Pub Date : 2025-07-01DOI: 10.1016/j.simpa.2025.100781
Christian Ghiaus
ELECTRE Tri-B is a sorting and classification method for multiple-criteria decision-making (MCDM) in which alternatives are assigned to categories. The categories are completely ordered and defined by base (or reference) profiles. The pELECTRE Tri software implements a probabilistic extension of the ELECTRE Tri-B method designed to handle uncertainty in both the decision matrix values and the base profiles delimiting the categories. Its modular architecture enables step-by-step workflows from data input to results output, ensuring flexibility and transparency in the decision-making process. Implemented as a Python module, pELECTRE Tri requires no installation and can be executed locally or online. The software is supported by comprehensive documentation, including tutorials, how-to guides, theoretical explanations, and a user reference manual.
{"title":"pELECTRE Tri: A computational framework and Python module for probabilistic ELECTRE Tri-B multiple-criteria decision-making","authors":"Christian Ghiaus","doi":"10.1016/j.simpa.2025.100781","DOIUrl":"10.1016/j.simpa.2025.100781","url":null,"abstract":"<div><div>ELECTRE Tri-B is a sorting and classification method for multiple-criteria decision-making (MCDM) in which alternatives are assigned to categories. The categories are completely ordered and defined by base (or reference) profiles. The <em>pELECTRE Tri</em> software implements a probabilistic extension of the ELECTRE Tri-B method designed to handle uncertainty in both the decision matrix values and the base profiles delimiting the categories. Its modular architecture enables step-by-step workflows from data input to results output, ensuring flexibility and transparency in the decision-making process. Implemented as a Python module, <em>pELECTRE Tri</em> requires no installation and can be executed locally or online. The software is supported by comprehensive documentation, including tutorials, how-to guides, theoretical explanations, and a user reference manual.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100781"},"PeriodicalIF":1.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.simpa.2025.100777
Nimmy S. John, ChangHwan Lee
Single-molecule RNA imaging has been made possible with the recent advances in microscopy methods. However, systematic analysis of these images has been challenging due to the highly variable background noise, even after applying sophisticated computational clearing methods. Here, we describe our custom MATLAB scripts that allow us to detect both nuclear nascent transcripts at the active transcription sites (ATS) and mature cytoplasmic mRNAs with single-molecule precision and reconstruct the tissue in 3D for further analysis. Our codes were initially optimized for the C. elegans germline but were designed to be broadly applicable to other species and tissue types.
{"title":"smFISH_batchRun: A smFISH image processing tool for single-molecule RNA Detection and 3D reconstruction","authors":"Nimmy S. John, ChangHwan Lee","doi":"10.1016/j.simpa.2025.100777","DOIUrl":"10.1016/j.simpa.2025.100777","url":null,"abstract":"<div><div>Single-molecule RNA imaging has been made possible with the recent advances in microscopy methods. However, systematic analysis of these images has been challenging due to the highly variable background noise, even after applying sophisticated computational clearing methods. Here, we describe our custom MATLAB scripts that allow us to detect both nuclear nascent transcripts at the active transcription sites (ATS) and mature cytoplasmic mRNAs with single-molecule precision and reconstruct the tissue in 3D for further analysis. Our codes were initially optimized for the <em>C. elegans</em> germline but were designed to be broadly applicable to other species and tissue types.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100777"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.simpa.2025.100778
Mehmet Anil Akbay , Christian Blum
This paper presents a web-based instance generator for Electric Vehicle Routing Problems (EVRP) with Road Junctions and Road Types, using OpenStreetMap data. Users define an area, specify network components (depots, customers, charging stations, junctions), and customize vehicle parameters. The React-based frontend enables configuration, visualization, and queries, while the Flask backend processes road networks, classifies road types, and assigns demand and service times. A RESTful API ensures real-time instance generation. Generated instances can be downloaded as text-based datasets and interactive visualizations. The tool is open-source and contributes to the area of sustainable transportation by enabling scenario-based EVRP experimentation.
{"title":"EVRPGen: A web-based instance generator for the electric vehicle routing problem with road junctions and road types","authors":"Mehmet Anil Akbay , Christian Blum","doi":"10.1016/j.simpa.2025.100778","DOIUrl":"10.1016/j.simpa.2025.100778","url":null,"abstract":"<div><div>This paper presents a web-based instance generator for Electric Vehicle Routing Problems (EVRP) with Road Junctions and Road Types, using OpenStreetMap data. Users define an area, specify network components (depots, customers, charging stations, junctions), and customize vehicle parameters. The React-based frontend enables configuration, visualization, and queries, while the Flask backend processes road networks, classifies road types, and assigns demand and service times. A RESTful API ensures real-time instance generation. Generated instances can be downloaded as text-based datasets and interactive visualizations. The tool is open-source and contributes to the area of sustainable transportation by enabling scenario-based EVRP experimentation.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100778"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Effective feature extraction from medical images is important for improving disease detection and assessment. Conventional linear transforms, such as the Radon transform, may not fully capture subtle and complex nonlinear features present in medical imaging data. To address these limitations, we present RadEx, a nonlinear extension of the Radon transform. RadEx employs parameterized nonlinear projections to facilitate the extraction of additional nonlinear feature representations from imaging modalities such as chest X-rays and retinal fundus images. Initial evaluations indicate that RadEx can offer improvements over traditional Radon transforms and raw image-based approaches in disease classification tasks, including COVID-19 detection from chest X-rays and diabetic retinopathy grading from retinal images. By capturing more complex structural and nonlinear patterns, RadEx may support enhanced diagnostic performance and illustrates the potential benefit of integrating adaptive mathematical transformations into medical imaging workflows.
{"title":"RadEx: An open source python package for nonlinear radon transformation","authors":"Farida Mohsen, Ashhadul Islam, Firas Mohsen, Zubair Shah, Samir Brahim Belhaouari","doi":"10.1016/j.simpa.2025.100779","DOIUrl":"10.1016/j.simpa.2025.100779","url":null,"abstract":"<div><div>Effective feature extraction from medical images is important for improving disease detection and assessment. Conventional linear transforms, such as the Radon transform, may not fully capture subtle and complex nonlinear features present in medical imaging data. To address these limitations, we present RadEx, a nonlinear extension of the Radon transform. RadEx employs parameterized nonlinear projections to facilitate the extraction of additional nonlinear feature representations from imaging modalities such as chest X-rays and retinal fundus images. Initial evaluations indicate that RadEx can offer improvements over traditional Radon transforms and raw image-based approaches in disease classification tasks, including COVID-19 detection from chest X-rays and diabetic retinopathy grading from retinal images. By capturing more complex structural and nonlinear patterns, RadEx may support enhanced diagnostic performance and illustrates the potential benefit of integrating adaptive mathematical transformations into medical imaging workflows.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100779"},"PeriodicalIF":1.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-17DOI: 10.1016/j.simpa.2025.100774
Ahmed Alaff , Çelebi Uluyol
Conventional vocabulary assessments emphasize precision rather than hesitation and rapidity. A machine learning system was developed utilizing behavioral analysis and linguistic insights to identify vocabulary gaps in Turkish language learners. This system integrates hesitation counts, reaction times, and answer attempts with word difficulty and thematic elements. Vocabulary strength was computed using a rule-based equation derived from behavioral indications. With 89% accuracy, 86% precision, 91% recall, and an 88% F1 score, the model showed better performance than the linear and Poisson kernel alternatives. By effectively separating complex interactions, the RBF kernel minimizes unnecessary actions and ensures accurate identification of real shortages.
{"title":"TR-VABML: Enhancing Turkish vocabulary acquisition through adaptive machine learning classification","authors":"Ahmed Alaff , Çelebi Uluyol","doi":"10.1016/j.simpa.2025.100774","DOIUrl":"10.1016/j.simpa.2025.100774","url":null,"abstract":"<div><div>Conventional vocabulary assessments emphasize precision rather than hesitation and rapidity. A machine learning system was developed utilizing behavioral analysis and linguistic insights to identify vocabulary gaps in Turkish language learners. This system integrates hesitation counts, reaction times, and answer attempts with word difficulty and thematic elements. Vocabulary strength was computed using a rule-based equation derived from behavioral indications. With 89% accuracy, 86% precision, 91% recall, and an 88% F1 score, the model showed better performance than the linear and Poisson kernel alternatives. By effectively separating complex interactions, the RBF kernel minimizes unnecessary actions and ensures accurate identification of real shortages.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100774"},"PeriodicalIF":1.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-14DOI: 10.1016/j.simpa.2025.100770
Dimas Ramdhan, Elshad Ryan Ardiyanto, Patrick Alexander, Edyth Novian Putra, David
Nyctophy is a serious game combining virtual reality (VR) and smartwatch integration for nyctophobia (fear of darkness) therapy. The paper thoroughly explores its development framework, simulating dark environments with real-time heart rate monitoring and adaptive flashlight mechanics. Built in Unity Engine, Nyctophy supports VR (Meta Quest 2) and keyboard–mouse interfaces. Performance tests achieved 71.9 FPS (”good” quality) across four devices. Tests with 34 participants revealed longer VR completion times (8:12 min) versus keyboard–mouse (3:54), highlighting immersive impact. Nyctophy demonstrates potential as a safe, innovative tool for diagnosing and treating nyctophobia, leveraging serious games to enhance accessibility and therapeutic outcomes.
{"title":"Nyctophy: Development of virtual reality and smartwatch integrated serious game for nyctophobia therapy","authors":"Dimas Ramdhan, Elshad Ryan Ardiyanto, Patrick Alexander, Edyth Novian Putra, David","doi":"10.1016/j.simpa.2025.100770","DOIUrl":"10.1016/j.simpa.2025.100770","url":null,"abstract":"<div><div>Nyctophy is a serious game combining virtual reality (VR) and smartwatch integration for nyctophobia (fear of darkness) therapy. The paper thoroughly explores its development framework, simulating dark environments with real-time heart rate monitoring and adaptive flashlight mechanics. Built in Unity Engine, Nyctophy supports VR (Meta Quest 2) and keyboard–mouse interfaces. Performance tests achieved 71.9 FPS (”good” quality) across four devices. Tests with 34 participants revealed longer VR completion times (8:12 min) versus keyboard–mouse (3:54), highlighting immersive impact. Nyctophy demonstrates potential as a safe, innovative tool for diagnosing and treating nyctophobia, leveraging serious games to enhance accessibility and therapeutic outcomes.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100770"},"PeriodicalIF":1.3,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a MATLAB-based computational software, TERANG to estimate physical and operational losses for school building in Indonesia. The basis of the estimation model used is the HAZUS model. TERANG provides modifications to the HAZUS model on school building cost parameters and reconstruction cost, as well as adjustments to local hazard data. TERANG provides an overview of the HAZUS model adoption process for countries that do not yet have a school building database. TERANG software supports Indonesia’s seismic loss studies, estimating school damages in Bandung and Mamuju’s 2021 earthquake while raising awareness among school stakeholders.
{"title":"TERANG: Seismic loss estimation tool for school buildings","authors":"Roi Milyardi , Krishna Suryanto Pribadi , Muhamad Abduh , Irwan Meilano , Erwin Lim","doi":"10.1016/j.simpa.2025.100773","DOIUrl":"10.1016/j.simpa.2025.100773","url":null,"abstract":"<div><div>This article presents a MATLAB-based computational software, TERANG to estimate physical and operational losses for school building in Indonesia. The basis of the estimation model used is the HAZUS model. TERANG provides modifications to the HAZUS model on school building cost parameters and reconstruction cost, as well as adjustments to local hazard data. TERANG provides an overview of the HAZUS model adoption process for countries that do not yet have a school building database. TERANG software supports Indonesia’s seismic loss studies, estimating school damages in Bandung and Mamuju’s 2021 earthquake while raising awareness among school stakeholders.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100773"},"PeriodicalIF":1.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-10DOI: 10.1016/j.simpa.2025.100771
Sait Alp , Sara Akan , Taymaz Akan , Mohammad Alfrad Nobel Bhuiyan
This study introduces a reproducible pipeline for classifying Alzheimer’s Disease from structural brain MRI utilizing a joint transformer architecture that integrates Vision Transformer and Time-Series Transformer models. The proposed framework uses pre-trained ViT for feature extraction from 2D slices of MRI volumes, followed by sequential modeling with a transformer-based classifier to capture inter-slice dependencies. The method is evaluated on the ADNI dataset, involving both binary (AD vs. NC) and multiclass (AD, MCI, NC) classification tasks across axial, sagittal, and coronal planes.
本研究介绍了一种可重复的管道，利用集成视觉变压器和时间序列变压器模型的联合变压器架构，从结构脑MRI中对阿尔茨海默病进行分类。所提出的框架使用预训练的ViT从MRI体积的二维切片中提取特征，然后使用基于变压器的分类器进行顺序建模以捕获切片间的依赖关系。该方法在ADNI数据集上进行了评估，包括二元（AD vs. NC）和多类别（AD， MCI， NC）跨轴向，矢状面和冠状面分类任务。
{"title":"MRI-based Alzheimer’s disease classification using Vision Transformer and time-series transformer: A step-by-step guide","authors":"Sait Alp , Sara Akan , Taymaz Akan , Mohammad Alfrad Nobel Bhuiyan","doi":"10.1016/j.simpa.2025.100771","DOIUrl":"10.1016/j.simpa.2025.100771","url":null,"abstract":"<div><div>This study introduces a reproducible pipeline for classifying Alzheimer’s Disease from structural brain MRI utilizing a joint transformer architecture that integrates Vision Transformer and Time-Series Transformer models. The proposed framework uses pre-trained ViT for feature extraction from 2D slices of MRI volumes, followed by sequential modeling with a transformer-based classifier to capture inter-slice dependencies. The method is evaluated on the ADNI dataset, involving both binary (AD vs. NC) and multiclass (AD, MCI, NC) classification tasks across axial, sagittal, and coronal planes.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100771"},"PeriodicalIF":1.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-10DOI: 10.1016/j.simpa.2025.100775
S. Herold-Garcia , H.L. Varona-Gonzalez , X. Gual-Arnau
Shape analysis within shape space provides a robust framework for examining geometric properties of objects, enabling comparisons invariant to translation, rotation, and scaling. A key task is computing geodesic distances between shapes, which quantify similarity but are computationally intensive due to the need for exhaustive parameterization searches. Recent advancements propose heuristic methods to simplify these computations, such as fixing parameterizations based on the major axis of shapes, significantly reducing computational costs while maintaining high accuracy (e.g., 96.03% in erythrocyte classification). This article introduces a software tool that leverages this heuristic to efficiently compute shape-space distances, aligning shapes considering their major axis, and using templates like circles and ellipses. The tool accelerates morphological analysis, making it ideal for high performance applications in fields like biology and medicine. By streamlining the computation of geodesic distances between shapes and enabling rapid retrieval of information, this software improves research workflows and supports the study of shape-dependent features in diverse fields from cellular morphology to diagnostic hematology.
{"title":"GD4Shapes: Geodesic distance with fixed parameterization for 2D Shapes","authors":"S. Herold-Garcia , H.L. Varona-Gonzalez , X. Gual-Arnau","doi":"10.1016/j.simpa.2025.100775","DOIUrl":"10.1016/j.simpa.2025.100775","url":null,"abstract":"<div><div>Shape analysis within shape space provides a robust framework for examining geometric properties of objects, enabling comparisons invariant to translation, rotation, and scaling. A key task is computing geodesic distances between shapes, which quantify similarity but are computationally intensive due to the need for exhaustive parameterization searches. Recent advancements propose heuristic methods to simplify these computations, such as fixing parameterizations based on the major axis of shapes, significantly reducing computational costs while maintaining high accuracy (e.g., 96.03% in erythrocyte classification). This article introduces a software tool that leverages this heuristic to efficiently compute shape-space distances, aligning shapes considering their major axis, and using templates like circles and ellipses. The tool accelerates morphological analysis, making it ideal for high performance applications in fields like biology and medicine. By streamlining the computation of geodesic distances between shapes and enabling rapid retrieval of information, this software improves research workflows and supports the study of shape-dependent features in diverse fields from cellular morphology to diagnostic hematology.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100775"},"PeriodicalIF":1.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ESCOX, also known as ESCOSkillExtractor, is an open-source, non-proprietary tool for identifying and classifying skills, skillsets, and occupations from job postings and general text. It utilizes the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy to structure extraction, addressing the need for taxonomy-aligned skill identification in unstructured labor market data. Developed within the SKILLAB EU Horizon project, ESCOX combines LLMs and text embeddings to map content to standardized categories. It offers a user-friendly graphical interface for researchers, educators, and HR professionals, supporting skills gap analysis, training, recruitment, and policy planning, and contributing to the development of a skills-based economy.
ESCOX，也被称为ESCOSkillExtractor，是一个开源、非专有的工具，用于从招聘启事和一般文本中识别和分类技能、技能集和职业。它利用欧洲技能、能力、资格和职业（ESCO）分类法进行结构提取，解决了在非结构化劳动力市场数据中对分类一致的技能识别的需求。ESCOX是在SKILLAB EU Horizon项目中开发的，它结合了法学硕士和文本嵌入，将内容映射到标准化类别。它为研究人员、教育工作者和人力资源专业人员提供了一个用户友好的图形界面，支持技能差距分析、培训、招聘和政策规划，并为技能经济的发展做出贡献。
{"title":"ESCOX: A tool for skill and occupation extraction using LLMs from unstructured text","authors":"Dimitrios Christos Kavargyris , Konstantinos Georgiou , Eleanna Papaioannou , Konstantinos Petrakis , Nikolaos Mittas , Lefteris Angelis","doi":"10.1016/j.simpa.2025.100772","DOIUrl":"10.1016/j.simpa.2025.100772","url":null,"abstract":"<div><div>ESCOX, also known as ESCOSkillExtractor, is an open-source, non-proprietary tool for identifying and classifying skills, skillsets, and occupations from job postings and general text. It utilizes the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy to structure extraction, addressing the need for taxonomy-aligned skill identification in unstructured labor market data. Developed within the SKILLAB EU Horizon project, ESCOX combines LLMs and text embeddings to map content to standardized categories. It offers a user-friendly graphical interface for researchers, educators, and HR professionals, supporting skills gap analysis, training, recruitment, and policy planning, and contributing to the development of a skills-based economy.</div></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":"25 ","pages":"Article 100772"},"PeriodicalIF":1.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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