Exponential Improvements in the Simulation of Lattice Gauge Theories Using Near-Optimal Techniques by Mason L. Rhodes, Michael Kreshchuk, Shivesh Pathak

“…Our work demonstrates that the use of advanced algorithmic techniques leads to dramatic reductions in the cost of simulating fundamental interactions, bringing it in step with resources required for first-principles quantum simulation of chemistry.…”
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Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency by Bruce Pyenson, Maggie Alston, Jeffrey Gomberg, Feng Han, Nikhil Khandelwal, Motoharu Dei, Monica Son, Jaime Vora

“… # Methods A machine learning algorithm was developed in Scikit-learn, a Python module. …”
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Explainable Artificial Intelligence (XAI) for Flood Susceptibility Assessment in Seoul: Leveraging Evolutionary and Bayesian AutoML Optimization by Kounghoon Nam, Youngkyu Lee, Sungsu Lee, Sungyoon Kim, Shuai Zhang

“…The proposed framework, which integrates TPOT, Optuna, and SHAP within an XAI pipeline, not only improves predictive capability but also offers transparent insights into feature behavior and model logic. …”
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Toward a conceptual model to improve the user experience of a sustainable and secure intelligent transport system by Abdullah Alsaleh

“…A novel search and management protocol, supported by a tailored algorithm, was developed to enhance resource allocation success rates for vehicles within a defined area of interest. …”
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The doctor and patient of tomorrow: exploring the intersection of artificial intelligence, preventive medicine, and ethical challenges in future healthcare by Paulo Santos, Paulo Santos, Isabel Nazaré, Isabel Nazaré

“…AI-driven technologies, including real-time health monitoring and predictive analytics, offer new personalized preventive care possibilities. …”
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Modelling the future of cleaner energy: Explainable artificial intelligence model for green hydrogen production rate estimation by Okorie Ekwe Agwu, Saad Alatefi, Ahmad Alkouh

“…However, the complex dynamics associated with the process requires predictive tools for large-scale deployment. Despite advancements in machine learning-based models, previous studies often lack explainability, diminishing user trust in their deployment. …”
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Magnetic resonance imaging (MRI) radiomics in paediatric neuro-oncology: A systematic review of clinical applications, feature interpretation, and biological insights in the ch... by Teesta Mukherjee, Omid Pournik, Theodoros N Arvanitis

“…SVM was the most used classification algorithm ( n  = 9), with an AUC range of 0.858–0.977. …”
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Quantitative structure-properties relationship, molecular dynamic simulations and designs of some novel lubricant additives by Usman Abdulfatai, Adamu Uzairu, Sani Uba, Gideon Adamu Shallangwa

“…Quantitative structure-properties relationship (QSPR) method was used to design some novel antioxidant lubricant additives, while molecular dynamics simulations were used to calculate their dynamic binding energies on steel and to hydrogen-containing DLC (a-C: H) crystal surfaces. 29 synthesized antioxidant lubricant additives were collected from literature and geometrically optimized by Spartan’14 version 1.1.2 software while Genetic Function Algorithm (GFA) method of the material studio version 8.0 software was used to build the predictive QSPR model. …”
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Identification of priority conservation areas and potential corridors for jaguars in the Caatinga biome, Brazil. by Ronaldo Gonçalves Morato, Katia Maria Paschoaletto Micchi de Barros Ferraz, Rogério Cunha de Paula, Cláudia Bueno de Campos

“…A total of 62 points records of jaguar occurrence and 10 potential predictors were analyzed in a GIS environment. A predictive distributional map was obtained using Species Distribution Modeling (SDM) as performed by the Maximum Entropy (Maxent) algorithm. …”
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Revealing High‐Temporal‐Resolution Flood Evolution With Low Latency Using GRACE Follow‐On Ranging Data by Hao‐si Li, Shuang Yi, Zi‐ren Luo, Peng Xu

“…We find that the potent manipulation of water resources by human activities might impact the predictive capabilities of these models, thereby misrepresenting the hydrological evolution during the flood event. …”
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CT-based radiomics features for the differential diagnosis of nodular goiter and papillary thyroid carcinoma: an analysis employing propensity score matching by Haiming Zhang, Zhenyu Li, Fengtao Zhang, Hengguo Li

“…Feature selection was carried out utilizing the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to generate the radiomics score (Rad-score). …”
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Integrating explainable artificial intelligence and light gradient boosting machine for glioma grading by Teuku Rizky Noviandy, Ghalieb Mutig Idroes, Irsan Hardi

“…The application of XAI techniques, particularly the use of Shapley Additive Explanation (SHAP) values, revealed the IDH1 gene mutation as a significant predictive factor in glioma grading, alongside providing valuable insights into the model's decision-making process. …”
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Development of a Short-Range Multispectral Camera Calibration Method for Geometric Image Correction and Health Assessment of Baby Crops in Greenhouses by Sabina Laveglia, Giuseppe Altieri, Francesco Genovese, Attilio Matera, Luciano Scarano, Giovanni Carlo Di Renzo

“…Despite the results being not yet sufficiently robust for a fully predictive model of chlorophyll content in plants, the analysis of vegetation indices demonstrated a clear distinction between healthy and unhealthy plants.…”
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Experimental assessment of communication delay's impact on connected automated vehicle speed volatility and energy consumption by Wan Li, Jackeline Rios-Torres, Boyu Wang, Zulqarnain H. Khattak

“…To fill the research gap, this study leverages the facilities at America Center of Mobility (ACM) Smart City Test Center to implement and evaluate a CAV merging control algorithm through vehicle-in-the-loop testing. This study aims at achieving three main objectives: (1) develop and implement a CAV merging control strategy in the experimental test bed through vehicle-in-the-loop testing, (2) propose analytical models to quantify the impacts of communication delay on the variability of CAV speed and energy consumption based on field experiment data, and (3) create a predictive model for energy usage considering various CAV attributes and dynamics, e.g., speed, acceleration, yaw rate, and communication delays. …”
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A Study of Potential Applications of Student Emotion Recognition in Primary and Secondary Classrooms by Yimei Huang, Wei Deng, Taojie Xu

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An Automated Image-Based Dietary Assessment System for Mediterranean Foods by Fotios S. Konstantakopoulos, Eleni I. Georga, Dimitrios I. Fotiadis

“…<italic>Results:</italic> The classification accuracy where true class matches with the most probable class predicted by the model (Top-1 accuracy) is 83.8&#x0025;, while the accuracy where true class matches with any one of the 5 most probable classes predicted by the model (Top-5 accuracy) is 97.6&#x0025;, for the food classification subsystem. …”
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TMSB4X is a regulator of inflammation-associated ferroptosis, and promotes the proliferation, migration and invasion of hepatocellular carcinoma cells by Linlin Tang, Yangli Jin, Jinxu Wang, Xiuyan Lu, Mengque Xu, Mingwei Xiang

“…Results 157 genes related to inflammation and ferroptosis in HCC were obtained by WGCNA. rLasso algorithm, with the highest C-index, screened out 29 hub genes, and this model showed good efficacy to predict the prognosis of HCC patients. …”
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Exploring the ceRNA network involving AGAP2-AS1 as a novel biomarker for preeclampsia by Fan Lu, Ni Zeng, Xiang Xiao, Xingxing Wang, Han Gong, Houkang Lei

“…The function of 5 hub genes was analyzed and the interaction between drugs and hub genes was predicted. A total of 5 small molecule drugs were predicted, namely benzbromarone, 9,10-phenanthrenequinone, chembl312032, insulin and aldesleukin. …”
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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…The National Health and Nutrition Examination Survey (NHANES) 2012–2018 data comprising audiometric tests and cardiovascular risk factors was utilized. Machine learning algorithms were trained to classify hearing impairment thresholds and predict pure tone average values. …”
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Optimization method improvement for nonlinear constrained single objective system without mathematical models by HOU Gong-yu, XU Zhe-dong, LIU Xin, NIU Xiao-tong, WANG Qing-le

“…Then, the immune particle swarm algorithm, which incorporates the self-regulatory mechanism of the immune system, is used to optimize the system based on the predicted model. …”
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