Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference最新文献_第10页

Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782103

Charles A Ellis, Robyn L Miller, Vince D Calhoun

{"title":"Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data.","authors":"Charles A Ellis, Robyn L Miller, Vince D Calhoun","doi":"10.1109/EMBC53108.2024.10782103","DOIUrl":"10.1109/EMBC53108.2024.10782103","url":null,"abstract":"While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their potential for clinical translation. Data augmentation (DA) could address this limitation, but the utility of EEG DA methods remains relatively underexplored in neuropsychiatric disorder diagnosis. In this study, we train a model for major depressive disorder diagnosis. We then evaluate the utility of 6 EEG DA approaches. Importantly, to remove the bias that could be introduced by comparing performance for models trained on larger augmented training sets to models trained on smaller baseline sets, we also introduce a new baseline trained on duplicate training data. We lastly examine the effects of the DA approaches upon representations learned by the model with a pair of explainability analyses. We find that while most approaches boost model performance, they do not improve model performance beyond that of simply using a duplicate training set without DA. The exception to this is channel dropout augmentation, which does improve model performance. These findings suggest the importance of comparing EEG DA methods to a baseline with a duplicate training set of equal size to the augmented training set. We also found that some DA methods increased model robustness to frequency (Fourier transform surrogates) and channel (channel dropout) perturbation. While our findings on EEG DA efficacy are restricted to our dataset and model, we hope that future studies on deep learning for small EEG datasets and on new EEG DA methods will find our findings helpful.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559536","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}

引用次数: 0

FedAssist: Federated Learning in AI-Powered Prosthetics for Sustainable and Collaborative Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781961

Hunmin Lee, Ming Jiang, Qi Zhao

引用次数: 0

Identifying Canonical multi-scale Intrinsic Connectivity Networks in Infant resting-state fMRI and their Association with Age.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782404

Prerana Bajracharya, Ashkan Faghiri, Zening Fu, Vince D Calhoun, Sarah Shultz, Armin Iraji

{"title":"Identifying Canonical multi-scale Intrinsic Connectivity Networks in Infant resting-state fMRI and their Association with Age.","authors":"Prerana Bajracharya, Ashkan Faghiri, Zening Fu, Vince D Calhoun, Sarah Shultz, Armin Iraji","doi":"10.1109/EMBC53108.2024.10782404","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782404","url":null,"abstract":"Intrinsic Connectivity Networks (ICNs) reflect functional brain organization responsible for various cognitive processes, including sensory perception, motor control, memory, and attention. In this study, we used the Multivariate-Objective Optimization Independent Component Analysis with Reference (MOO-ICAR) and the NeuroMark 2.1 (adult) template to estimate subject-specific ICNs in resting-state functional magnetic resonance imaging (rsfMRI) data of infants. The NeuroMark 2.1 template contains 105 multi-scale canonical ICNs derived from 100k+ adults across multiple datasets. The multi-scale ICNs capture functional segregation across various levels of granularity across brain, revealing functional sources and their interactions. The results showed that the 105 ICNs in infants were spatially aligned with those in the template and revealed age-related distinctive patterns in static Functional Network Connectivity (sFNC), particularly in the sub-cortical and high-level cognitive domains. This study is the first to investigate the presence and development of these multi-scale ICNs in infant rsfMRI data. Our findings confirmed the presence of identifiable canonical ICNs in infants as young as six months, showcasing a strong association between these networks and age and suggesting potential biomarkers for early identification of neurodevelopmental disability.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559559","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}

引用次数: 0

Identifying Prediabetes in Canadian Populations Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782174

Katherine Lu, Paijani Sheth, Zhi Lin Zhou, Kamyar Kazari, Aziz Guergachi, Karim Keshavjee, Mohammad Noaeen, Zahra Shakeri

{"title":"Identifying Prediabetes in Canadian Populations Using Machine Learning.","authors":"Katherine Lu, Paijani Sheth, Zhi Lin Zhou, Kamyar Kazari, Aziz Guergachi, Karim Keshavjee, Mohammad Noaeen, Zahra Shakeri","doi":"10.1109/EMBC53108.2024.10782174","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782174","url":null,"abstract":"Prediabetes is a critical health condition characterized by elevated blood glucose levels that fall below the threshold for Type 2 diabetes (T2D) diagnosis. Accurate identification of prediabetes is essential to forestall the progression to T2D among at-risk individuals. This study aims to pinpoint the most effective machine learning (ML) model for prediabetes prediction and to elucidate the key biological variables critical for distinguishing individuals with prediabetes. Utilizing data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), our analysis included 6,414 participants identified as either nondiabetic or prediabetic. A rigorous selection process led to the identification of ten variables for the study, informed by literature review, data completeness, and the evaluation of collinearity. Our comparative analysis of seven ML models revealed that the Deep Neural Network (DNN), enhanced with early stop regularization, outshined others by achieving a recall rate of 60%. This model's performance underscores its potential in effectively identifying prediabetic individuals, showcasing the strategic integration of ML in healthcare. While the model reflects a significant advancement in prediabetes prediction, it also opens avenues for further research to refine prediction accuracy, possibly by integrating novel biological markers or exploring alternative modeling techniques. The results of our work represent a pivotal step forward in the early detection of prediabetes, contributing significantly to preventive healthcare measures and the broader fight against the global epidemic of Type 2 diabetes.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559562","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}

引用次数: 0

Evaluation of Cough Sound Segmentation Algorithms in the Presence of Background Noise.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782675

Roneel V Sharan, Hao Xiong

{"title":"Evaluation of Cough Sound Segmentation Algorithms in the Presence of Background Noise.","authors":"Roneel V Sharan, Hao Xiong","doi":"10.1109/EMBC53108.2024.10782675","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782675","url":null,"abstract":"Automated cough sound segmentation is important for the objective analysis of cough sounds. While various cough sound segmentation algorithms have been proposed over the years, it is not clear how these algorithms perform in the presence of background noise, which can vary in intensity across different environments. Therefore, in this study, we evaluate the performance of cough sound segmentation algorithms in the presence of background noise. Specifically, we examine algorithms employing conventional feature engineering and machine learning methods, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and a combination of CNNs and RNNs. These algorithms are developed using relatively clean cough signals but evaluated under both clean and noisy conditions. The results indicate that, while the performance of all algorithms declined in the presence of background noise, the combination of CNNs and RNNs yielded the best cough segmentation results under both clean and noisy conditions. These findings can contribute to the development of noise-robust cough sound segmentation algorithms for objective cough sound analysis in noisy conditions.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559577","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}

引用次数: 0

Impact of multidirectional vibratory feedback on posture control during standing and weight lifting: a pilot study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782169

Hugo M Martins, Matheus G Nogueira, Pedro Parik-Americano, Rafael T Moura, Arturo Forner-Cordero, Cristina P Camargo

{"title":"Impact of multidirectional vibratory feedback on posture control during standing and weight lifting: a pilot study.","authors":"Hugo M Martins, Matheus G Nogueira, Pedro Parik-Americano, Rafael T Moura, Arturo Forner-Cordero, Cristina P Camargo","doi":"10.1109/EMBC53108.2024.10782169","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782169","url":null,"abstract":"In this pilot study, we investigated the influence of vibratory feedback (VF) on postural control (PC) during frontal weight elevation and standing. We developed a multidirectional VF belt to provide feedback to the user on trunk inclination to correct the center of pressure (CoP) sway. CoP data were measured with a force plate, and user experience was collected through assessed questionnaires. Our findings suggest that VF contributes to a rapid return of the CoP to the Base of Support (BoS) during weight lifting(WL). However, VF may pose challenges during open-eyed conditions or with the addition of a compliant platform, potentially overwhelming users with proprioceptive stimuli and requiring increased attention. Although VF aids in postural correction near the support base and its periphery, individual factors such as user attention and sensitivity influence VF perception. Further studies with larger sample sizes are needed to validate these findings and explore the efficacy of VF in diverse postural control scenarios.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559616","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}

引用次数: 0

Letter Tracing: a Serious Game to Teach Handwriting and Assess Proficiency through Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782843

Linda Greta Dui, Chiara Piazzalunga, Stefania Fontolan, Marisa Bortolozzo, Sandro Franceschini, Cristiano Termine, Simona Ferrante

{"title":"Letter Tracing: a Serious Game to Teach Handwriting and Assess Proficiency through Machine Learning.","authors":"Linda Greta Dui, Chiara Piazzalunga, Stefania Fontolan, Marisa Bortolozzo, Sandro Franceschini, Cristiano Termine, Simona Ferrante","doi":"10.1109/EMBC53108.2024.10782843","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782843","url":null,"abstract":"This work introduces the \"Letter Tracing\", a serious game designed to teach correct letter formation. Co-designed through collaboration with clinicians and technicians, the game underwent testing with 9 first- and 26 second-grade children to assess its ability to capture the evolution of handwriting abilities over time and handwriting proficiency. In the game, children were asked to trace the initial two letters of 12 words following a demonstration of correct movements, in a gamified environment. A set of indicators derived from raw executions was computed and utilized to train diverse machine learning models, predicting both the grade and the risk of a handwriting delay as evaluated by a handwriting fluency test. The classification yielded an accuracy of 71% in predicting the grade and 71% in predicting the risk of handwriting delay. These results hold promise for the game's potential as a training tool, as it effectively models the maturation of children's handwriting and their proficiency.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559664","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}

引用次数: 0

Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782331

Nawfal Guefrachi, Jian Shi, Hakim Ghazzai, Ahmad Alsharoa

{"title":"Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection.","authors":"Nawfal Guefrachi, Jian Shi, Hakim Ghazzai, Ahmad Alsharoa","doi":"10.1109/EMBC53108.2024.10782331","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782331","url":null,"abstract":"The integration of Light Detection and Ranging (LiDAR) and Internet of Things (IoT) technologies offers transformative opportunities for public health informatics in urban safety and pedestrian well-being. This paper proposes a novel framework that utilizes these technologies for enhanced 3D object detection and activity classification in urban traffic scenarios. By employing elevated LiDAR, we obtain detailed 3D point cloud data, enabling precise pedestrian activity monitoring. To overcome urban data scarcity, we create a specialized dataset through simulated traffic environments in Blender, facilitating targeted model training. Our approach uses a modified Point Voxel Region-Based Convolutional Neural Network (PV-RCNN) for robust 3D detection and PointNet for classifying pedestrian activities, significantly benefiting urban traffic management and public health by offering insights into pedestrian behavior and promoting safer urban environments. Our dual-model approach not only enhances urban traffic management but also contributes significantly to public health by providing insights into pedestrian behavior and promoting safer urban environment.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559665","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}

引用次数: 0

Magnetic Resonance Elastography for Mechanical Modeling of the Human Lumbar Intervertebral Disc.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782890

Emily Triolo, Waiman Meinhold, Efe Ozkaya, Jun Ueda, Mehmet Kurt

{"title":"Magnetic Resonance Elastography for Mechanical Modeling of the Human Lumbar Intervertebral Disc.","authors":"Emily Triolo, Waiman Meinhold, Efe Ozkaya, Jun Ueda, Mehmet Kurt","doi":"10.1109/EMBC53108.2024.10782890","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782890","url":null,"abstract":"Magnetic Resonance Elastography (MRE) is a phase-contrast imaging technique that allows for determination of mechanical properties of tissue in-vivo. Due to physiological and morphological changes leading to changes in tissue mechanical properties, MRE may be a promising imaging tool for detection of intervertebral disc degeneration. We therefore performed a preliminary study to determine the frequency dependent mechanical properties of the lumbar intervertebral discs. Six healthy volunteers underwent multifrequency MRE (50, 80, and 100 Hz) to measure the mechanical properties of the intervertebral discs between the L3 and L4, and L4 and L5 vertebrae. Frequency-independent disc mechanical properties and best-fit mechanical model were determined from the frequency-dependent disc data by comparing four different linear viscoelastic material models (Maxwell, Kelvin-Voigt, Springpot, and Zener). A seventh individual with a history of a discectomy on the disc between the L4 and L5 vertebrae was also scanned to provide a preliminary analysis about how degeneration impacts disc mechanical properties. Our findings show that the Zener model may best represent the disc's frequency-dependent mechanical response. Additionally, we observed a significantly lower complex shear modulus in the degenerated disc than the healthy discs at each frequency, demonstrating the potential for MRE to detect early signs of degeneration and pinpoint the cause of chronic back pain.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559683","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}

引用次数: 0

MedDietAgent: An AI-based Mobile App for Harmonizing Individuals' Dietary Choices with the Mediterranean Diet Pattern.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781576

Fotios S Konstantakopoulos, Michail Sfakianos, Eleni I Georga, Konstantinos I Mavrokotas, Daphne N Katsarou, Konstantinos Chalatsis, Charalambos Zapadiotis, Anastasia Panousi, Sifis Plimakis, Sofia Eleftheriou, Anastasia Kanellou, Dimitrios I Fotiadis

{"title":"MedDietAgent: An AI-based Mobile App for Harmonizing Individuals' Dietary Choices with the Mediterranean Diet Pattern.","authors":"Fotios S Konstantakopoulos, Michail Sfakianos, Eleni I Georga, Konstantinos I Mavrokotas, Daphne N Katsarou, Konstantinos Chalatsis, Charalambos Zapadiotis, Anastasia Panousi, Sifis Plimakis, Sofia Eleftheriou, Anastasia Kanellou, Dimitrios I Fotiadis","doi":"10.1109/EMBC53108.2024.10781576","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10781576","url":null,"abstract":"Recently, there has been an increasing interest in applying technological advances to offer specific dietary recommendations in the field of nutrition and health. Dietary recommendation systems are advanced tools designed to assist individuals in making well-informed and health-conscious decisions on their food choices, taking into account their personal needs, preferences, and health targets or habits. In this study, we present an AI-based mobile app for harmonizing individuals' dietary choices with the pattern of the Mediterranean diet. A combination of computer vision, natural language processing, machine learning, and reinforcement techniques are used to record the nutritional information via images or speech and to generate dynamic recommendations tailored to the user's performance across key nutritional areas, encompassing calories, combined fats, proteins, carbohydrates, sugars, dietary fibers, sodium intake, fruits, vegetables, and dairy products. The image-based dietary assessment subsystem achieves a mean absolute percentage error of 3.73%, while the reinforcement learning subsystem achieves a 96% average reward. Then, a well-designed approach was taken to develop the MedDietAgent mobile app, using cutting-edge technologies and applying a simplistic approach. One of the key aspects of MedDietAgent is its ability to offer dynamic recommendations by monitoring the user's environment.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559722","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}

引用次数: 0