In silico and ex vivo approaches indicate immune pressure on capsid and non-capsid regions of coxsackie B viruses in the human system. by Rhiannon Kundu, Robin Knight, Meenakshi Dunga, Mark Peakman

“…To address this knowledge gap, we augmented conventional HLA-binding predictive algorithm-based epitope discovery by cross-referencing epitopes with sites of positive natural selection within the CBV3 viral genome, identified using mixed effects models of evolution. …”
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A Data-Driven Approach for Energy Consumption Modeling and Optimization of Welding Robot Systems by Minling Pan, Bingqi Jia, Lei Zhang, Haihong Pan, Lin Chen

“…To solve the optimization problem, an improved whale optimization algorithm (IWOA) was employed. Experimental validations with a welding robot demonstrate that the proposed method not only accurately predicted EC with a MAPE of 2.66% but also reduced the robot system’s EC by 6.72%, outperforming the traditional method focused solely on joint motor EC, which achieved a 4.08% reduction. …”
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Tumor Intrinsic Immunogenicity Suppressor SETDB1 Worsens the Prognosis of Patients with Hepatocellular Carcinoma by Chang-Qing Yin, Chun-Qing Song

“…In conclusion, the upregulation of SETDB1 is associated with a poorer prognosis in HCC patients and inversely correlates with immune cell infiltration, potentially serving as a predictive marker for immunotherapy response.…”
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Physics-based Forecasting of Tomorrow’s Solar Wind at 1 au by Igor V. Sokolov, Tamas I. Gombosi

“…We derive the modified governing equations for both hydrodynamics and magnetohydrodynamics and present a new numerical algorithm that solves the modified governing equations. …”
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Leveraging Graph Neural Networks for IoT Attack Detection by Mevlüt Uysal, Erdal Özdoğan, Onur Ceran

“…It leverages GNNs to model spatial dependencies and interactions within IoT networks and utilizes XGBoost to distill complex features for predictive analysis. The late fusion technique combines predictions from both models to enhance overall performance. …”
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Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare by Nargish Parvin, Sang Woo Joo, Jae Hak Jung, Tapas K. Mandal

“…Advanced tools like AlphaFold have significantly improved protein structure prediction, enabling the creation of biomaterials with enhanced biological compatibility. …”
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Enhancing Hazard Detection and Risk Severity Assessment in Construction through Multinomial Naive Bayes and Regression by Akaninyene Michael Akwaisua, Anietie Ekong, Godwin Ansa

“…Using the Linear Regression algorithm, the model predicts the severity of risks based on textual descriptions of a hazard.  …”
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Machine learning-based analysis on pharmaceutical compounds interaction with polymer to estimate drug solubility in formulations by Ahmad J. Obaidullah, Wael A. Mahdi

“…Hyperparameter tuning is rigorously conducted utilizing the Harmony Search (HS) algorithm. For drug solubility prediction, the ADA-DT model demonstrates superior performance, achieving an R² score of 0.9738 on the test set, with a Mean Squared Error (MSE) of 5.4270E-04 and a Mean Absolute Error (MAE) of 2.10921E-02. …”
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Graph neural network-based water contamination detection from community housing information by Raphael Anaadumba, Yigit Bozkurt, Connor Sullivan, Madhavi Pagare, Pradeep Kurup, Benyuan Liu, Mohammad Arif Ul Alam

“…Specifically, the GAT achieved an accuracy of 0.80, precision of 0.71, and recall of 0.93, outperforming XGBoost, a classical machine learning algorithm, which had an accuracy of 0.70, precision of 0.66, and recall of 0.67.Discussion: In addition to its predictive capabilities, the GAT model identifies key factors contributing to lead contamination, enabling more precise targeting of at-risk areas. …”
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Face Identification Based on K-Nearest Neighbor by Ni Kadek Ayu Wirdiani, Praba Hridayami, Ni Putu Ayu Widiari, Komang Diva Rismawan, Putu Bagus Candradinata, I Putu Deva Jayantha

“…The K-Nearest Neighbor algorithm uses neighborhood classification as the predictive value of a good instance value. …”
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Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization by Mohammed Alqarni, Ali Alqarni

“…Abstract This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. …”
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Credit Rating Model Based on Improved TabNet by Shijie Wang, Xueyong Zhang

“…Under the rapid evolution of financial technology, traditional credit risk management paradigms relying on expert experience and singular algorithmic architectures have proven inadequate in addressing complex decision-making demands arising from dynamically correlated multidimensional risk factors and heterogeneous data fusion. …”
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Artificial Intelligence for Financial Accountability and Governance in the Public Sector: Strategic Opportunities and Challenges by Ceray Aldemir, Tuğba Uçma Uysal

“…However, challenges such as algorithmic bias, data privacy issues, and the need for strong ethical guidelines still exist, and these could hinder the equitable use of AI. …”
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Machine learning approach for studying the neural underpinnings of dyslexia on a phonological awareness task by Christoforos Christoforou, Timothy C. Papadopoulos, Maria Theodorou

“…Results showed that the proposed phoneme-related neural-congruency components are predictive about the underlying neuronal differences amongst groups. …”
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Dense Crowd Dynamics and Pedestrian Trajectories: A Multiscale Field Dataset from the Festival of Lights in Lyon by Oscar Dufour, Huu-Tu Dang, Jakob Cordes, Raphael Korbmacher, Mohcine Chraibi, Benoit Gaudou, Alexandre Nicolas, Antoine Tordeux

“…Abstract The dynamics of dense crowds have received considerable attention from researchers seeking fundamental understanding or aiming to develop data-driven algorithms to predict pedestrian trajectories. However, current research mainly relies on data collected in controlled settings. …”
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Impact Analysis of BIM on Power Substation Project Costs: Techno-Economic Data Evidence from China by Ding Liu, Lizhong Qi, Yi Sun, Jingguo Rong, Su Zhang, Guangze Yu

“…To this end, this study collected total project cost data from 164 power substation projects and techno-economic statements from 34 power substation projects from SGCC to capture data evidence of the impact of BIM on project costs and explore its patterns. Algorithms such as hierarchical clustering based on improved DTW and feature selection based on QDA were designed for data mining. …”
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Impact of Airborne Radio Occultation Observations on Short Term Precipitation Forecasts of an Atmospheric River by Phuong‐Nghi Do, Jennifer S. Haase, Ivette Hernández Baños, Pawel Hordyniec, Bing Cao

“…This study evaluates the impact of assimilating ARO bending angle observations using the Model for Prediction Across Scales—Atmosphere—Joint Effort for Data assimilation Integration (MPAS‐JEDI). …”
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AI-Driven Future Farming: Achieving Climate-Smart and Sustainable Agriculture by Karishma Kumari, Ali Mirzakhani Nafchi, Salman Mirzaee, Ahmed Abdalla

“…Using sophisticated algorithms to predict soil conditions, improving agricultural yield projections, diagnosing water stress from sensor data, and identifying plant diseases and weeds through image recognition, crop mapping, and AI-guided crop selection are some of the main applications investigated. …”
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Enhancing Kalman Filter Performance: Design and Estimation with Fading-Memory Kalman Filter for Reduced Modeling Error and Improved Robustness by Jalil Manafian, Mehdi Fazli, Onur Alp Ilhan, Somaye Malmir, Sukaina Tuama Ghafel

“…The Kalman filter is a widely employed algorithm for state estimation and sensor fusion in various fields. …”
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A novel transformer-based approach for cardiovascular disease detection by Nimra Noor, Muhammad Bilal, Saadullah Farooq Abbasi, Omid Pournik, Theodoros N. Arvanitis

“…The proposed study achieved a maximum accuracy, precision, recall, and F1 score of 0.9979, 0.9959, 0.9958, and 0.9959, respectively. The proposed algorithm outperformed all the existing state-of-the-art algorithms for CVD classification.…”
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