Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool by Hatem Ali, Arun Shroff, Tibor Fülöp, Miklos Z. Molnar, Adnan Sharif, Bernard Burke, Sunil Shroff, David Briggs, Nithya Krishnan

“…We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK’s transplant selection process.Methodology: Pre-transplant characteristics from 12,661 live-donor kidney transplants (performed between 2007 and 2022) from the United Kingdom Transplant Registry database were analyzed. …”
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A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging by Aram Lee, J. J. Kavelaars, Hossen Teimoorinia, Wesley Fraser, Edward Ashton

“…We demonstrate that deep learning object detection algorithms can aid in TNO and SSO discovery, supporting future discovery pipeline development.…”
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D-SPDH: Improving 3D Robot Pose Estimation in Sim2Real Scenario via Depth Data by Alessandro Simoni, Guido Borghi, Lorenzo Garattoni, Gianpiero Francesca, Roberto Vezzani

“…., the system is trained only with synthetic data and then tested on real sequences, thus eliminating the time-consuming acquisition and annotation procedures of real data, common phases in deep learning algorithms. Moreover, we introduce SimBa++, a dataset featuring both synthetic and real sequences with new real-world double-arm movements, and that represents a challenging setting in which the proposed approach is tested. …”
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Addition of Social Determinants of Health to Coronary Heart Disease Risk Prediction: The Multi‐Ethnic Study of Atherosclerosis by Brittany Saldivar Murphy, Yunbi Nam, Robyn L. McClelland, Isaac Acquah, Miguel Cainzos‐Achirica, Khurram Nasir, Wendy S. Post, Melinda C. Aldrich, Andrew P. DeFilippis

“…Our findings do not support the incorporation of SDoH into current risk prediction algorithms.…”
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Next-Gen UAV-Satellite Communications: AI Innovations and Future Prospects by Sherief Hashima, Ahmad Gendia, Kohei Hatano, Osamu Muta, Mostafa S. Nada, Ehab Mahmoud Mohamed

“…Furthermore, it includes a case study demonstrating the effectiveness of multi-armed bandit (MAB) algorithms in optimizing resource allocation and decision-making processes for UAV-low Earth orbit (LEO) satellite communication scenarios, showcasing significant improvements in network performance. …”
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Enhancing student reflections with natural language processing based scaffolding: A quasi-experimental study in a large lecture course by Muhsin Menekse, Alfa Satya Putra, Jiwon Kim, Ahmed Ashraf Butt, Mark A. McDaniel, Ido Davidesco, Michelle Cadieux, Joe Kim, Diane Litman

“…The app uses Natural Language Processing (NLP) algorithms to evaluate students' reflection quality and specificity using a 4-point scale, with 1 indicating shallow reflection and 4 indicating highly relevant or specific reflection. …”
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Evaluation of different in silico tools for the assessment of deleterious variants in acute myeloid leukemia by Wardah Qureshi, Muhammad Irfan, Ishtiaq Ahmad Khan, Muhammad Shakeel

“…The widely accepted practice of using in silico pathogenicity predictions as evidence when interpreting genetic variants is an integral part of standard variant classification guidelines. Several algorithms have been developed and evaluated to predict deleterious variants. …”
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An Efficient Method for Counting Large-Scale Plantings of Transplanted Crops in UAV Remote Sensing Images by Huihua Wang, Yuhang Zhang, Zhengfang Li, Mofei Li, Haiwen Wu, Youdong Jia, Jiankun Yang, Shun Bi

“…Additionally, some deep-learning-based algorithms can only crop large-scale remote sensing images obtained by Unmanned Aerial Vehicles (UAVs) into smaller sub-images for counting. …”
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Association between serum hypertriglyceridemia and hematological indices: data mining approaches by Somayeh Ghiasi Hafezi, Amin Mansoori, Alireza Kooshki, Marzieh Hosseini, Sahar Ghoflchi, Mark Ghamsary, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…Machine learning methodologies, specifically logistic regression, decision tree, and random forest algorithms, were utilized for data analysis in the investigation of individuals with normal and high TG levels. …”
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Advances in Sorghum Improvement for Climate Resilience in the Global Arid and Semi-Arid Tropics: A Review by Andekelile Mwamahonje, Zamu Mdindikasi, Devotha Mchau, Emmanuel Mwenda, Daines Sanga, Ana Luísa Garcia-Oliveira, Chris O. Ojiewo

“…In addition, recent advancements including new machine learning algorithms, gene editing, genomic selection, rapid generation advancement, and recycling of elite material, along with high-throughput phenotyping tools such as drone- and satellite-based images and other speed-breeding techniques, have increased the precision, speed, and accuracy of new crop variety development. …”
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Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling by Yayang Feng, Guoshuai Wang, Jun Wang, Hexiang Zheng, Xiangyang Miao, Xiulu Sun, Peng Li, Yan Li, Yanhui Jia

“…Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices.…”
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Coalition Game-Based Reinforcement Learning in Underwater Acoustic Networks by Khouloud Gharsalli, Sameh Najeh, Thierry Le Pors, Leila Najjar, Pierre-Jean Bouvet, Hichem Besbes

“…Simulation results demonstrate that the U-CoCRL method outperforms baseline algorithms across all interference levels. It achieves up to 18.1% higher global utility and 33.9% better performance indices under high interference, 13.6% and 33.9% improvements under medium interference, and 6.1% and 27.7% gains under low interference, respectively. …”
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Blockchain Technology in Supply Chain Management Prospects and Challenges for Implementation by Peshattiwar Atish, P Mohanraj, A Anand Gerald, S Dharmalingam, Tiwari Mohit, G Swarnalakshmi

“…In response, this research presents a next-gen blockchain framework leveraging hybrid blockchain architectures, AI-integrated smart contracts, green consensus algorithms, and cross-platform interoperability strategies to bridge the gaps of the existing systems. …”
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Health economics evaluation of diagnostic strategies for gastro-oesophageal reflux disease with reflux symptoms in China: a modelling study by Yang Chen, Feng Xie, Hongda Chen, Dong Wu, Xiaoqing Li, Xiaxiao Yan, Meiduo Ouzhu, Ziqi Guo, Chengzhen Lyu, Daiyu Yang

“…Objectives American College of Gastroenterology (ACG) and Chinese expert consensus recommended different algorithmic approaches for the diagnosis of gastro-oesophageal reflux disease (GERD) are not yet defined. …”
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Analysis of Regions of Homozygosity: Revisited Through New Bioinformatic Approaches by Susana Valente, Mariana Ribeiro, Jennifer Schnur, Filipe Alves, Nuno Moniz, Dominik Seelow, João Parente Freixo, Paulo Filipe Silva, Jorge Oliveira

“…<b>Methods</b>: To streamline personalized multigene panel creation, using WES and ROHs, we developed a methodology integrating ROHMMCLI and HomozygosityMapper algorithms, and, optionally, Human Phenotype Ontology (HPO) terms, implemented in a Django Web application. …”
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Anti-masking Posts on Instagram: Content Analysis During the COVID-19 Pandemic by Emma K. Quinn, Robert T. Duffy, Kristian Larsen, Maria Dalton, Cheryl E. Peters

“…Conclusion: Science denial and propaganda shared among Instagram users may represent an onramp to consumption of broader conspiracy theories and government distrust, in addition to having negative health effects and social consequences for workers regardless of whether they wear masks. Social media algorithms promote similar misinformation or authority-questioning beliefs to users who view related content. …”
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Enhancement of GPU-accelerated smoothed particle hydrodynamics (SPH) method with dynamic parallelism by Liwen Xue, Shenglong Gu, Songdong Shao

“…Besides, the RMS errors between SPH simulations and experimental data were found in the range of 0.03 - 0.12 m/s for the dam break flow and 0.004 - 0.008 m/s for the water entry, respectively. Under same SPH algorithms, adopting the CDP architecture can achieve higher computational efficiency than the traditional CUDA technique. …”
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Population Cohort-Validated PM_2.5-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure Prediction by Yu-Chung Wei, Wen-Chi Cheng, Pinpin Lin, Zhi-Yao Zhang, Chi-Hsien Chen, Chih-Da Wu, Yue Leon Guo, Hung-Jung Wang

“…Logistic regression and decision tree algorithms were then utilized to construct predictive models for PM_2.5 exposure based on these gene expression profiles. …”
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A Local Dwarf Galaxy Search Using Machine Learning by Huanian Zhang, Guangping Ye, Rongyu Wu, Dennis Zaritsky

“…We introduce the spectrally confirmed training sample, discuss evaluation metrics, investigate features, compare different machine learning algorithms, and find that a seven-class neural network classification model is highly effective in separating the signal (local, low-mass galaxies) from various contaminants, reaching a precision of 95% and a recall of 76%. …”
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Dual Control Strategy for Non-Minimum Phase Behavior Mitigation in DC-DC Boost Converters Using Finite Control Set Model Predictive Control and Proportional–Integral Controllers by Alejandra Marmol, Elyas Zamiri, Marziye Purraji, Duberney Murillo, Jairo Tuñón Díaz, Aitor Vazquez, Angel de Castro

“…Model Predictive Control (MPC) has emerged as a promising alternative for controlling power converters, offering benefits such as flexibility, simplicity, and rapid control response, particularly when short-horizon algorithms are employed. This paper introduces a system using a short-horizon Finite Control Set MPC (FCS-MPC) strategy to specifically address the challenge of non-minimum phase behavior in boost converters. …”
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