Small-Signal Stability Analysis and Parameters Optimization of Virtual Synchronous Generator for Low-Inertia Power System by Alaa Altawallbeh, Abdulrahman Alassi, Nader Meskin, Mohammed A. Al-Hitmi, Ahmed M. Massoud

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The Evolution of Service Ecosystems Based on the Lotka–Volterra Model by Binbin Shi, Yu Li, Tingting Liang, Xixi Sun, Liquan Cui, Haonan Zhang, Yuyu Yin

“…In addition, an agent-based computational experiment is designed to integrate adversarial games for decision-making and genetic algorithms for service evolution. …”
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Modeling and validation of wearable sensor-based gait parameters in Parkinson’s disease patients with cognitive impairment by Guo Hong, Guo Hong, Fengju Mao, Fengju Mao, Mingming Zhang, Fei Zhang, Fei Zhang, Xiangcheng Wang, Kang Ren, Kang Ren, Zhonglue Chen, Zhonglue Chen, Xiaoguang Luo, Xiaoguang Luo

“…The logistic regression model demonstrated superior predictive performance (test set AUC: 0.957), outperforming other machine learning algorithms. SHAP analysis revealed that Step Length, UPDRS-III score, Duration of PD, and Peak angular velocity during steering were the most influential predictors in the logistic regression model. …”
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Interactive Mitigation of Biases in Machine Learning Models for Undergraduate Student Admissions by Kelly Van Busum, Shiaofen Fang

“…Because these issues are intrinsically subjective and context-dependent, creating trustworthy software requires human input and feedback. (1) Introduction: This work introduces an interactive method for mitigating the bias introduced by machine learning models by allowing the user to adjust bias and fairness metrics iteratively to make the model more fair in the context of undergraduate student admissions. (2) Related Work: The social implications of bias in AI systems used in education are nuanced and can affect university reputation and student retention rates motivating a need for the development of fair AI systems. (3) Methods and Dataset: Admissions data over six years from a large urban research university was used to create AI models to predict admissions decisions. …”
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Sensor-Generated In Situ Data Management for Smart Grids: Dynamic Optimization Driven by Double Deep Q-Network with Prioritized Experience Replay by Peiying Zhang, Siyi Li, Dandan Li, Qingyang Ding, Lei Shi

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Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis by Yuxing Liu, Jintao Chen, Daifeng Yang, Chenming Liu, Chunhui Tang, Shanshan Cai, Yingxuan Huang

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Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

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Urban tourism management based on artificial neural networks analysis and data mining by Xin Cui

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CHANGING STATUS OF GLOBAL COVID-19 OUTBREAK IN THE WORLD AND IN TURKEY AND CLUSTERING ANALYSIS by Elif Kartal, Mehmet Erdal Balaban, Bülent Bayraktar

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Integrated Bidding and Battery Scheduling in a Microgrid for Sealed-Bid Double Auction Power Trading With Peer Microgrids Under Uncertainty and Its Blockchain-Based Implementation by Zubin J. B., Sunitha R., Gopakumar Pathirikkat

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Estimating the potential risks of developing clusters in medical industry using various ranking criteria by Y. G. Gertsik

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Intra-Technology Enhancements for Multi-Service Multi-Priority Short-Range V2X Communication by Ihtisham Khalid, Vasilis Maglogiannis, Dries Naudts, Adnan Shahid, Ingrid Moerman

“…To bridge this gap, we propose intelligent Multi-Attribute Decision-Making algorithms for adaptive AC selection in ITS-G5 and RRI adjustment in C-V2X PC5, tailored to the varying priorities of active V2X services. …”
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Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting by Jaemyung Shin, Ryan Kang, Kinam Hyun, Zhangkang Li, Hitendra Kumar, Kangsoo Kim, Simon S. Park, Keekyoung Kim

“…In this study, a high‐throughput cellular droplet bioprinter is designed, capable of printing over 50 cellular droplets simultaneously, producing the large dataset required for effective machine learning training. Among the five algorithms evaluated, the multilayer perceptron model demonstrates the highest prediction accuracy, while the decision tree model offers the fastest computation time. …”
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Precision Medicine in Hematologic Malignancies: Evolving Concepts and Clinical Applications by Rita Khoury, Chris Raffoul, Christina Khater, Colette Hanna

“…Complementary tools such as liquid biopsy and minimal residual disease (MRD) monitoring have improved diagnosis, risk stratification, and therapeutic decision making. We discuss major molecular targets and personalized strategies across hematologic malignancies: <i>FLT3</i> and <i>IDH1/2</i> in acute myeloid leukemia (AML); Philadelphia chromosome–positive and Ph-like subtypes in acute lymphoblastic leukemia (ALL); <i>BCR-ABL1</i> in chronic myeloid leukemia (CML); <i>TP53</i> and <i>IGHV</i> mutations in chronic lymphocytic leukemia (CLL); molecular subtypes and immune targets in diffuse large B-cell lymphoma (DLBCL) and other lymphomas; and B-cell maturation antigen (BCMA) in multiple myeloma. …”
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Deep learning radiomics based on MRI for differentiating tongue cancer T - staging by Zhaoyi Lu, Bowen Zhu, Hang Ling, Xi Chen

“…ResNet18 and ResNet50 algorithms were employed to build deep learning models (deep learning radiomics (DLR) resnet18 / DLRresnet50), compared with a radiomics model (Rad) based on 17 optimized features. …”
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Detection of Malicious Office Open Documents (OOXML) Using Large Language Models: A Static Analysis Approach by Jonas Heß , Kalman Graffi

“…The extensive knowledge base and rapid analytical abilities of a large language model enable not only the assessment of extracted evidence but also the contextualisation and referencing of information to support the final decision. We demonstrate that Claude 3.5 Sonnet by Anthropic, provided with a substantial quantity of raw data, equivalent to several hundred pages, can identify individual malicious indicators within an average of five to nine seconds and generate a comprehensive static analysis report, with an average cost of USD 0.19 per request and an F1-score of 0.929.…”
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Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis by Pierre Heudel, Mashal Ahmed, Felix Renard, Arnaud Attye

“…Manifold learning and machine learning algorithms were applied to uncover complex data relationships and develop predictive models. …”
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High-precision prediction of non-resonant high-order harmonics energetic particle modes via stacking ensemble strategies by Sheng Liu, Zhenzhen Ren, Weihua Wang, Kai Zhong, Jinhong Yang, Hongwei Ning

“…The evaluation results indicate that the performance of the proposed model surpasses most supervised learning algorithms. Specifically, in comparison with the SVR and Bagging algorithms, the growth rate predictions of stacking model reduces Root mean squared error (RMSE) by 45% and 33%, mean absolute error (MAE) by 47% and 32%, and increases the R -squared coefficient ( R ^2 ) by 5% and 3%, respectively. …”
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Prognostic tools or clinical predictions: Which are better in palliative care? by P Stone, V Vickerstaff, A Kalpakidou, C Todd, J Griffiths, V Keeley, K Spencer, P Buckle, D Finlay, R Z Omar

“…Future studies should therefore assess the impact of prognostic tools on clinical practice and decision-making.…”
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The impact of virtual rheumatology care on patient outcomes and hospital admissions: an ambispective study by Ummugulsum Gazel, Tommy Han, Seyyid Bilal Acikgoz, Tara Swami, Ricardo Sabido-Sauri, Hart Goldhar, Nataliya Milman, Nancy Maltez, Catherine Ivory, Susan Humphrey-Murto, Sibel Aydin

“…Results Within 226 patients, the total number of rheumatology (median (IQR): 2 (2–3) vs. 3 [2, 3, 4], p < 0.001), emergency visits (19% vs. 29.3%, p:0.006) and hospital admissions (12.9% vs. 20.8%, p:0.015) due to any cause were increased during the pandemic, whereas there was no increased ER visit or admissions due to their rheumatological disease. …”
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