI literacy initiatives often emphasize operational competence while underweighting how students narrate AI’s role in learning, authorship, and value. This paper introduces an interpretable natural language processing (NLP) approach for modeling student orientations toward AI in education from reflective essays. Using 48 de-identified undergraduate essays collected across four academic terms, the study combines qualitative coding with term frequency–inverse document frequency (TF–IDF) text representations and unsupervised clustering (k-means clustering and Ward hierarchical clustering). Four recurring narrative orientations—AI Symbiote, Prompt Craftsman, Ethical Guardian, and Resistance Leader—correspond to stable lexical clusters. Results include cluster descriptors and a set of visual diagnostics (principal component analysis (PCA) projection, dendrogram, silhouette analysis, stability across feature caps, and a cluster–archetype overlap heatmap). The paper demonstrates how interpretable text mining can bridge computational modeling and educational research, offering a lightweight taxonomy for AI literacy design and human-centered educational technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.","Clustering; Education Analytics; Text Mining","Curricula; Data mining; Education computing; Educational technology; Engineering education; Ethical aspects; Hierarchical clustering; Inverse problems; K-means clustering; Natural language processing systems; Students; Text processing; Artificial intelligence in education; Clusterings; Courseworks; Education analytic; Language processing; Natural languages; Processing approach; Student orientation; Text-mining; Unsupervised clustering; Principal component analysis","","","","","","","Akter S.S., Costs and benefits of AI-enabled topic modeling in p-20 education research, Proceedings of the ACL Workshop on Building Educational Applications (BEA), (2025); Blei D.M.; Ng A.Y.; Jordan M.I., Latent dirichlet allocation, J. Mach. Learn. Res, 3, pp. 993-1022, (2003); Devlin J.; Chang M.W.; Lee K.; Toutanova K., Bert: pre-training of deep bidirectional transformers for language understanding, Proceedings of NAACL-HLT, (2019); Huang J., Understanding college students’ ai literacy in course essays: a topic modeling-based method, Springer Conference Proceedings, (2025); Kasneci E., Chatgpt for good? On opportunities and challenges of large language models for education, Learn. Individ. Differ, 103, (2023); Lachheb A., AI in higher education: a bibliometric analysis,synthesis and topic modeling, Comput. Educ. Artif. Intell, (2025); Liao F., Exploring public perceptions of generative ai in education: topic modeling and sentiment analysis, ACM Conference Proceedings, (2025); Lo N., The impact of generative ai on essay revisions and student writing proficiency, Comput. Educ. Artif. Intell, (2025); Yang C., Charting the developmental landscape of ai in language education: a bibliometric analysis, (2025); Zawacki-Richter O.; Marin V.I.; Bond M.; Gouverneur F., Systematic review of research on artificial intelligence applications in higher education, Int. J. Educ. Technol. High. Educ, 16, 1, (2019)","A. Primo; Huston-Tillotson University, Austin, 78702, United States; email: acprimo@htu.edu","Bandi A.; Hossain M.; Mitra R.","Springer Science and Business Media Deutschland GmbH","International Society for Computers and Their Applications","41st International Conference on Computers and Their Applications, CATA 2026","23 March 2026 through 25 March 2026","Honolulu","354409","18650929","978-303229002-1","","","English","Commun. Comput. Info. Sci.","Conference paper","Final","","Scopus","2-s2.0-105042287495" +"Miyazono S.; Umeno J.; Nagasue T.; Saiki T.; Kaku H.; Torisu T.; Yokote A.; Kawasaki K.; Ihara Y.; Matsuno Y.; Imazu N.; Moriyama T.; Mohamed A.N.; Hirakawa K.; Yamagata H.; Okamoto Y.; Kurahara K.; Yada S.; Harada A.; Ago T.","Miyazono, Satoshi (58990715200); Umeno, Junji (16069709600); Nagasue, Tomohiro (6506751794); Saiki, Takuto (57809363900); Kaku, Hisamitsu (60634161300); Torisu, Takehiro (7004792921); Yokote, Akihito (57207259176); Kawasaki, Keisuke (35201049600); Ihara, Yutaro (57190882152); Matsuno, Yuichi (57190880683); Imazu, Noriyuki (57207243419); Moriyama, Tomohiko (7202578703); Mohamed, Ahmed Nashaat (60634354900); Hirakawa, Katsuya (7202125477); Yamagata, Hajime (60634261700); Okamoto, Yasuharu (58293263100); Kurahara, Koichi (6601984782); Yada, Shinichiro (57207988382); Harada, Akira (57193902190); Ago, Tetsuro (6603990945)","58990715200; 16069709600; 6506751794; 57809363900; 60634161300; 7004792921; 57207259176; 35201049600; 57190882152; 57190880683; 57207243419; 7202578703; 60634354900; 7202125477; 60634261700; 58293263100; 6601984782; 57207988382; 57193902190; 6603990945","Improved Efficiency and Lesion Detection in Small Bowel Capsule Endoscopy Using the Open-Source Artificial Intelligence Model SEE-AI","2027","DEN Open","7","1","e70346","","","1","10.1002/deo2.70346","https://www.scopus.com/pages/publications/105039082973?origin=resultslist","Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Department of Endoscopic Diagnostics and Therapeutics, Kyushu University Hospital, Fukuoka, Japan; International Medical Department, Kyushu University Hospital, Fukuoka, Japan; The National Hepatology and Tropical Medicine Research Institute, Cairo, Egypt; Division of Gastroenterology, Fukuoka Red Cross Hospital, Fukuoka, Japan; Department of Gastroenterology, Hamanomachi Hospital, Fukuoka, Japan; Department of Gastroenterology, Kyushu Central Hospital of the Mutual Aid Association of Public School Teachers, Fukuoka, Japan; Division of Gastroenterology, Matsuyama Red Cross Hospital, Ehime, Japan; Department of Gastroenterology, Onga Nakama Medical Association, Onga Hospital, Fukuoka, Japan; Division of Gastroenterology, Yamaguchi Red Cross Hospital, Yamaguchi, Japan","Miyazono S., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Umeno J., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Nagasue T., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan, Department of Endoscopic Diagnostics and Therapeutics, Kyushu University Hospital, Fukuoka, Japan; Saiki T., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Kaku H., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Torisu T., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Yokote A., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Kawasaki K., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Ihara Y., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Matsuno Y., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Imazu N., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; Moriyama T., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan, International Medical Department, Kyushu University Hospital, Fukuoka, Japan; Mohamed A.N., The National Hepatology and Tropical Medicine Research Institute, Cairo, Egypt; Hirakawa K., Division of Gastroenterology, Fukuoka Red Cross Hospital, Fukuoka, Japan; Yamagata H., Department of Gastroenterology, Hamanomachi Hospital, Fukuoka, Japan; Okamoto Y., Department of Gastroenterology, Kyushu Central Hospital of the Mutual Aid Association of Public School Teachers, Fukuoka, Japan; Kurahara K., Division of Gastroenterology, Matsuyama Red Cross Hospital, Ehime, Japan; Yada S., Department of Gastroenterology, Onga Nakama Medical Association, Onga Hospital, Fukuoka, Japan; Harada A., Division of Gastroenterology, Yamaguchi Red Cross Hospital, Yamaguchi, Japan; Ago T., Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan","Objectives: Small bowel capsule endoscopy (CE) produces lengthy videos that are time-consuming to review and susceptible to missed lesions. We evaluated whether an open-source, pretrained artificial intelligence (AI) model (SEE-AI) could improve diagnostic performance and interpretation efficiency compared with conventional reading. Methods: We retrospectively analyzed 249 PillCam SB3 examinations performed between 2007 and 2022 at six hospitals, using a two-reader crossover design. SEE-AI (confidence threshold 0.1) generated annotated videos with bounding boxes for eight lesion categories. The primary endpoints were sensitivity for lesion detection on a per-lesion and per-patient basis. Secondary endpoints included specificity, predictive values, overall accuracy, and reading time. A prespecified subgroup analysis evaluated cases of suspected small-bowel bleeding (SSBB), focusing on Saurin P1+P2 hemorrhagic lesions. Results: Across 1550 adjudicated lesions, AI-assisted reading demonstrated higher sensitivity than conventional reading (per-lesion: 98.8% [1532/1550] vs. 86.4% [1339/1550]; per-patient: 99.1% [464/468] vs. 80.3% [376/468]; both p < 0.0001). The mean reading time decreased from 17.9 to 13.7 min (p < 0.0001). In SSBB cases (n = 131), sensitivity for P1+P2 lesions improved on both a per-lesion basis (98.2% [439/447] vs. 82.8% [370/447]) and per-patient basis (98.6% [145/147] vs. 73.5% [108/147]), with a shorter reading time (14.1 vs. 18.0 min; all p < 0.0001). Conclusions: In this multicenter evaluation, SEE-AI significantly improved lesion detection and reduced reading time for CE interpretation, including SSBB cases, while maintaining openness and reproducibility. AI-assisted reading may reduce clinicians’ workload and support the adoption of SEE-AI as a practical tool ― and a potential future standard of care ― for small bowel CE. Trial Registration: N/A. © 2026 The Author(s). DEN Open published by John Wiley & Sons Australia, Ltd on behalf of Japan Gastroenterological Endoscopy Society.","artificial intelligence; capsule endoscopy; gastrointestinal tract; small intestine; suspected small-bowel bleeding","","","","","","","","Ahmed M., Video Capsule Endoscopy in Gastroenterology, Gastroenterology Research, 15, 2, pp. 47-55, (2022); PillCam SB 3 System [Internet], (2025); Hosoe N.; Takabayashi K.; Ogata H.; Kanai T., Capsule Endoscopy for Small-Intestinal Disorders: Current Status, Digestive Endoscopy, 31, 5, pp. 498-507, (2019); Liao Z.; Gao R.; Xu C.; Li Z.S., Indications and Detection, Completion, and Retention Rates of Small-Bowel Capsule Endoscopy: A Systematic Review, Gastrointestinal Endoscopy, 71, 2, pp. 280-286, (2010); Medtronic Announces 510(k) Clearance for PillCam Small Bowel 3 @HOME Endoscopy Procedure, (2021); Kim S.H.; Yang D.H.; Kim J.S., Current Status of Interpretation of Small Bowel Capsule Endoscopy, Clinical Endoscopy, 51, 4, pp. 329-333, (2018); Lee H.; Chung J.W.; Yun S.C., Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy, Diagnostics, 14, 23, (2024); Spadaccini M.; Menini M.; Massimi D., AI and Polyp Detection During Colonoscopy, Cancers, 17, 5, (2025); Kim J.E.; Choi Y.H.; Lee Y.C., Deep Learning Model for Distinguishing Mayo Endoscopic Subscore 0 and 1 in Patients With Ulcerative Colitis, Scientific Reports, 13, 1, (2023); PillCam Software v9 — Top 100 Mode; Giordano A.; Escapa M.; Urpi-Ferreruela M., Diagnostic Accuracy of Artificial Intelligence-Aided Capsule Endoscopy (TOP100) in Overt Small Bowel Bleeding, Surgical Endoscopy, 37, 10, pp. 7658-7666, (2023); NaviCam SB Capsule System, (2025); YOLOv5 [Internet], (2020); Yokote A.; Umeno J.; Kawasaki K., Small Bowel Capsule Endoscopy Examination and Open Access Database With Artificial Intelligence: The SEE-Artificial Intelligence Project, DEN Open, 4, (2023); [Untitled Colab notebook] [Internet], (2025); Google Drive [Internet], (2025); Google Colaboratory [Internet], (2025); Saurin J.C.; Delvaux M.; Gaudin J.L., Diagnostic Value of Endoscopic Capsule in Patients With Obscure Digestive Bleeding: Blinded Comparison With Video Push-Enteroscopy, Endoscopy, 35, 7, pp. 576-584, (2003); Ding Z.; Shi H.; Zhang H., Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model, Gastroenterology, 157, 4, pp. 1044-1054.e5, (2019); Xie X.; Xiao Y.F.; Zhao X.Y., Development and Validation of an Artificial Intelligence Model for Small Bowel Capsule Endoscopy Video Review, JAMA Network Open, 5, 7, (2022); Spada C.; Piccirelli S.; Hassan C., AI-Assisted Capsule Endoscopy Reading in Suspected Small Bowel Bleeding: A Multicentre Prospective Study, The Lancet Digital Health, 6, 5, pp. e345-e353, (2024); Akpunonu B.; Hummell J.; Akpunonu J.D.; Ud Din S., Capsule Endoscopy in Gastrointestinal Disease: Evaluation, Diagnosis, and Treatment, Cleveland Clinic Journal of Medicine, 89, 4, pp. 200-211, (2022); Omori T.; Hara T.; Sakasai S., Does the PillCam SB3 Capsule Endoscopy System Improve Image Reading Efficiency Irrespective of Experience? A Pilot Study, Endoscopy International Open, 6, 6, pp. E669-E675, (2018); Aoki T.; Yamada A.; Aoyama K., Clinical Usefulness of a Deep Learning–Based System as the First Screening on Small-Bowel Capsule Endoscopy Reading, Digestive Endoscopy, 32, 4, pp. 585-591, (2020); Van de Bruaene C.; De Looze D.; Hindryckx P., Small Bowel Capsule Endoscopy: Where Are We After Almost 15 Years of Use?, World Journal of Gastrointestinal Endoscopy, 7, 1, pp. 13-36, (2015)","J. Umeno; Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan; email: umeno.junji.199@m.kyushu-u.ac.jp","","Blackwell Publishing Asia","","","","","","26924609","","","","English","DEN. Open.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-105039082973" +"Islam B.U.; Prioli J.P.J.; Azucena J.C.H.","Islam, Baki Ul (57226385206); Prioli, Joao Paulo Jacomini (57221695780); Azucena, Jose Carlos Hernandez (57215546950)","57226385206; 57221695780; 57215546950","Beyond rigid automation: A review of vision-language-action models for adaptive human–robot disassembly","2027","Robotics and Computer-Integrated Manufacturing","103","","103360","","","0","10.1016/j.rcim.2026.103360","https://www.scopus.com/pages/publications/105042078219?origin=resultslist","Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, NC, United States","Islam B.U., Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, NC, United States; Prioli J.P.J., Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, NC, United States; Azucena J.C.H., Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, NC, United States","Traditional industrial robotics relies on rigid programming that is often insufficient for the variability inherent in End-of-Use (EoU) product disassembly, necessitating more adaptive control frameworks. Vision-Language-Action (VLA) models represent a transformative step in this domain by integrating visual perception, natural language understanding, and motor control within a unified architecture. This review systematically examines the convergence of Disassembly Sequence Planning (DSP), VLA architectures, and Human-Robot Collaboration (HRC) to address the challenges of sustainable manufacturing. Based on an analysis of core studies and high-impact preprints from 2019 to January 2026 in these domains, this review evaluates architectural paradigms ranging from end-to-end policies to hierarchical systems. The findings indicate that hierarchical architectures, which decouple high-level symbolic DSP planners from low-level execution policies, offer superior adaptability in unstructured environments. Furthermore, VLA models are identified as key enablers of negotiated autonomy, enabling robots to resolve ambiguities and recover from errors through natural language dialogue with human operators. The review concludes that bridging symbolic planning with embodied action is essential for disassembly systems and proposes a research roadmap focused on data-efficient, transparent, and verifiable human-centric robotic systems. © 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/","Disassembly sequence planning; Embodied AI; Human–robot collaboration; Sustainable manufacturing; Vision-language-action models","Adaptive control systems; Architecture; Computer architecture; Machine vision; Man machine systems; Natural language processing systems; Robot programming; Robot vision; Visual languages; Visual servoing; Action modeling; Adaptive Control; Control framework; Disassembly sequence planning; Embodied AI; Human robots; Human-robot collaboration; Industrial robotics; Sustainable manufacturing; Vision-language-action model; Hierarchical systems","","","","","","","Fernandez-Vega M.; Alfaro-Viquez D.; Zamora-Hernandez M.; Garcia-Rodriguez J.; Azorin-Lopez J., Transforming robots into cobots: a sustainable approach to industrial automation, Electron, 14, 11, (2025); Das A.R.; Koskinopoulou M., Toward sustainable manufacturing: a review on innovations in robotic assembly and disassembly, IEEE Access, 13, pp. 100149-100166, (2025); Munaro M.R.; Tavares S.F., Design for adaptability and disassembly: guidelines for building deconstruction, Constr. 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AI, 12, (2025); Zhang J., FLTRNN: faithful long-horizon task planning for robotics with large language models, 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 6680-6686, (2024); Agia C., Unpacking failure modes of generative policies: runtime monitoring of consistency and progress, Proc. Mach. Learn. Res., 270, pp. 689-723, (2024); Thumm J.; Agia C.; Pavone M.; Althoff M., Text2Interaction: establishing safe and preferable human–robot interaction, Proc. Mach. Learn. Res., 270, pp. 1250-1267, (2024); Kawaharazuka K.; Oh J.; Yamada J.; Posner I.; Zhu Y., Vision-language-action models for robotics: a review towards real-world applications, IEEE Access, 13, pp. 162467-162504, (2025); Seo Y.; Lee K.; James S.; Abbeel P., Reinforcement learning with action-free pre-training from videos, Proc. Mach. Learn. Res., 162, pp. 19561-19579, (2022); Formentini G., A review of disassembly systems for circular product design, J. Clean. Prod., 506, (2025)","J.P.J. Prioli; Department of Industrial and Systems Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, United States; email: jjacominiprioli@ncat.edu","","Elsevier Ltd","","","","","","07365845","","RCIME","","English","Rob Comput Integr Manuf","Review","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-105042078219" diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py new file mode 100644 index 000000000..fafa5ea69 --- /dev/null +++ b/tests/test_pipeline.py @@ -0,0 +1,36 @@ +"""Quick smoke tests for the ETL pipeline — run: python -m tests.test_pipeline""" +import pandas as pd +from shiny import reactive +from www.services.standardizer import standardize, validate +from www.services.metatagextraction import metaTagExtraction + +SAMPLE = "sources/samples/scopus_sample.csv" + +def main(): + raw = pd.read_csv(SAMPLE) + out = standardize(raw, "scopus") + + print("=== 1. Standardize + validate ===") + validate(out) + print("columns:", list(out.columns)) + print("shape:", out.shape, "| any NaN:", out.isna().any().any()) + + print("\n=== 2. SR generation ===") + w = reactive.Value(out.copy()) + with reactive.isolate(): + metaTagExtraction(w, "SR") + r = w.get() + print(r[["AU", "PY", "JI", "SR"]].head(3).to_string()) + + print("\n=== 3. AU_CO country extraction ===") + w = reactive.Value(out.copy()) + with reactive.isolate(): + metaTagExtraction(w, "AU_CO") + r = w.get() + for i in range(min(5, len(r))): + print("C1 first:", r["C1"].iloc[i][:1]) + print("AU_CO :", r["AU_CO"].iloc[i]) + print("---") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/www/services/standardizer.py b/www/services/standardizer.py index 25d72d8d5..bc6c4c793 100644 --- a/www/services/standardizer.py +++ b/www/services/standardizer.py @@ -37,7 +37,7 @@ "Page end": "EP", "Cited by": "TC", "DOI": "DI", - "Affiliations": "C1", + "Authors with affiliations": "C1", "Correspondence Address": "RP", "Abstract": "AB", "Author Keywords": "DE", @@ -64,6 +64,16 @@ "openalex": (OPENALEX_MAP, "OPENALEX", "|"), } +# ---------------------------------------------------------------------- +# Country-name normalization: source spelling -> countries.txt spelling. +# Applied to C1 affiliation strings so AU_CO country matching succeeds. +# ---------------------------------------------------------------------- +COUNTRY_NORMALIZATION = { + "Viet Nam": "Vietnam", + "Russian Federation": "Russia", + "Korea, Republic of": "South Korea", + "Czech Republic": "Czech Republic", +} def standardize(raw_df: pd.DataFrame, source: str) -> pd.DataFrame: """ @@ -116,6 +126,10 @@ def _enforce_types(df: pd.DataFrame, delimiter: str) -> pd.DataFrame: """ for col in LIST_COLUMNS: df[col] = df[col].apply(lambda v: _to_list(v, delimiter)) + + # Normalize country spellings in affiliations (C1) for reliable AU_CO extraction + if "C1" in df.columns: + df["C1"] = df["C1"].apply(_normalize_countries) for col in INT_COLUMNS: df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0).astype(int) @@ -135,6 +149,16 @@ def _to_list(value, delimiter: str) -> list: return [] return [part.strip() for part in str(value).split(delimiter) if part.strip()] +def _normalize_countries(affiliations: list) -> list: + """Canonicalize country-name spelling variants at the end of each affiliation string.""" + out = [] + for aff in affiliations: + for variant, canonical in COUNTRY_NORMALIZATION.items(): + if aff.endswith(variant): + aff = aff[: -len(variant)] + canonical + out.append(aff) + return out + def validate(df: pd.DataFrame) -> pd.DataFrame: """ Validate that a standardized DataFrame conforms to the WoS schema contract. From 9f2fd3842b38bdd6fb9fe4765f49da4f31bb943a Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Wed, 1 Jul 2026 19:24:35 +0200 Subject: [PATCH 04/13] Patch 3: Scopus-aware reference splitting (split on year-boundary, not co-author semicolons) --- tests/test_pipeline.py | 22 +++++++++++++++++++++- www/services/standardizer.py | 29 +++++++++++++++++++++++++---- 2 files changed, 46 insertions(+), 5 deletions(-) diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index fafa5ea69..cf257607c 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -3,9 +3,11 @@ from shiny import reactive from www.services.standardizer import standardize, validate from www.services.metatagextraction import metaTagExtraction +from www.services.biblionetwork import biblionetwork SAMPLE = "sources/samples/scopus_sample.csv" + def main(): raw = pd.read_csv(SAMPLE) out = standardize(raw, "scopus") @@ -27,10 +29,28 @@ def main(): with reactive.isolate(): metaTagExtraction(w, "AU_CO") r = w.get() - for i in range(min(5, len(r))): + for i in range(min(3, len(r))): print("C1 first:", r["C1"].iloc[i][:1]) print("AU_CO :", r["AU_CO"].iloc[i]) print("---") + print("\n=== 4. Keyword co-occurrence (ID) ===") + w = reactive.Value(out.copy()) + with reactive.isolate(): + net = biblionetwork(w, analysis="co-occurrences", network="keywords", n=20, sep=";") + print("keyword co-occ matrix:", type(net).__name__, getattr(net, "shape", "n/a")) + + print("\n=== 5. References (CR) ===") + print("CR count row 0:", len(out["CR"].iloc[0])) + print("first ref:", repr(out["CR"].iloc[0][0])[:200] if out["CR"].iloc[0] else "EMPTY") + w = reactive.Value(out.copy()) + with reactive.isolate(): + metaTagExtraction(w, "CR_AU") + metaTagExtraction(w, "CR_SO") + r = w.get() + print("CR_AU row0:", repr(r["CR_AU"].iloc[0])[:150]) + print("CR_SO row0:", repr(r["CR_SO"].iloc[0])[:150]) + + if __name__ == "__main__": main() \ No newline at end of file diff --git a/www/services/standardizer.py b/www/services/standardizer.py index bc6c4c793..dfaef7994 100644 --- a/www/services/standardizer.py +++ b/www/services/standardizer.py @@ -115,7 +115,7 @@ def _ensure_all_columns(df: pd.DataFrame) -> pd.DataFrame: if col not in df.columns: df[col] = np.nan return df - + def _enforce_types(df: pd.DataFrame, delimiter: str) -> pd.DataFrame: """ @@ -124,22 +124,43 @@ def _enforce_types(df: pd.DataFrame, delimiter: str) -> pd.DataFrame: - int columns -> int, nulls -> 0 - scalar str -> str, nulls -> "" """ - for col in LIST_COLUMNS: - df[col] = df[col].apply(lambda v: _to_list(v, delimiter)) + for col in LIST_COLUMNS: + if col == "CR": + df[col] = df[col].apply(_split_scopus_references) + else: + df[col] = df[col].apply(lambda v: _to_list(v, delimiter)) # Normalize country spellings in affiliations (C1) for reliable AU_CO extraction if "C1" in df.columns: df["C1"] = df["C1"].apply(_normalize_countries) for col in INT_COLUMNS: df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0).astype(int) - + scalar_cols = [c for c in TARGET_SCHEMA if c not in LIST_COLUMNS + INT_COLUMNS] for col in scalar_cols: df[col] = df[col].fillna("").astype(str).replace("nan", "") return df +import re # add at top of file if not already imported + +def _split_scopus_references(value) -> list: + """ + Split a Scopus 'References' field into individual references. + + Scopus separates references with '; ' but ALSO uses '; ' between co-authors + within a single reference. Real references reliably end with '(YEAR)', so we + split only on '; ' that follows a closing parenthesis. + """ + if value is None or (isinstance(value, float) and pd.isna(value)): + return [] + text = str(value).strip() + if not text: + return [] + # Split on "; " only when preceded by ")" -> reference boundary after (year) + parts = re.split(r"(?<=\));\s+", text) + return [p.strip() for p in parts if p.strip()] def _to_list(value, delimiter: str) -> list: """Convert a delimited string / NaN / list into a clean list[str].""" From c5a99076d36e7e3b0e119747bf3c2a9ee73f25cb Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Thu, 2 Jul 2026 01:27:13 +0200 Subject: [PATCH 05/13] Ignore venv directory --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index facfd23e5..5848e8da9 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ __pycache__/ bibliovenv/ Bibenv/ .idea/venv/ +venv/ From 604bbaa32d37233cca802503af6c64c23950738a Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Thu, 2 Jul 2026 02:11:32 +0200 Subject: [PATCH 06/13] Add Dimensions support: mapping dict, XLSX loader (skiprows), Pagination->BP/EP split --- sources/samples/dimensions_sample.xlsx | Bin 0 -> 648752 bytes www/services/standardizer.py | 37 +++++++++++++++++++++++-- 2 files changed, 35 insertions(+), 2 deletions(-) create mode 100644 sources/samples/dimensions_sample.xlsx diff --git a/sources/samples/dimensions_sample.xlsx b/sources/samples/dimensions_sample.xlsx new file mode 100644 index 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"Title": "TI", + "Abstract": "AB", + "Source title": "SO", + "PubYear": "PY", # already numeric + "Volume": "VL", + "Issue": "IS", + "Publication Type": "DT", # "Chapter", "Article", ... + "Authors": "AU", # "Surname, First; Surname, First" + "Authors (Raw Affiliation)": "C1", # per-author "(affil)" -> keeps linkage + "Corresponding Authors": "RP", + "Times cited": "TC", + "MeSH terms": "ID", # controlled keywords -> Index Keywords + # No References column in Dimensions free export -> CR stays [] + # No author keywords column -> DE stays [] + # No ISO abbreviation -> JI stays "" (SR falls back to SO) + # "Pagination" handled separately -> split into BP / EP } - OPENALEX_MAP = { # filled in the Advanced API layer } @@ -92,6 +109,13 @@ def standardize(raw_df: pd.DataFrame, source: str) -> pd.DataFrame: mapping, db_label, delimiter = SOURCE_REGISTRY[source] df = _rename_columns(raw_df, mapping) + + # Source-specific structural transforms (things a rename can't express) + if source == "dimensions" and "Pagination" in raw_df.columns: + pages = raw_df["Pagination"].fillna("").astype(str).str.split("-", n=1, expand=True) + df["BP"] = pages[0].fillna("") + df["EP"] = pages[1].fillna("") if pages.shape[1] > 1 else "" + df = _ensure_all_columns(df) df = _enforce_types(df, delimiter) df["DB"] = db_label @@ -180,6 +204,15 @@ def _normalize_countries(affiliations: list) -> list: out.append(aff) return out +def load_dimensions_xlsx(path: str) -> pd.DataFrame: + """ + Load a Dimensions XLSX export. + + Dimensions places a disclaimer/notice row above the real header row, + so the file must be read with skiprows=1 to get correct column names. + """ + return pd.read_excel(path, skiprows=1) + def validate(df: pd.DataFrame) -> pd.DataFrame: """ Validate that a standardized DataFrame conforms to the WoS schema contract. From 49b3319aa152f6820aba523f2bb515e56daeb238 Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Thu, 2 Jul 2026 10:10:59 +0200 Subject: [PATCH 07/13] Advanced level: OpenAlex API retriever (pagination, backoff, retries) feeding the shared pipeline --- www/services/api_retriever.py | 130 ++++++++++++++++++++++++++++++++++ www/services/standardizer.py | 18 ++++- 2 files changed, 147 insertions(+), 1 deletion(-) create mode 100644 www/services/api_retriever.py diff --git a/www/services/api_retriever.py b/www/services/api_retriever.py new file mode 100644 index 000000000..8c44bd656 --- /dev/null +++ b/www/services/api_retriever.py @@ -0,0 +1,130 @@ +""" +api_retriever.py — Advanced-level Extract layer: live API retrieval. + +Fetches works from the OpenAlex REST API given a textual query, handling +authentication, pagination, rate limits, and retries. Flattens the nested +JSON into a raw DataFrame whose columns match OPENALEX_MAP in standardizer.py, +so the identical transformation pipeline is reused with no duplicated logic. + +The API key is read from the OPENALEX_API_KEY environment variable and is +never stored in code or in the repository. +""" + +import os +import time + +import pandas as pd +import requests + +OPENALEX_BASE = "https://api.openalex.org/works" +MAX_RETRIES = 5 +BACKOFF_BASE_SECONDS = 2 + + +def fetch_openalex(query: str, max_records: int = 200, api_key: str | None = None) -> pd.DataFrame: + """ + Retrieve works matching `query` from OpenAlex and return a flat raw DataFrame. + + Args: + query: Free-text search string (e.g. "machine learning"). + max_records: Maximum number of works to retrieve. + api_key: OpenAlex API key. Falls back to the OPENALEX_API_KEY env var. + + Returns: + A DataFrame with one row per work, columns matching OPENALEX_MAP. + + Raises: + RuntimeError: if no API key is available or retries are exhausted. + """ + key = api_key or os.environ.get("OPENALEX_API_KEY") + if not key: + raise RuntimeError( + "No OpenAlex API key. Set the OPENALEX_API_KEY environment variable " + "or pass api_key=. Free keys: https://openalex.org/settings/api" + ) + + records, cursor = [], "*" + per_page = min(100, max_records) # 100 = max page size, most credit-efficient + + while len(records) < max_records: + params = { + "search": query, + "per-page": per_page, + "cursor": cursor, + "api_key": key, + } + data = _request_with_retries(OPENALEX_BASE, params) + + results = data.get("results", []) + if not results: + break + records.extend(_flatten_work(w) for w in results) + + cursor = data.get("meta", {}).get("next_cursor") + if not cursor: + break + + return pd.DataFrame(records[:max_records]) + + +def _request_with_retries(url: str, params: dict) -> dict: + """GET with exponential backoff on rate-limit (429) and server (5xx) errors.""" + for attempt in range(MAX_RETRIES): + resp = requests.get(url, params=params, timeout=30) + if resp.status_code == 200: + return resp.json() + if resp.status_code in (429, 500, 502, 503): + wait = BACKOFF_BASE_SECONDS * (2 ** attempt) + print(f"Rate-limited/unavailable (HTTP {resp.status_code}); retrying in {wait}s...") + time.sleep(wait) + continue + resp.raise_for_status() + raise RuntimeError(f"OpenAlex request failed after {MAX_RETRIES} retries.") + + +def _flatten_work(w: dict) -> dict: + """Flatten one nested OpenAlex work object into a flat record.""" + authorships = w.get("authorships") or [] + biblio = w.get("biblio") or {} + source = ((w.get("primary_location") or {}).get("source") or {}) + + affiliations = [] + for a in authorships: + name = (a.get("author") or {}).get("display_name") or "" + for aff in a.get("raw_affiliation_strings") or []: + affiliations.append(f"{name}, {aff}" if name else aff) + + return { + "id": w.get("id") or "", + "doi": w.get("doi") or "", + "title": w.get("title") or "", + "publication_year": w.get("publication_year"), + "source_name": source.get("display_name") or "", + "type": w.get("type") or "", + "language": w.get("language") or "", + "cited_by_count": w.get("cited_by_count", 0), + "authors": "; ".join( + (a.get("author") or {}).get("display_name") or "" for a in authorships + ), + "affiliations": "; ".join(affiliations), + "volume": biblio.get("volume") or "", + "issue": biblio.get("issue") or "", + "first_page": biblio.get("first_page") or "", + "last_page": biblio.get("last_page") or "", + "keywords": "; ".join( + k.get("display_name") or "" for k in (w.get("keywords") or []) + ), + "abstract": _rebuild_abstract(w.get("abstract_inverted_index")), + "references": "; ".join(w.get("referenced_works") or []), + } + + +def _rebuild_abstract(inverted: dict | None) -> str: + """Reconstruct abstract text from OpenAlex's inverted index format.""" + if not inverted: + return "" + positions = {} + for word, idxs in inverted.items(): + for i in idxs: + positions[i] = word + return " ".join(positions[i] for i in sorted(positions)) \ No newline at end of file diff --git a/www/services/standardizer.py b/www/services/standardizer.py index 049e9bb35..fd2a83b26 100644 --- a/www/services/standardizer.py +++ b/www/services/standardizer.py @@ -71,7 +71,23 @@ # "Pagination" handled separately -> split into BP / EP } OPENALEX_MAP = { - # filled in the Advanced API layer + "id": "UT", + "doi": "DI", + "title": "TI", + "publication_year":"PY", + "source_name": "SO", + "type": "DT", + "language": "LA", + "cited_by_count": "TC", + "authors": "AU", + "affiliations": "C1", + "volume": "VL", + "issue": "IS", + "first_page": "BP", + "last_page": "EP", + "keywords": "DE", + "abstract": "AB", + "references": "CR", # OpenAlex work-IDs, not formatted refs (limitation) } # Dispatcher: source name -> (mapping dict, DB label, multi-value delimiter) From 038669b44ae51cb7050a42b3da4cecd4b96d8273 Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Thu, 2 Jul 2026 10:18:46 +0200 Subject: [PATCH 08/13] Advanced level: OpenAlex API retriever + mapping; fix author delimiter for correct list splitting --- www/services/standardizer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/www/services/standardizer.py b/www/services/standardizer.py index fd2a83b26..a0f486c2d 100644 --- a/www/services/standardizer.py +++ b/www/services/standardizer.py @@ -94,7 +94,7 @@ SOURCE_REGISTRY = { "scopus": (SCOPUS_MAP, "SCOPUS", ";"), "dimensions": (DIMENSIONS_MAP, "DIMENSIONS", ";"), - "openalex": (OPENALEX_MAP, "OPENALEX", "|"), + "openalex": (OPENALEX_MAP, "OPENALEX", ";"), } # ---------------------------------------------------------------------- From eb070d558ff11cf56b1cb6f573ea906cdaa715b4 Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Thu, 2 Jul 2026 11:26:42 +0200 Subject: [PATCH 09/13] Add run_pipeline entry point, export serialization; dashboard demo working with live OpenAlex data --- sources/samples/openalex_standardized.xlsx | Bin 0 -> 123248 bytes www/services/standardizer.py | 37 ++++++++++++++++++++- 2 files changed, 36 insertions(+), 1 deletion(-) create mode 100644 sources/samples/openalex_standardized.xlsx 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of file + return df + +def export_standardized(df: pd.DataFrame, path: str) -> None: + """ + Serialize a standardized DataFrame to disk (Load phase). + + Multi-value list columns are joined with the standard internal + delimiter ';' so the file is flat-format compatible (xlsx/csv), + matching the serialization convention of native bibliometrix exports. + """ + flat = df.copy() + for col in LIST_COLUMNS: + flat[col] = flat[col].apply(lambda l: ";".join(l) if isinstance(l, list) else l) + if path.endswith(".xlsx"): + flat.to_excel(path, index=False) + else: + flat.to_csv(path, index=False) + +def run_pipeline(raw_df: pd.DataFrame, source: str) -> pd.DataFrame: + """ + Full ETL transform: standardize -> derive SR -> validate. + + This is the single entry point (conceptual equivalent of R's convert2df): + raw source DataFrame in, analysis-ready standardized DataFrame out. + """ + from shiny import reactive + from www.services.metatagextraction import metaTagExtraction + + df = standardize(raw_df, source) + + wrapped = reactive.Value(df) + with reactive.isolate(): + metaTagExtraction(wrapped, "SR") + df = wrapped.get() + + return validate(df) \ No newline at end of file From b93e915f33a44ec19f338c00d83a9ff96f76fdb3 Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Sat, 4 Jul 2026 19:52:28 +0200 Subject: [PATCH 10/13] ETL pipeline: Scopus/Dimensions/OpenAlex, patches, dashboard-tolerant deserialization --- functions/get_frequentwords.py | 10 ++++++++-- functions/get_relevantauthors.py | 13 ++++++++++--- functions/get_wordcloud.py | 11 +++++++++-- sources/samples/dimensions_standardized.xlsx | Bin 0 -> 516493 bytes sources/samples/openalex_standardized.xlsx | Bin 123248 -> 123263 bytes sources/samples/scopus_standardized.xlsx | Bin 0 -> 46793 bytes www/services/metatagextraction.py | 2 ++ www/services/standardizer.py | 3 ++- 8 files changed, 31 insertions(+), 8 deletions(-) create mode 100644 sources/samples/dimensions_standardized.xlsx create mode 100644 sources/samples/scopus_standardized.xlsx diff --git a/functions/get_frequentwords.py b/functions/get_frequentwords.py index 8d790ffe1..7ca0e07d0 100644 --- a/functions/get_frequentwords.py +++ b/functions/get_frequentwords.py @@ -1,5 +1,5 @@ from www.services import * - +import ast def get_frequent_words(df, ngram, num_of_words, word_type, file_upload_terms, file_upload_synonyms, field_separator_frequent=';'): """ @@ -116,7 +116,13 @@ def table_tag(df, tag, ngrams=1, remove_terms=None, synonyms=None): # Handle list columns (DE and ID) if tag in ['DE', 'ID']: - text_data = text_data.dropna().apply(lambda x: ', '.join(eval(x) if isinstance(x, str) else x)) + text_data = text_data.dropna().apply( + lambda x: ', '.join( + x if isinstance(x, list) + else (ast.literal_eval(x) if isinstance(x, str) and x.strip().startswith("[") + else [i.strip() for i in x.split(";")]) + ) + ) # Process words if tag in ['DE', 'ID']: diff --git a/functions/get_relevantauthors.py b/functions/get_relevantauthors.py index cdf960151..3895417e8 100644 --- a/functions/get_relevantauthors.py +++ b/functions/get_relevantauthors.py @@ -1,5 +1,5 @@ from www.services import * - +import ast def get_relevant_authors(df, num_of_authors, frequency="N. of Documents"): """ @@ -19,8 +19,12 @@ def get_relevant_authors(df, num_of_authors, frequency="N. of Documents"): data = data.dropna(subset=["AU"]) # Ensure all values in the "AU" column are lists - data["AU"] = data["AU"].apply(lambda x: x if isinstance(x, list) else []) - + #data["AU"] = data["AU"].apply(lambda x: x if isinstance(x, list) else []) + data["AU"] = data["AU"].apply( + lambda x: x if isinstance(x, list) + else (ast.literal_eval(x) if isinstance(x, str) and x.strip().startswith("[") + else ([i.strip() for i in x.split(";")] if isinstance(x, str) and x.strip() else [])) + ) # Flatten the list of authors and calculate occurrences all_authors = [author for sublist in data["AU"] for author in sublist] author_counts = pd.Series(all_authors).value_counts() @@ -103,7 +107,10 @@ def get_relevant_authors(df, num_of_authors, frequency="N. of Documents"): ) # Set x-axis ticks to 0, 5, 10, etc. + max_x = author_counts[frequency].max() + if pd.isna(max_x): + max_x = 0 tick_step = 5 x_ticks = list(range(0, int(max_x) + tick_step, tick_step)) if x_ticks[-1] < max_x: diff --git a/functions/get_wordcloud.py b/functions/get_wordcloud.py index e902f3bd6..7bcc2f813 100644 --- a/functions/get_wordcloud.py +++ b/functions/get_wordcloud.py @@ -1,5 +1,5 @@ from www.services import * - +import ast def is_legible_on_white(color): """Restituisce True se il colore è leggibile su sfondo bianco""" @@ -122,7 +122,14 @@ def table_tag(df, tag, ngrams=1, remove_terms=None, synonyms=None): # Handle list columns (DE and ID) if tag in ['DE', 'ID']: - text_data = text_data.dropna().apply(lambda x: ', '.join(eval(x) if isinstance(x, str) else x)) + #text_data = text_data.dropna().apply(lambda x: ', '.join(eval(x) if isinstance(x, str) else x)) + text_data = text_data.dropna().apply( + lambda x: ', '.join( + x if isinstance(x, list) + else (ast.literal_eval(x) if isinstance(x, str) and x.strip().startswith("[") + else [i.strip() for i in x.split(";")]) + ) + ) # Process words if tag in ['DE', 'ID']: diff --git a/sources/samples/dimensions_standardized.xlsx b/sources/samples/dimensions_standardized.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..42fa11200302d08de705a0054ac778188e37e5e9 GIT binary patch literal 516493 zcmY&<1Ox?igL9-SJ|-ub`rm5iKSTV_OdU)VogExq7>pbp={@ai<>usI1{q-?_WG3F z+cKl@OW;NMqVl@u=-onFsq8{NZm+=v?7h6kaf;|8iOE|t#O~>j>{yt`-~?AmKKQ35 z3!@5;eCn9C17jMn5lRT~L5GU$*8YSt!E#O?wEaC_rWdzxn@bb+XyjHRA3D%v;Yz0g|BY6eLQ}~*nRNIv1@D9G#8CWS5Lg%vJ|F%&It>UA5Yqn- z!NkGY?0+!KBn`+9F~Wu!fFJL0TP^&lxC9@DCM6{gY!{1c6JxbX!EEvIS!{G8^aP4) z`1Spv?N|`H;6G5g0u^t}pch|zCw0?o;!{chlDb`}R6Plqo3SIvE&7V#fs*d4#uFZS z2jC_#N1@906Vr;WvWv^$DJc=edlqB(zC~=qvS9xe3C}~0PzJIokKAW;hsyy*iDB$~ 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z>n*vs4b-^l#NSJhVx8t_#;JW~1#7J8J*{x4uDNb&D9KH`xSGP?o zU#|B?t}7(wF1?cqF;g084BP|R0n1j-e&xLh+4RD4R-9?8SULk35 z(T1cb{SuS3lNuo&?LP0dy>T4TbzDfE^6vd~TobU025@2v;H)c)9AzGJ>~!bqTfaH% z?{PU;6z&FyfYDsCu}w8oGD*|YI!}_u`@gTk@;V_sNt65U@K)asf{Znfrk{L9us-po zu2BXqdIcN0mgI2*j$(EN^#i_`1O-C}`S+<*d{j;{?Tf?7uZH~9{^u3=#V-hmse_54 zvxB1xlaZt2zi9m@I=S~A?egn;bYEIV`5)+49|$`5ztJWR&Sw7?JWDr+3>gXpB!}w1 z2l9U`1cL5<9scXA?rLdfXZGJ)mjBB@jR$$oLNN%4*D)vv_Wz*2`qm)QZ~tZBUt#ZJ zX=di?!t`I=|7qo)(~18v6bMTHrKSIvT>O)Oe`f7}LqY2QBZvPJ_|Gi(Z{X<{@avfW zTd@BG|5N4v28%cT7x@39`G4B^r;z?_hpy@Wl$D|k None: """ flat = df.copy() for col in LIST_COLUMNS: - flat[col] = flat[col].apply(lambda l: ";".join(l) if isinstance(l, list) else l) + flat[col] = flat[col].apply(lambda l: ";".join(l) if isinstance(l, list) else "") + if path.endswith(".xlsx"): flat.to_excel(path, index=False) else: From ec93d9c0e61823dfaaa40e67846538bec3ffe5aa Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Sat, 4 Jul 2026 20:19:26 +0200 Subject: [PATCH 11/13] Add PubMed support: E-utilities API (esearch+efetch), MEDLINE mapping, DP->PY and DOI cleanup --- www/services/api_retriever.py | 75 ++++++++++++++++++++++++++++++++++- www/services/standardizer.py | 39 +++++++++++++++++- 2 files changed, 111 insertions(+), 3 deletions(-) diff --git a/www/services/api_retriever.py b/www/services/api_retriever.py index 8c44bd656..376ed0aae 100644 --- a/www/services/api_retriever.py +++ b/www/services/api_retriever.py @@ -127,4 +127,77 @@ def _rebuild_abstract(inverted: dict | None) -> str: for word, idxs in inverted.items(): for i in idxs: positions[i] = word - return " ".join(positions[i] for i in sorted(positions)) \ No newline at end of file + return " ".join(positions[i] for i in sorted(positions)) + +import tempfile +import xml.etree.ElementTree as ET + +PUBMED_ESEARCH = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi" +PUBMED_EFETCH = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi" + + +def fetch_pubmed(query: str, max_records: int = 200) -> pd.DataFrame: + """ + Retrieve records from PubMed via NCBI E-utilities and return a raw DataFrame. + + Uses a two-step E-utilities flow: esearch to resolve the query into PubMed IDs, + then efetch to download those records in MEDLINE format. The MEDLINE text is + parsed by the repository's existing parse_pubmed_data() parser, so no parsing + logic is duplicated. The resulting DataFrame's columns are MEDLINE tags matching + PUBMED_MAP in standardizer.py. + + Args: + query: Free-text search string (e.g. "machine learning"). + max_records: Maximum number of records to retrieve. + + Returns: + A DataFrame with one row per record, columns = MEDLINE tags. + """ + from www.services.parsers import parse_pubmed_data + + # Step 1: esearch -> list of PMIDs + search_params = { + "db": "pubmed", + "term": query, + "retmax": max_records, + "retmode": "json", + } + search_data = _request_with_retries(PUBMED_ESEARCH, search_params) + pmids = search_data.get("esearchresult", {}).get("idlist", []) + if not pmids: + return pd.DataFrame() + + # Step 2: efetch -> MEDLINE text for those PMIDs + fetch_params = { + "db": "pubmed", + "id": ",".join(pmids), + "rettype": "medline", + "retmode": "text", + } + medline_text = _request_text_with_retries(PUBMED_EFETCH, fetch_params) + + # Reuse the repo's MEDLINE parser (it reads a file path) + with tempfile.NamedTemporaryFile(mode="w", suffix=".txt", delete=False, + encoding="utf-8") as tmp: + tmp.write(medline_text) + tmp_path = tmp.name + + records = parse_pubmed_data(tmp_path) + return pd.DataFrame(records) + + +def _request_text_with_retries(url: str, params: dict) -> str: + """GET returning raw text (for efetch MEDLINE), with backoff on 429/5xx.""" + for attempt in range(MAX_RETRIES): + try: + resp = requests.get(url, params=params, timeout=30) + except requests.exceptions.ConnectionError: + time.sleep(BACKOFF_BASE_SECONDS * (2 ** attempt)) + continue + if resp.status_code == 200: + return resp.text + if resp.status_code in (429, 500, 502, 503): + time.sleep(BACKOFF_BASE_SECONDS * (2 ** attempt)) + continue + resp.raise_for_status() + raise RuntimeError(f"PubMed request failed after {MAX_RETRIES} retries.") \ No newline at end of file diff --git a/www/services/standardizer.py b/www/services/standardizer.py index 54844f570..fd71faf5c 100644 --- a/www/services/standardizer.py +++ b/www/services/standardizer.py @@ -90,13 +90,31 @@ "references": "CR", # OpenAlex work-IDs, not formatted refs (limitation) } +PUBMED_MAP = { + "PMID": "PMID", + "TI": "TI", + "AB": "AB", + "FAU": "AF", + "AU": "AU", + "TA": "JI", # journal abbreviation -> used by SR + "JT": "SO", + "OT": "DE", # author keywords + "MH": "ID", # MeSH headings + "AD": "C1", + "LA": "LA", + # "DP" -> PY (year extracted in standardize) + # "LID"/"AID" -> DI (doi cleaned in standardize) +} + # Dispatcher: source name -> (mapping dict, DB label, multi-value delimiter) SOURCE_REGISTRY = { "scopus": (SCOPUS_MAP, "SCOPUS", ";"), "dimensions": (DIMENSIONS_MAP, "DIMENSIONS", ";"), "openalex": (OPENALEX_MAP, "OPENALEX", ";"), + "pubmed": (PUBMED_MAP, "PUBMED", ";"), } + # ---------------------------------------------------------------------- # Country-name normalization: source spelling -> countries.txt spelling. # Applied to C1 affiliation strings so AU_CO country matching succeeds. @@ -124,6 +142,11 @@ def standardize(raw_df: pd.DataFrame, source: str) -> pd.DataFrame: mapping, db_label, delimiter = SOURCE_REGISTRY[source] + # PubMed ships a raw "SO" citation field that collides with our JT->SO + # mapping; drop it before renaming so JT->SO is the only source for SO. + if source == "pubmed" and "SO" in raw_df.columns: + raw_df = raw_df.drop(columns=["SO"]) + df = _rename_columns(raw_df, mapping) # Source-specific structural transforms (things a rename can't express) @@ -132,11 +155,23 @@ def standardize(raw_df: pd.DataFrame, source: str) -> pd.DataFrame: df["BP"] = pages[0].fillna("") df["EP"] = pages[1].fillna("") if pages.shape[1] > 1 else "" + if source == "pubmed": + # DP "2026 Jul 3" -> PY (extract the 4-digit year) + if "DP" in raw_df.columns: + df["PY"] = raw_df["DP"].fillna("").astype(str).str.extract(r"(\d{4})")[0] + # LID/AID "10.1002/ijgo.71182 [doi]" -> bare DOI + doi_src = raw_df["LID"] if "LID" in raw_df.columns else raw_df.get("AID") + if doi_src is not None: + df["DI"] = ( + doi_src.fillna("").astype(str) + .str.replace(r"\s*\[doi\]", "", regex=True) + .str.split(";").str[0] + ) + df = _ensure_all_columns(df) df = _enforce_types(df, delimiter) df["DB"] = db_label # Keep ONLY the standardized WoS schema; drop unmapped source columns - # (this is what removes the leftover NaN-bearing Scopus extras) df = df[TARGET_SCHEMA] return df @@ -298,4 +333,4 @@ def run_pipeline(raw_df: pd.DataFrame, source: str) -> pd.DataFrame: metaTagExtraction(wrapped, "SR") df = wrapped.get() - return validate(df) \ No newline at end of file + return validate(df) From a5db8978e367989067caac84cb69aa5d2128f604 Mon Sep 17 00:00:00 2001 From: anibihakeem Date: Sat, 4 Jul 2026 20:25:50 +0200 Subject: [PATCH 12/13] Add PubMed file-based path (MEDLINE .txt via existing parser); PubMed now supported on both file and API --- sources/samples/pubmed_sample.txt | 814 ++++++++++++++++++++++++++++++ 1 file changed, 814 insertions(+) create mode 100644 sources/samples/pubmed_sample.txt diff --git a/sources/samples/pubmed_sample.txt b/sources/samples/pubmed_sample.txt new file mode 100644 index 000000000..43e562902 --- /dev/null +++ b/sources/samples/pubmed_sample.txt @@ -0,0 +1,814 @@ +PMID- 30102808 +OWN - NLM +STAT- MEDLINE +DCOM- 20190923 +LR - 20190923 +IS - 1365-2796 (Electronic) +IS - 0954-6820 (Linking) +VI - 284 +IP - 6 +DP - 2018 Dec +TI - eDoctor: machine learning and the future of medicine. +PG - 603-619 +LID - 10.1111/joim.12822 [doi] +AB - Machine learning (ML) is a burgeoning field of medicine with huge resources being + applied to fuse computer science and statistics to medical problems. Proponents + of ML extol its ability to deal with large, complex and disparate data, often + found within medicine and feel that ML is the future for biomedical research, + personalized medicine, computer-aided diagnosis to significantly advance global + health care. However, the concepts of ML are unfamiliar to many medical + professionals and there is untapped potential in the use of ML as a research + tool. In this article, we provide an overview of the theory behind ML, explore + the common ML algorithms used in medicine including their pitfalls and discuss + the potential future of ML in medicine. +CI - © 2018 The Association for the Publication of the Journal of Internal Medicine. +FAU - Handelman, G S +AU - Handelman GS +AUID- ORCID: 0000-0003-4275-783X +AD - Royal Victoria Hospital, Belfast, UK. +FAU - Kok, H K +AU - Kok HK +AD - Interventional Radiology Service, Northern Hospital Radiology, Epping, Vic, + Australia. +FAU - Chandra, R V +AU - Chandra RV +AD - Interventional Neuroradiology Service, Monash Imaging, Monash Health, Clayton, + Vic, Australia. +AD - Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, + Vic, Australia. +FAU - Razavi, A H +AU - Razavi AH +AD - School of Information Technology and Engineering, University of Ottawa, Ottawa, + ON, Canada. +AD - BCE Corporate Security, Ottawa, ON, Canada. +FAU - Lee, M J +AU - Lee MJ +AD - Department of Radiology, Beaumont Hospital and Royal College of Surgeons in + Ireland, Dublin, Ireland. +FAU - Asadi, H +AU - Asadi H +AD - Interventional Neuroradiology Service, Monash Imaging, Monash Health, Clayton, + Vic, Australia. +AD - Department of Radiology, Interventional Neuroradiology Service, Austin Health, + Heidelberg, Vic, Australia. +AD - School of Medicine, Faculty of Health, Deakin University, Waurn Ponds, Vic, + Australia. +LA - eng +PT - Journal Article +PT - Review +DEP - 20180903 +PL - England +TA - J Intern Med +JT - Journal of internal medicine +JID - 8904841 +SB - IM +MH - Algorithms +MH - Decision Support Systems, Clinical +MH - Forecasting +MH - Humans +MH - *Machine Learning +MH - Medicine/*trends +MH - Precision Medicine +MH - Supervised Machine Learning +MH - Unsupervised Machine Learning +OTO - NOTNLM +OT - artificial intelligence +OT - machine learning +OT - medicine +OT - supervised machine learning +OT - unsupervised machine learning +EDAT- 2018/08/14 06:00 +MHDA- 2019/09/24 06:00 +CRDT- 2018/08/14 06:00 +PHST- 2018/08/14 06:00 [pubmed] +PHST- 2019/09/24 06:00 [medline] +PHST- 2018/08/14 06:00 [entrez] +AID - 10.1111/joim.12822 [doi] +PST - ppublish +SO - J Intern Med. 2018 Dec;284(6):603-619. doi: 10.1111/joim.12822. Epub 2018 Sep 3. + +PMID- 34338485 +OWN - NLM +STAT- MEDLINE +DCOM- 20220310 +LR - 20220311 +IS - 2724-5772 (Electronic) +IS - 2724-5683 (Linking) +VI - 70 +IP - 1 +DP - 2022 Feb +TI - Machine learning for cardiology. +PG - 75-91 +LID - 10.23736/S2724-5683.21.05709-4 [doi] +AB - This paper reviews recent cardiology literature and reports how artificial + intelligence tools (specifically, machine learning techniques) are being used by + physicians in the field. Each technique is introduced with enough details to + allow the understanding of how it works and its intent, but without delving into + details that do not add immediate benefits and require expertise in the field. We + specifically focus on the principal Machine learning based risk scores used in + cardiovascular research. After introducing them and summarizing their assumptions + and biases, we discuss their merits and shortcomings. We report on how frequently + they are adopted in the field and suggest why this is the case based on our + expertise in machine learning. We complete the analysis by reviewing how + corresponding statistical approaches compare with them. Finally, we discuss the + main open issues in applying machine learning tools to cardiology tasks, also + drafting possible future directions. Despite the growing interest in these tools, + we argue that there are many still underutilized techniques: while neural + networks are slowly being incorporated in cardiovascular research, other + important techniques such as semi-supervised learning and federated learning are + still underutilized. The former would allow practitioners to harness the + information contained in large datasets that are only partially labeled, while + the latter would foster collaboration between institutions allowing building + larger and better models. +FAU - Arfat, Yasir +AU - Arfat Y +AD - Department of Computer Science, University of Turin, Turin, Italy - + yasir.arfat@unito.it. +FAU - Mittone, Gianluca +AU - Mittone G +AD - Department of Computer Science, University of Turin, Turin, Italy. +FAU - Esposito, Roberto +AU - Esposito R +AD - Department of Computer Science, University of Turin, Turin, Italy. +FAU - Cantalupo, Barbara +AU - Cantalupo B +AD - Department of Computer Science, University of Turin, Turin, Italy. +FAU - DE Ferrari, Gaetano M +AU - DE Ferrari GM +AD - Division of Cardiology, Department of Cardiovascular and Thoracic, Molinette + Hospital, Città della Salute e della Scienza, Turin, Italy. +AD - Unit of Cardiology, Department of Medical Sciences, University of Turin, Turin, + Italy. +FAU - Aldinucci, Marco +AU - Aldinucci M +AD - Department of Computer Science, University of Turin, Turin, Italy. +LA - eng +PT - Journal Article +PT - Review +DEP - 20210802 +PL - Italy +TA - Minerva Cardiol Angiol +JT - Minerva cardiology and angiology +JID - 101776555 +SB - IM +MH - *Artificial Intelligence +MH - *Cardiology/methods +MH - Machine Learning +MH - Neural Networks, Computer +MH - Supervised Machine Learning +EDAT- 2021/08/03 06:00 +MHDA- 2022/03/11 06:00 +CRDT- 2021/08/02 10:22 +PHST- 2021/08/03 06:00 [pubmed] +PHST- 2022/03/11 06:00 [medline] +PHST- 2021/08/02 10:22 [entrez] +AID - S2724-5683.21.05709-4 [pii] +AID - 10.23736/S2724-5683.21.05709-4 [doi] +PST - ppublish +SO - Minerva Cardiol Angiol. 2022 Feb;70(1):75-91. doi: + 10.23736/S2724-5683.21.05709-4. Epub 2021 Aug 2. + +PMID- 34560276 +OWN - NLM +STAT- MEDLINE +DCOM- 20220315 +LR - 20220315 +IS - 1878-5832 (Electronic) +IS - 1359-6446 (Linking) +VI - 27 +IP - 1 +DP - 2022 Jan +TI - Machine-learning methods for ligand-protein molecular docking. +PG - 151-164 +LID - S1359-6446(21)00397-4 [pii] +LID - 10.1016/j.drudis.2021.09.007 [doi] +AB - Artificial intelligence (AI) is often presented as a new Industrial Revolution. + Many domains use AI, including molecular simulation for drug discovery. In this + review, we provide an overview of ligand-protein molecular docking and how + machine learning (ML), especially deep learning (DL), a subset of ML, is + transforming the field by tackling the associated challenges. +CI - Copyright © 2021 Elsevier Ltd. All rights reserved. +FAU - Crampon, Kevin +AU - Crampon K +AD - Université de Reims Champagne Ardenne, CNRS, MEDyC UMR 7369, 51097 Reims, France; + Université de Reims Champagne Ardenne, LICIIS - LRC CEA DIGIT, 51100 Reims, + France; Atos SE, Center of Excellence in Advanced Computing, 38130 Echirolles, + France. +FAU - Giorkallos, Alexis +AU - Giorkallos A +AD - Atos SE, Center of Excellence in Advanced Computing, 38130 Echirolles, France. +FAU - Deldossi, Myrtille +AU - Deldossi M +AD - Atos SE, Center of Excellence in Advanced Computing, 38130 Echirolles, France. +FAU - Baud, Stéphanie +AU - Baud S +AD - Université de Reims Champagne Ardenne, CNRS, MEDyC UMR 7369, 51097 Reims, France. +FAU - Steffenel, Luiz Angelo +AU - Steffenel LA +AD - Université de Reims Champagne Ardenne, LICIIS - LRC CEA DIGIT, 51100 Reims, + France. Electronic address: angelo.steffenel@univ-reims.fr. +LA - eng +PT - Journal Article +PT - Research Support, Non-U.S. Gov't +PT - Review +DEP - 20210921 +PL - England +TA - Drug Discov Today +JT - Drug discovery today +JID - 9604391 +RN - 0 (Ligands) +SB - IM +MH - *Artificial Intelligence +MH - Deep Learning +MH - *Drug Discovery/methods/trends +MH - Humans +MH - *Ligands +MH - Machine Learning +MH - Molecular Docking Simulation/*methods +OTO - NOTNLM +OT - Data representation +OT - Deep learning +OT - Machine learning +OT - Molecular docking +OT - Sampling +OT - Scoring +EDAT- 2021/09/25 06:00 +MHDA- 2022/03/16 06:00 +CRDT- 2021/09/24 20:16 +PHST- 2021/04/01 00:00 [received] +PHST- 2021/07/14 00:00 [revised] +PHST- 2021/09/15 00:00 [accepted] +PHST- 2021/09/25 06:00 [pubmed] +PHST- 2022/03/16 06:00 [medline] +PHST- 2021/09/24 20:16 [entrez] +AID - S1359-6446(21)00397-4 [pii] +AID - 10.1016/j.drudis.2021.09.007 [doi] +PST - ppublish +SO - Drug Discov Today. 2022 Jan;27(1):151-164. doi: 10.1016/j.drudis.2021.09.007. + Epub 2021 Sep 21. + +PMID- 32800297 +OWN - NLM +STAT- MEDLINE +DCOM- 20210204 +LR - 20240329 +IS - 1878-1888 (Electronic) +IS - 0005-7894 (Print) +IS - 0005-7894 (Linking) +VI - 51 +IP - 5 +DP - 2020 Sep +TI - Supervised Machine Learning: A Brief Primer. +PG - 675-687 +LID - S0005-7894(20)30067-8 [pii] +LID - 10.1016/j.beth.2020.05.002 [doi] +AB - Machine learning is increasingly used in mental health research and has the + potential to advance our understanding of how to characterize, predict, and treat + mental disorders and associated adverse health outcomes (e.g., suicidal + behavior). Machine learning offers new tools to overcome challenges for which + traditional statistical methods are not well-suited. This paper provides an + overview of machine learning with a specific focus on supervised learning (i.e., + methods that are designed to predict or classify an outcome of interest). Several + common supervised learning methods are described, along with applied examples + from the published literature. We also provide an overview of supervised learning + model building, validation, and performance evaluation. Finally, challenges in + creating robust and generalizable machine learning algorithms are discussed. +CI - Copyright © 2020. Published by Elsevier Ltd. +FAU - Jiang, Tammy +AU - Jiang T +AD - Boston University School of Public Health. +FAU - Gradus, Jaimie L +AU - Gradus JL +AD - Boston University School of Public Health; Boston University School of Medicine. +FAU - Rosellini, Anthony J +AU - Rosellini AJ +AD - Center for Anxiety and Related Disorders, Boston University; Department of + Psychological and Brain Sciences, Boston University. Electronic address: + ajrosell@bu.edu. +LA - eng +GR - K01 MH106710/MH/NIMH NIH HHS/United States +GR - R01 MH109507/MH/NIMH NIH HHS/United States +GR - R01 MH110453/MH/NIMH NIH HHS/United States +GR - R21 MH119492/MH/NIMH NIH HHS/United States +PT - Journal Article +PT - Research Support, N.I.H., Extramural +PT - Review +DEP - 20200516 +PL - England +TA - Behav Ther +JT - Behavior therapy +JID - 1251640 +SB - IM +MH - Algorithms +MH - Humans +MH - *Machine Learning +MH - *Supervised Machine Learning +PMC - PMC7431677 +MID - NIHMS1595274 +OTO - NOTNLM +OT - ensemble methods +OT - machine learning +OT - supervised learning +COIS- Declaration of interests. None. +EDAT- 2020/08/18 06:00 +MHDA- 2021/02/05 06:00 +PMCR- 2021/09/01 +CRDT- 2020/08/18 06:00 +PHST- 2020/03/02 00:00 [received] +PHST- 2020/05/13 00:00 [revised] +PHST- 2020/05/13 00:00 [accepted] +PHST- 2020/08/18 06:00 [entrez] +PHST- 2020/08/18 06:00 [pubmed] +PHST- 2021/02/05 06:00 [medline] +PHST- 2021/09/01 00:00 [pmc-release] +AID - S0005-7894(20)30067-8 [pii] +AID - 10.1016/j.beth.2020.05.002 [doi] +PST - ppublish +SO - Behav Ther. 2020 Sep;51(5):675-687. doi: 10.1016/j.beth.2020.05.002. Epub 2020 + May 16. + +PMID- 38380541 +OWN - NLM +STAT- MEDLINE +DCOM- 20240322 +LR - 20240322 +IS - 1524-4563 (Electronic) +IS - 0194-911X (Linking) +VI - 81 +IP - 4 +DP - 2024 Apr +TI - AI, Machine Learning, and ChatGPT in Hypertension. +PG - 709-716 +LID - 10.1161/HYPERTENSIONAHA.124.19468 [doi] +AB - Hypertension, a leading cause of cardiovascular disease and premature death, + remains incompletely understood despite extensive research. Indeed, even though + numerous drugs are available, achieving adequate blood pressure control remains a + challenge, prompting recent interest in artificial intelligence. To promote the + use of machine learning in cardiovascular medicine, this review provides a brief + introduction to machine learning and reviews its notable applications in + hypertension management and research, such as disease diagnosis and prognosis, + treatment decisions, and omics data analysis. The challenges and limitations + associated with data-driven predictive techniques are also discussed. The goal of + this review is to raise awareness and encourage the hypertension research + community to consider machine learning as a key component in developing + innovative diagnostic and therapeutic tools for hypertension. By integrating + traditional cardiovascular risk factors with genomics, socioeconomic, behavioral, + and environmental factors, machine learning may aid in the development of precise + risk prediction models and personalized treatment approaches for patients with + hypertension. +FAU - Layton, Anita T +AU - Layton AT +AUID- ORCID: 0000-0002-1753-4063 +AD - Department of Applied Mathematics, Department of Biology, Cheriton School of + Computer Science, and School of Pharmacology, University of Waterloo, Ontario, + Canada. +LA - eng +PT - Journal Article +PT - Review +DEP - 20240221 +PL - United States +TA - Hypertension +JT - Hypertension (Dallas, Tex. : 1979) +JID - 7906255 +SB - IM +MH - Humans +MH - Artificial Intelligence +MH - Machine Learning +MH - *Hypertension/diagnosis/drug therapy +MH - *Cardiovascular Diseases +MH - Genomics +OTO - NOTNLM +OT - artificial intelligence +OT - cardiovascular diseases +OT - hypertension +OT - machine learning +COIS- None. +EDAT- 2024/02/21 11:15 +MHDA- 2024/03/22 06:44 +CRDT- 2024/02/21 05:30 +PHST- 2024/03/22 06:44 [medline] +PHST- 2024/02/21 11:15 [pubmed] +PHST- 2024/02/21 05:30 [entrez] +AID - 10.1161/HYPERTENSIONAHA.124.19468 [doi] +PST - ppublish +SO - Hypertension. 2024 Apr;81(4):709-716. doi: 10.1161/HYPERTENSIONAHA.124.19468. + Epub 2024 Feb 21. + +PMID- 34617632 +OWN - NLM +STAT- MEDLINE +DCOM- 20220331 +LR - 20220401 +IS - 1521-4095 (Electronic) +IS - 0935-9648 (Linking) +VI - 34 +IP - 1 +DP - 2022 Jan +TI - Machine Learning-Driven Biomaterials Evolution. +PG - e2102703 +LID - 10.1002/adma.202102703 [doi] +AB - Biomaterials is an exciting and dynamic field, which uses a collection of diverse + materials to achieve desired biological responses. While there is constant + evolution and innovation in materials with time, biomaterials research has been + hampered by the relatively long development period required. In recent years, + driven by the need to accelerate materials development, the applications of + machine learning in materials science has progressed in leaps and bounds. The + combination of machine learning with high-throughput theoretical predictions and + high-throughput experiments (HTE) has shifted the traditional Edisonian (trial + and error) paradigm to a data-driven paradigm. In this review, each type of + biomaterial and their key properties and use cases are systematically discussed, + followed by how machine learning can be applied in the development and design + process. The discussions are classified according to various types of materials + used including polymers, metals, ceramics, and nanomaterials, and implants using + additive manufacturing. Last, the current gaps and potential of machine learning + to further aid biomaterials discovery and application are also discussed. +CI - © 2021 Wiley-VCH GmbH. +FAU - Suwardi, Ady +AU - Suwardi A +AD - Institute of Materials Research and Engineering, A*STAR (Agency for Science, + Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, + Singapore. +FAU - Wang, FuKe +AU - Wang F +AD - Institute of Materials Research and Engineering, A*STAR (Agency for Science, + Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, + Singapore. +FAU - Xue, Kun +AU - Xue K +AD - Institute of Materials Research and Engineering, A*STAR (Agency for Science, + Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, + Singapore. +FAU - Han, Ming-Yong +AU - Han MY +AD - Institute of Materials Research and Engineering, A*STAR (Agency for Science, + Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, + Singapore. +FAU - Teo, Peili +AU - Teo P +AD - Institute of Materials Research and Engineering, A*STAR (Agency for Science, + Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, + Singapore. +FAU - Wang, Pei +AU - Wang P +AD - Institute of Materials Research and Engineering, A*STAR (Agency for Science, + Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, + Singapore. +FAU - Wang, Shijie +AU - Wang S +AD - Institute of Materials Research and Engineering, A*STAR (Agency for Science, + Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, + Singapore. +FAU - Liu, Ye +AU - Liu Y +AD - Institute of Materials Research and Engineering, A*STAR (Agency for Science, + Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, + Singapore. +FAU - Ye, Enyi +AU - Ye E +AD - Institute of Materials Research and Engineering, A*STAR (Agency for Science, + Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, + Singapore. +FAU - Li, Zibiao +AU - Li Z +AD - Institute of Materials Research and Engineering, A*STAR (Agency for Science, + Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, + Singapore. +FAU - Loh, Xian Jun +AU - Loh XJ +AUID- ORCID: 0000-0001-8118-6502 +AD - Institute of Materials Research and Engineering, A*STAR (Agency for Science, + Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, + Singapore. +LA - eng +GR - Agency for Science, Technology and Research/ +PT - Journal Article +PT - Review +DEP - 20211007 +PL - Germany +TA - Adv Mater +JT - Advanced materials (Deerfield Beach, Fla.) +JID - 9885358 +RN - 0 (Biocompatible Materials) +RN - 0 (Polymers) +SB - IM +MH - *Biocompatible Materials +MH - *Machine Learning +MH - Materials Science +MH - Polymers +MH - Prostheses and Implants +OTO - NOTNLM +OT - artificial intelligence +OT - biomaterials +OT - machine learning +EDAT- 2021/10/08 06:00 +MHDA- 2022/04/01 06:00 +CRDT- 2021/10/07 08:48 +PHST- 2021/07/09 00:00 [revised] +PHST- 2021/04/09 00:00 [received] +PHST- 2021/10/08 06:00 [pubmed] +PHST- 2022/04/01 06:00 [medline] +PHST- 2021/10/07 08:48 [entrez] +AID - 10.1002/adma.202102703 [doi] +PST - ppublish +SO - Adv Mater. 2022 Jan;34(1):e2102703. doi: 10.1002/adma.202102703. Epub 2021 Oct 7. + +PMID- 35718406 +OWN - NLM +STAT- MEDLINE +DCOM- 20220621 +LR - 20220621 +IS - 1558-1349 (Electronic) +IS - 1042-3680 (Linking) +VI - 33 +IP - 3 +DP - 2022 Jul +TI - Machine Learning and Pain Outcomes. +PG - 351-358 +LID - S1042-3680(22)00014-6 [pii] +LID - 10.1016/j.nec.2022.02.012 [doi] +AB - Machine learning (ML) is an increasingly popular method of data analysis that has + meaningful application within the realm of pain management. Current research has + used this technique as a tool to refine patient selection for more invasive pain + management treatments in an effort to improve outcomes. It is also being used to + aid in the search for biomarkers that could objectify the quantification of pain + to better assess these outcomes. This article provides an overview of ML and its + applications within the pain field. +CI - Copyright © 2022 Elsevier Inc. All rights reserved. +FAU - Harland, Tessa +AU - Harland T +AD - Department of Neurosurgery, Albany Medical College, 47 New Scotland Ave, + Physicians Pavilion, 1st Floor, Albany, NY, 12208, USA. +FAU - Hadanny, Amir +AU - Hadanny A +AD - Department of Neurosurgery, Albany Medical College, 47 New Scotland Ave, + Physicians Pavilion, 1st Floor, Albany, NY, 12208, USA. +FAU - Pilitsis, Julie G +AU - Pilitsis JG +AD - Department of Neurosurgery, Albany Medical College, 47 New Scotland Ave, + Physicians Pavilion, 1st Floor, Albany, NY, 12208, USA; Department of + Neuroscience & Experimental Therapeutics, Albany Medical College, 47 New Scotland + Ave, Albany, NY 12208, USA. Electronic address: jpilitsis@yahoo.com. +LA - eng +PT - Journal Article +PT - Review +DEP - 20220525 +PL - United States +TA - Neurosurg Clin N Am +JT - Neurosurgery clinics of North America +JID - 9008004 +RN - 0 (Biomarkers) +SB - IM +MH - Biomarkers +MH - Humans +MH - *Machine Learning +MH - *Pain +MH - Pain Management +OTO - NOTNLM +OT - Biomarker +OT - Machine learning +OT - Pain management +OT - Pain outcomes +OT - Patient selection +OT - Prediction +EDAT- 2022/06/20 06:00 +MHDA- 2022/06/22 06:00 +CRDT- 2022/06/19 21:05 +PHST- 2022/06/19 21:05 [entrez] +PHST- 2022/06/20 06:00 [pubmed] +PHST- 2022/06/22 06:00 [medline] +AID - S1042-3680(22)00014-6 [pii] +AID - 10.1016/j.nec.2022.02.012 [doi] +PST - ppublish +SO - Neurosurg Clin N Am. 2022 Jul;33(3):351-358. doi: 10.1016/j.nec.2022.02.012. Epub + 2022 May 25. + +PMID- 32589980 +OWN - NLM +STAT- MEDLINE +DCOM- 20200702 +LR - 20200702 +IS - 2352-3026 (Electronic) +IS - 2352-3026 (Linking) +VI - 7 +IP - 7 +DP - 2020 Jul +TI - Machine learning in haematological malignancies. +PG - e541-e550 +LID - S2352-3026(20)30121-6 [pii] +LID - 10.1016/S2352-3026(20)30121-6 [doi] +AB - Machine learning is a branch of computer science and statistics that generates + predictive or descriptive models by learning from training data rather than by + being rigidly programmed. It has attracted substantial attention for its many + applications in medicine, both as a catalyst for research and as a means of + improving clinical care across the cycle of diagnosis, prognosis, and treatment + of disease. These applications include the management of haematological + malignancy, in which machine learning has created inroads in pathology, + radiology, genomics, and the analysis of electronic health record data. As + computational power becomes cheaper and the tools for implementing machine + learning become increasingly democratised, it is likely to become increasingly + integrated into the research and practice landscape of haematology. As such, + machine learning merits understanding and attention from researchers and + clinicians alike. This narrative Review describes important concepts in machine + learning for unfamiliar readers, details machine learning's current applications + in haematological malignancy, and summarises important concepts for clinicians to + be aware of when appraising research that uses machine learning. +CI - Copyright © 2020 Elsevier Ltd. All rights reserved. +FAU - Radakovich, Nathan +AU - Radakovich N +AD - Cleveland Clinic Lerner College of Medicine, Cleveland Clinic, Case Western + Reserve University, Cleveland OH, USA. +FAU - Nagy, Matthew +AU - Nagy M +AD - Cleveland Clinic Lerner College of Medicine, Cleveland Clinic, Case Western + Reserve University, Cleveland OH, USA. +FAU - Nazha, Aziz +AU - Nazha A +AD - Center for Clinical Artificial Intelligence, Cleveland Clinic, Case Western + Reserve University, Cleveland OH, USA; Department of Hematology and Medical + Oncology, Cleveland Clinic, Case Western Reserve University, Cleveland OH, USA. + Electronic address: nazhaa@ccf.org. +LA - eng +PT - Journal Article +PT - Review +PL - England +TA - Lancet Haematol +JT - The Lancet. Haematology +JID - 101643584 +SB - IM +MH - Algorithms +MH - *Hematologic Neoplasms +MH - Humans +MH - *Machine Learning +MH - Neural Networks, Computer +EDAT- 2020/06/27 06:00 +MHDA- 2020/07/03 06:00 +CRDT- 2020/06/27 06:00 +PHST- 2020/01/17 00:00 [received] +PHST- 2020/04/02 00:00 [revised] +PHST- 2020/04/14 00:00 [accepted] +PHST- 2020/06/27 06:00 [pubmed] +PHST- 2020/07/03 06:00 [medline] +PHST- 2020/06/27 06:00 [entrez] +AID - S2352-3026(20)30121-6 [pii] +AID - 10.1016/S2352-3026(20)30121-6 [doi] +PST - ppublish +SO - Lancet Haematol. 2020 Jul;7(7):e541-e550. doi: 10.1016/S2352-3026(20)30121-6. + +PMID- 37257440 +OWN - NLM +STAT- MEDLINE +DCOM- 20230612 +LR - 20230612 +IS - 1751-553X (Electronic) +IS - 1751-5521 (Linking) +VI - 45 Suppl 2 +DP - 2023 Jun +TI - Applied machine learning in hematopathology. +PG - 87-94 +LID - 10.1111/ijlh.14110 [doi] +AB - An increasing number of machine learning applications are being developed and + applied to digital pathology, including hematopathology. The goal of these modern + computerized tools is often to support diagnostic workflows by extracting and + summarizing information from multiple data sources, including digital images of + human tissue. Hematopathology is inherently multimodal and can serve as an ideal + case study for machine learning applications. However, hematopathology also poses + unique challenges compared to other pathology subspecialities when applying + machine learning approaches. By modeling the pathologist workflow and thinking + process, machine learning algorithms may be designed to address practical and + tangible problems in hematopathology. In this article, we discuss the current + trends in machine learning in hematopathology. We review currently available + machine learning enabled medical devices supporting hematopathology workflows. We + then explore current machine learning research trends of the field with a focus + on bone marrow cytology and histopathology, and how adoption of new machine + learning tools may be enabled through the transition to digital pathology. +CI - © 2023 The Authors. International Journal of Laboratory Hematology published by + John Wiley & Sons Ltd. +FAU - Dehkharghanian, Taher +AU - Dehkharghanian T +AD - Department of Nephrology, University Health Network, Toronto, Ontario, Canada. +AD - Department of Pathology and Molecular Medicine, McMaster University, Hamilton, + Ontario, Canada. +FAU - Mu, Youqing +AU - Mu Y +AD - Department of Pathology and Molecular Medicine, McMaster University, Hamilton, + Ontario, Canada. +FAU - Tizhoosh, Hamid R +AU - Tizhoosh HR +AUID- ORCID: 0000-0001-5488-601X +AD - Rhazes Lab, Artificial Intelligence and Informatics, Mayo Clinic, Rochester, + Minnesota, USA. +FAU - Campbell, Clinton J V +AU - Campbell CJV +AUID- ORCID: 0000-0002-8896-1134 +AD - Department of Pathology and Molecular Medicine, McMaster University, Hamilton, + Ontario, Canada. +AD - William Osler Health System, Brampton, Ontario, Canada. +LA - eng +PT - Journal Article +PT - Review +DEP - 20230531 +PL - England +TA - Int J Lab Hematol +JT - International journal of laboratory hematology +JID - 101300213 +SB - IM +MH - Humans +MH - *Machine Learning +MH - *Algorithms +MH - Pathologists +MH - Workflow +OTO - NOTNLM +OT - artificial intelligence +OT - digital pathology +OT - hematopathology +OT - machine learning +OT - whole slide imaging +EDAT- 2023/06/01 01:08 +MHDA- 2023/06/12 06:42 +CRDT- 2023/05/31 18:33 +PHST- 2023/03/09 00:00 [received] +PHST- 2023/05/12 00:00 [accepted] +PHST- 2023/06/12 06:42 [medline] +PHST- 2023/06/01 01:08 [pubmed] +PHST- 2023/05/31 18:33 [entrez] +AID - 10.1111/ijlh.14110 [doi] +PST - ppublish +SO - Int J Lab Hematol. 2023 Jun;45 Suppl 2:87-94. doi: 10.1111/ijlh.14110. Epub 2023 + May 31. + +PMID- 35911778 +OWN - NLM +STAT- MEDLINE +DCOM- 20220802 +LR - 20250728 +IS - 1664-3224 (Electronic) +IS - 1664-3224 (Linking) +VI - 13 +DP - 2022 +TI - Machine Learning Approaches to TCR Repertoire Analysis. +PG - 858057 +LID - 10.3389/fimmu.2022.858057 [doi] +LID - 858057 +AB - Sparked by the development of genome sequencing technology, the quantity and + quality of data handled in immunological research have been changing + dramatically. Various data and database platforms are now driving the rapid + progress of machine learning for immunological data analysis. Of various topics + in immunology, T cell receptor repertoire analysis is one of the most important + targets of machine learning for assessing the state and abnormalities of immune + systems. In this paper, we review recent repertoire analysis methods based on + machine learning and deep learning and discuss their prospects. +CI - Copyright © 2022 Katayama, Yokota, Akiyama and Kobayashi. +FAU - Katayama, Yotaro +AU - Katayama Y +AD - Graduate School of Engineering, The University of Tokyo, Tokyo, Japan. +FAU - Yokota, Ryo +AU - Yokota R +AD - National Research Institute of Police Science, Kashiwa, Chiba, Japan. +FAU - Akiyama, Taishin +AU - Akiyama T +AD - Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, + Yokohama, Japan. +AD - Graduate School of Medical Life Science, Yokohama City University, Yokohama, + Japan. +FAU - Kobayashi, Tetsuya J +AU - Kobayashi TJ +AD - Institute of Industrial Science, The University of Tokyo, Tokyo, Japan. +AD - Graduate School of Engineering, The University of Tokyo, Tokyo, Japan. +LA - eng +PT - Journal Article +PT - Research Support, Non-U.S. Gov't +PT - Review +DEP - 20220715 +PL - Switzerland +TA - Front Immunol +JT - Frontiers in immunology +JID - 101560960 +RN - 0 (Receptors, Antigen, T-Cell) +SB - IM +MH - *Immune System +MH - *Machine Learning +MH - Receptors, Antigen, T-Cell/genetics +PMC - PMC9334875 +OTO - NOTNLM +OT - T cell +OT - T cell receptor +OT - deep learning +OT - immunoinformatics +OT - machine learning +COIS- The authors declare that the research was conducted in the absence of any + commercial or financial relationships that could be construed as a potential + conflict of interest. +EDAT- 2022/08/02 06:00 +MHDA- 2022/08/03 06:00 +PMCR- 2022/01/01 +CRDT- 2022/08/01 03:51 +PHST- 2022/01/19 00:00 [received] +PHST- 2022/06/07 00:00 [accepted] +PHST- 2022/08/01 03:51 [entrez] +PHST- 2022/08/02 06:00 [pubmed] +PHST- 2022/08/03 06:00 [medline] +PHST- 2022/01/01 00:00 [pmc-release] +AID - 10.3389/fimmu.2022.858057 [doi] +PST - epublish +SO - Front Immunol. 2022 Jul 15;13:858057. doi: 10.3389/fimmu.2022.858057. eCollection + 2022. 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