Computer methods and programs in biomedicine update最新文献

{"title":"Isolation and abuse: The intersection of Covid19 and domestic violence","authors":"Sidra Waseem Khan ,&nbsp;Hafsah Arshed Ali Khan ,&nbsp;Dawn Clarke","doi":"10.1016/j.cmpbup.2024.100149","DOIUrl":"10.1016/j.cmpbup.2024.100149","url":null,"abstract":"<div><p>Amid the global lockdowns, the surge in domestic violence cases has been one of the distressing consequences of the Covid19 pandemic [<span>1</span>]. Isolation, stress, and economic distress amongst other factors have all contributed to an increase in this form of abuse. Women have been subjected to discrimination and abuse for around 2700 years, and a clear example of such discrimination can be seen in the form of laws operating in 753 BCE that allowed the disciplining of wives [<span>2</span>]. The matter of domestic abuse started receiving recognition in the 1970s when it became a compulsion on all the certified hospitals by the Joint Commission on Accreditation of Health Care Organizations to refer patients of domestic abuse to authorities after treating them [<span>3</span>].</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000168/pdfft?md5=0263ba1128c8e267657bbc317fe3b81e&pid=1-s2.0-S2666990024000168-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140274269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing clinical decision support: The role of artificial intelligence across six domains","authors":"Mohamed Khalifa ,&nbsp;Mona Albadawy ,&nbsp;Usman Iqbal","doi":"10.1016/j.cmpbup.2024.100142","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100142","url":null,"abstract":"<div><h3>Background</h3><p>Artificial Intelligence (AI) is a transformative force in clinical decision support (CDS) systems within healthcare. Its emergence, fuelled by the growing volume and diversity of healthcare data, offers significant potential in patient care, diagnosis, treatment, and health management. This study systematically reviews AI's role in enhancing CDS across six domains, underscoring its impact on patient outcomes and healthcare efficiency.</p></div><div><h3>Methods</h3><p>A four-step systematic review was conducted, involving a comprehensive literature search, application of inclusion and exclusion criteria, data extraction and synthesis, and analysis. Sources included PubMed, Embase, and Google Scholar, with papers published in English since 2019. Selected studies focused on AI's application in CDS, with 32 papers ultimately reviewed.</p></div><div><h3>Results</h3><p>The review identified six AI CDS domains: Data-Driven Insights and Analytics, Diagnostic and Predictive Modelling, Treatment Optimisation and Personalised Medicine, Patient Monitoring and Telehealth Integration, Workflow and Administrative Efficiency, and Knowledge Management and Decision Support. Each domain is crucial in improving various aspects of CDS, from enhancing diagnostic accuracy to optimising resource management. AI's capabilities in EHR analysis, predictive analytics, personalised treatment, and telehealth demonstrate its critical role in advancing healthcare.</p></div><div><h3>Discussion</h3><p>AI significantly enhances healthcare by improving diagnostic precision, predictive capabilities, and administrative efficiency. It facilitates personalised medicine, remote monitoring, and evidence-based decision-making. However, challenges such as data privacy, ethical considerations, and integration with existing systems persist. This requires collaboration among technologists, healthcare professionals, and policymakers.</p></div><div><h3>Conclusion</h3><p>AI is revolutionising healthcare by enhancing CDS in several domains, contributing to more efficient, effective, and patient-centric care. However, it should complement, not replace, human expertise. Future directions include ethical AI development, continuous professional development for healthcare personnel, and collaborative efforts to address challenges. This approach ensures AI's potential is fully harnessed, leading to a synergistic blend of technology and human care.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100142"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000090/pdfft?md5=aaaa8b38d130717ba82fc96ec2dea81f&pid=1-s2.0-S2666990024000090-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139908302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fostering digital health literacy to enhance trust and improve health outcomes","authors":"Kristine Sørensen","doi":"10.1016/j.cmpbup.2024.100140","DOIUrl":"10.1016/j.cmpbup.2024.100140","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000077/pdfft?md5=5fddb8d7de20b2508c53b5099afe8495&pid=1-s2.0-S2666990024000077-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139874509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acknowledgments to our reviewers in 2023","authors":"","doi":"10.1016/j.cmpbup.2024.100138","DOIUrl":"10.1016/j.cmpbup.2024.100138","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000053/pdfft?md5=009e4e2d12849f38ea7767ad3627e409&pid=1-s2.0-S2666990024000053-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139639048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for diabetes: Enhancing prevention, diagnosis, and effective management","authors":"Mohamed Khalifa ,&nbsp;Mona Albadawy","doi":"10.1016/j.cmpbup.2024.100141","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100141","url":null,"abstract":"<div><h3>Introduction</h3><p>Diabetes, a major cause of premature mortality and complications, affects millions globally, with its prevalence increasing due to lifestyle factors and aging populations. This systematic review explores the role of Artificial Intelligence (AI) in enhancing the prevention, diagnosis, and management of diabetes, highlighting the potential for personalised and proactive healthcare.</p></div><div><h3>Methods</h3><p>A structured four-step method was used, including extensive literature searches, specific inclusion and exclusion criteria, data extraction from selected studies focusing on AI's role in diabetes, and thorough analysis to identify specific domains and functions where AI contributes significantly.</p></div><div><h3>Results</h3><p>Through examining 43 experimental studies, AI has been identified as a transformative force across eight key domains in diabetes care: 1) Diabetes Management and Treatment, 2) Diagnostic and Imaging Technologies, 3) Health Monitoring Systems, 4) Developing Predictive Models, 5) Public Health Interventions, 6) Lifestyle and Dietary Management, 7) Enhancing Clinical Decision-Making, and 8) Patient Engagement and Self-Management. Each domain showcases AI's potential to revolutionize care, from personalizing treatment plans and improving diagnostic accuracy to enhancing patient engagement and predictive healthcare.</p></div><div><h3>Discussion</h3><p>AI's integration into diabetes care offers personalised, efficient, and proactive solutions. It enhances care accuracy, empowers patients, and provides better understanding of diabetes management. However, the successful implementation of AI requires continued research, data security, interdisciplinary collaboration, and a focus on patient-centered solutions. Education for healthcare professionals and regulatory frameworks are also crucial to address challenges like algorithmic bias and ethics.</p></div><div><h3>Conclusion and Recommendations</h3><p>AI in diabetes care promises improved health outcomes and quality of life through personalised and proactive healthcare. Future efforts should focus on continued investment, ensuring data security, fostering interdisciplinary collaboration, and prioritizing patient-centered solutions. Regular monitoring and evaluation are essential to adjust strategies and understand long-term impacts, ensuring AI's ethical and effective integration into healthcare.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000089/pdfft?md5=df60e0f94e7b010da1e695a8a5bf5d47&pid=1-s2.0-S2666990024000089-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical study on normal lung sounds in bronchial airways under different breathing intensities","authors":"Huiqiang Li ,&nbsp;Xiaozhao Li ,&nbsp;Juntao Feng","doi":"10.1016/j.cmpbup.2024.100154","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100154","url":null,"abstract":"<div><h3>Background</h3><p>Due to the complexity of airways and the limitation of experiments, the production mechanism of the lung sounds in airways has not been fully understood, which often confuses diagnosis.</p></div><div><h3>Method</h3><p>A 3D geometrical model of human airways (G5-G8) has been developed based on Weibel's model. Simulation on transient airflow and the noise production during exhalation under different breathing intensities (<em>Q</em> = 15, 30, 45, 60, 75, 90 L/min) has been carried out with Direct Noise Computation (DNC) and Ffowcs Williams-Hawkings (FW-H) method.</p></div><div><h3>Results</h3><p>(1) The junctions between airways are most likely to produce lung sounds, and the peak value is located in the junction between G7 and G6 at the middle of exhalation (about 0.75 s). (2) With the increase in breathing intensity, the average sound pressure level first increases, reaches the peak value at 70–75 L/min, and then drops. (3) Higher breathing intensity is helpful to produce the feature of wheezing, namely a comparatively higher sound pressure level in the range of 200–500 Hz. Moreover, this feature is prominent with the increase in breathing intensity.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000211/pdfft?md5=6b1cdf9b1b9d99f91f6def14fe7bffab&pid=1-s2.0-S2666990024000211-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140604780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI in diagnostic imaging: Revolutionising accuracy and efficiency","authors":"Mohamed Khalifa ,&nbsp;Mona Albadawy","doi":"10.1016/j.cmpbup.2024.100146","DOIUrl":"10.1016/j.cmpbup.2024.100146","url":null,"abstract":"<div><h3>Introduction</h3><p>This review evaluates the role of Artificial Intelligence (AI) in transforming diagnostic imaging in healthcare. AI has the potential to enhance accuracy and efficiency of interpreting medical images like X-rays, MRIs, and CT scans.</p></div><div><h3>Methods</h3><p>A comprehensive literature search across databases like PubMed, Embase, and Google Scholar was conducted, focusing on articles published in peer-reviewed journals in English language since 2019. Inclusion criteria targeted studies on AI's application in diagnostic imaging, while exclusion criteria filtered out irrelevant or empirically unsupported studies.</p></div><div><h3>Results and discussion</h3><p>Through 30 included studies, the review identifies four AI domains and eight functions in diagnostic imaging: 1) In the area of Image Analysis and Interpretation, AI capabilities enhanced image analysis, spotting minor discrepancies and anomalies, and by reducing human error, maintaining accuracy and mitigating the impact of fatigue or oversight, 2) The Operational Efficiency is enhanced by AI through efficiency and speed, which accelerates the diagnostic process, and cost-effectiveness, reducing healthcare costs by improving efficiency and accuracy, 3) Predictive and Personalised Healthcare benefit from AI through predictive analytics, leveraging historical data for early diagnosis, and personalised medicine, which employs patient-specific data for tailored diagnostic approaches, 4) Lastly, in Clinical Decision Support, AI assists in complex procedures by providing precise imaging support and integrates with other technologies like electronic health records for enriched health insights, showcasing ai's transformative potential in diagnostic imaging. The review also discusses challenges in AI integration, such as ethical concerns, data privacy, and the need for technology investments and training.</p></div><div><h3>Conclusion</h3><p>AI is revolutionising diagnostic imaging by improving accuracy, efficiency, and personalised healthcare delivery. Recommendations include continued investment in AI, establishment of ethical guidelines, training for healthcare professionals, and ensuring patient-centred AI development. The review calls for collaborative efforts to integrate AI in clinical practice effectively and address healthcare disparities.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000132/pdfft?md5=dc2a7d25e2ce178c93e675f9e58901e5&pid=1-s2.0-S2666990024000132-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital health literacy and information-seeking on the internet in relation to COVID-19 among university students in Greece","authors":"Evanthia Sakellari ,&nbsp;Orkan Okan ,&nbsp;Kevin Dadaczynski ,&nbsp;Kostantinos Koutentakis ,&nbsp;Areti Lagiou","doi":"10.1016/j.cmpbup.2024.100139","DOIUrl":"10.1016/j.cmpbup.2024.100139","url":null,"abstract":"<div><h3>Background</h3><p>COVID-19 is the first pandemic in history in which technology and social media are being used for people to be informed and be safe. Thus, digital health literacy skills affect the way people will protect and promote their health.</p></div><div><h3>Methods</h3><p>A cross-sectional web-based study was conducted with a convenience sample among university students (<em>N</em>=604) from one of the Universities located in Attica (Greece) during May - June 2020. The COVID-HL university students survey questionnaire was used for collecting the data.</p></div><div><h3>Results</h3><p>In regards to information search, 28 % of the university students indicated that they found it very difficult/difficult to find the exact information they were looking for and 20.4 % to make a choice from all the information they found. Additionally, 45.1 % of the participants found it very difficult/difficult to decide whether the information retrieved via online search is reliable or not.</p></div><div><h3>Conclusion</h3><p>The results indicate a need for the promotion of digital health literacy among university students and therefore, health education interventions need to optimize students’ seeking skills and critical thinking. Health educators should consider the results of this study and involve the university students in any intervention they plan in order to address the students’ specific needs. It is also suggested that these health education interventions should be integrated throughout all academic activities.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000065/pdfft?md5=7424d30d38ac13d3fb171812c2d3fc89&pid=1-s2.0-S2666990024000065-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139638316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis","authors":"Deepak Kumar ,&nbsp;Brijesh Bakariya ,&nbsp;Chaman Verma ,&nbsp;Zoltán Illés","doi":"10.1016/j.cmpbup.2024.100165","DOIUrl":"10.1016/j.cmpbup.2024.100165","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Accurate classification of liver disease stages provides crucial insights into patient prognosis, aiding in the prediction of disease outcomes and influencing clinical decision-making. There is an urgent need for non-invasive methods to diagnose various stages of liver dysfunction and uncover hidden pattern based on individual disease characteristics.</div></div><div><h3>Method:</h3><div>One popular and effective approach is collecting serum biomarker samples. The study was conducted on collected serum biomaker samples of 81 patients with Inflammatory Bowel Disease (IBD) of Changhua Christian Hospital in China, including 36 with Crohn’s disease (CD) and 45 with Ulcerative Colitis (UC) using Latent Semantic Analysis(LSA) and machine learning (ML) techniques.Machine Learning algorithms Random Forest (RF), Logistic Regression (LR), XGBoost (XGB), and Support Vector Classifier (SVC), were utilized to predict liver risk associated with conditions including Hepatitis, Autoimmune Hepatitis (AIH), Alcoholic Liver Disease (ALD), and Non-Alcoholic Fatty Liver Disease (NAFLD). Models’ accuracy was assessed using K-Fold Cross-Validation (CV).Distinct pattern were identified using Latent Semantic Analysis(LSA). Furthermore, SHAP plots were utilized for enhanced interpretability, highlighting essential features for liver dysfunction levels.</div></div><div><h3>Results:</h3><div>The inflammatory profile, mixed disease profile, and healthy profile were the three distinct clusters were identified with LSA. The RF model achieved high accuracy of <span><math><mrow><mn>0</mn><mo>.</mo><mn>94</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>06</mn></mrow></math></span>. Serum Glutamate Pyruvate Transaminase (GPT), Age at Diagnosis (AAD), Erythrocyte Sedimentation Rate (ESR), C-reactive protein (CRP) were found the most key important features in liver disease staging increment.</div></div><div><h3>Conclusion:</h3><div>The research significantly contributes to the fields of biomedical informatics and clinical decision-making. The developed model offers valuable decision-making tools for clinicians, enabling early and targeted interventions.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"6 ","pages":"Article 100165"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches for predicting frailty base on multimorbidities in US adults using NHANES data (1999–2018)","authors":"Teng Li ,&nbsp;Xueke Li ,&nbsp;Haoran XU ,&nbsp;Yanyan Wang ,&nbsp;Jingyu Ren ,&nbsp;Shixiang Jing ,&nbsp;Zichen Jin ,&nbsp;Gang chen ,&nbsp;Youyou Zhai ,&nbsp;Zeyu Wu ,&nbsp;Ge Zhang ,&nbsp;Yuying Wang","doi":"10.1016/j.cmpbup.2024.100164","DOIUrl":"10.1016/j.cmpbup.2024.100164","url":null,"abstract":"<div><h3>Background</h3><p>The global increase in an aging population has led to more common age-related health challenges, particularly multimorbidity and frailty, but there is a significant gap.</p></div><div><h3>Methods</h3><p>This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (1999–2018). The association between age and frailty was assessed using a restricted cubic spline (RCS) model, while weighted adjusted multivariable logistic regression evaluated the effect of diseases to frailty. And in machine learning process, feature selection for the frailty prediction model involved three algorithms. The model's performance was optimized using nested cross-validation and tested with various algorithms including decision tree, Logistic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and Regression Trees, and eXtreme Gradient Boosting (XGBoost). We used areas under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AU-PRC) to evaluate six algorithms, select the optimal model, and test the discrimination and consistency of the optimal model.</p></div><div><h3>Results</h3><p>The study included 46,187 participants, with 6,009 cases of frailty. RCS analysis showed a non-linear association between age and frailty, with a turning point at 49 years. Key impacting variables identified are Anemia, Arthritis, Diabetes Mellitus, Coronary Heart Disease, and Hypertension. In the machine learning process, we selected the optimal data set by feature selection, including 13 variables. Through nested cross-validation, a total of 31,900 models were built using 6 algorithms. And the XGBoost model showed the highest performance (AUC = 0.8828 and AU-PRC = 0.624), and clear proficiency in both discrimination and calibration.</p></div><div><h3>Conclusions</h3><p>We found 49 years maintain the balance of physiological reserve and external aggression. In addition, chronic diseases are trigger factor of frailty, while acute diseases are contributing factor that exacerbates the body's rapid decline. Last, the XGBoost frailty prediction model, with its simplicity, high performance and high clinical value holds potential for clinical application.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"6 ","pages":"Article 100164"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000314/pdfft?md5=b2ac2f1faea71ce864789e43929be852&pid=1-s2.0-S2666990024000314-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}