Decoding the diagnostic biomarkers of mitochondrial dysfunction related gene variants in pediatric T cell acute lymphoblastic leukemia by Yunfeng Lu, Xi Yang, Liying Liu, Xiaojun Sun, Xiuli Li, Ping Zhao, Guotao Guan, Yunpeng Dai

“…The intersection of LASSO regression and RF algorithms highlighted two pivotal genes: RNLS and ULK1. …”
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Machine learning-based prediction of scale formation in produced water as a tool for environmental monitoring by Arash Tayyebi, Ali Alshami, Erfan Tayyebi, Ademola Owoade, MusabbirJahan Talukder, Nadhem Ismail, Zeinab Rabiei, Xue Yu, Glavic Tikeri

“…We used a database comprised of 2313 quality data points from different locations in the Bakken Shale Play, including values such as ionic compositions, pH, and the saturation index of the potential mineral scales in PW at 60°F and 60 psi to train the ML algorithms and identify what scale will likely form in the PW. …”
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Conceptual design of a decision knowledge service model integrating a multi-agent supply relationship diagram for electric power emergency equipment by Jiandong Si, Chang Liu, Jingxian Ye, Jianfeng Wu, Jianguo Wang, Kairui Hu, Chunhua Ju, Qianwen Cao

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SPA-Net: An Offset-Free Proposal Network for Individual Tree Segmentation from TLS Data by Yunjie Zhu, Zhihao Wang, Qiaolin Ye, Lifeng Pang, Qian Wang, Xiaolong Zheng, Chunhua Hu

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A Reinforcement Learning Approach to Personalized Asthma Exacerbation Prediction Using Proximal Policy Optimization by Dahiru Adamu Aliyu, Emelia Akashah Patah Akhir, Maryam Omar Abdullah Sawad, Jameel Shehu Yalli, Yahaya Saidu

“…The model achieved 96.60% accuracy, 95.79% precision, 96.65% recall, and 95.92% F1-score, outperforming baseline RL algorithms such as Deep Q-Learning (92.21% accuracy), Advantage Actor-Critic (94.34% accuracy), and Trust Region Policy Optimization (95.12% accuracy). …”
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MFFCI–YOLOv8: A Lightweight Remote Sensing Object Detection Network Based on Multiscale Features Fusion and Context Information by Sheng Xu, Lin Song, Junru Yin, Qiqiang Chen, Tianming Zhan, Wei Huang

“…Most current researches primarily focus on improving experimental accuracy using large models, often neglecting the deployment challenges. …”
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Data driven models for predicting pH of CO2 in aqueous solutions: Implications for CO2 sequestration by Mohammad Rasool Dehghani, Moein Kafi, Hamed Nikravesh, Maryam Aghel, Erfan Mohammadian, Yousef Kazemzadeh, Reza Azin

“…However, previous studies have not comprehensively investigated the development of machine learning models to estimate this parameter. To fill this research gap, this study developed 15 models comprising five machine learning methods: regression trees, support vector regression, Gaussian process regression, bagged trees, and boosted trees, and three optimization algorithms: random search, grid search, and Bayesian optimization. …”
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Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving MXene-based ionanofluids applicable in PV/T sol... by Tao Hai, Ali Basem, As’ad Alizadeh, Kamal Sharma, Dheyaa J. jasim, Husam Rajab, Abdelkader Mabrouk, Lioua Kolsi, Wajdi Rajhi, Hamid Maleki, Narinderjit Singh Sawaran Singh

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Double‐row suture‐bridge technique does not yield better clinical and radiological results than single‐row technique in patients older than 55 years at 2 years minimum follow‐up: A... by Vito Gaetano Rinaldi, Sassoli Iacopo, Federico Coliva, Antongiulio Favero, Alberto Bazzocchi, Marco Miceli, Stefano Di Paolo, Stefano Zaffagnini, Giulio Maria Marcheggiani Muccioli

“…While limitations exist, our findings contribute to understanding optimal surgical approaches, emphasizing individualized treatment based on patient characteristics and surgeon expertise. Further research, including randomized controlled trials with long‐term follow‐up, is needed to refine treatment algorithms and improve patient outcomes in rotator cuff surgery. …”
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The potential & limitations of monoplotting in cross-view geo-localization conditions by Bradley J. Koskowich, Michael J. Starek, Scott A. King

“…Traditional photogrammetry methods like structure-from-motion (SfM) or simultaneous localization and mapping (SLAM) can technically accomplish CVGL, but require a sufficiently dense collection of camera views in order to recover camera pose. This research proposes an alternative CVGL solution, a series of algorithmic operations which can completely automate the calculation of target camera pose via a less common photogrammetry method known as monoplotting, also called single camera resectioning. …”
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Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases by Yuji Miyamoto, Takeshi Nakaura, Mayuko Ohuchi, Katsuhiro Ogawa, Rikako Kato, Yuto Maeda, Kojiro Eto, Masaaki Iwatsuki, Yoshifumi Baba, Toshinori Hirai, Hideo Baba

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Using composable NFTs and blockchain for the creation of EV battery digital passports with sustainability and traceability features by Haya R. Hasan, Khaled Salah, Ahmad Mayyas, Ahmad Musamih, Ibrar Yaqoob, Mohammed Omar, Raja Jayaraman

“…We present a system architecture, smart contracts, and supporting algorithms are presented, with testing and validation in a simulated environment. …”
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Cognitive weaknesses or impairments on the NIH toolbox cognition battery in children and adolescents: base rates in a normative sample and proposed methods for classification by Nathan E. Cook, Nathan E. Cook, Nathan E. Cook, Grant L. Iverson, Grant L. Iverson, Grant L. Iverson, Grant L. Iverson, Justin E. Karr

“…We propose flexible, psychometrically derived criteria for identifying a cognitive weakness or impairment.DiscussionReferencing the base rates of low scores will help researchers and clinicians enhance the interpretation of NIHTB-CB performance among children with cognitive weakness or impairments that are neurodevelopmental or acquired.…”
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Applications of molecular dynamics in nanomaterial design and characterization - A review by Md. Aminul Islam, S M Maksudur Rahman, Juhi Jannat Mim, Safiullah Khan, Fardin Khan, Md. Ahadul Islam Patwary, Nayem Hossain

“…Molecular dynamics (MD) simulations have become game changers in nanomaterial research, providing detailed understanding of how materials behave, are designed, or characterized at the atomic scale. …”
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River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model by Khabat Khosravi, Salim Heddam, Changhyun Jun, Sayed M. Bateni, Dongkyun Kim, Essam Heggy

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Advancing road maintenance with EfficientDet-based pothole monitoring by Jenefa Archpaul, Taurshia Antony, Kuriakose Bessy Mani, Kumar Edward Naveen Vijaya, Lincy Archpaul

“…Addressing these limitations, our research leverages the EfficientDet architecture, known for its optimal balance of accuracy and computational efficiency, to enhance the detection and monitoring of potholes. …”
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Multimodal data integration with machine learning for predicting PARP inhibitor efficacy and prognosis in ovarian cancer by Xi’an Xiong, Li Cai, Li Cai, Zhen Yang, Zhongping Cao, Nayiyuan Wu, Nayiyuan Wu, Qianxi Ni

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SA-MARL: Novel Self-Attention-Based Multi-Agent Reinforcement Learning With Stochastic Gradient Descent by Rabbiya Younas, Hafiz Muhammad Raza Ur Rehman, Ingyu Lee, Byung-Won On, Sungwon Yi, Gyu Sang Choi

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GastroEndoNet: Comprehensive endoscopy image dataset for GERD and polyp detectionMendeley Data by Abu Kowshir Bitto, Md. Hasan Imam Bijoy, Kamrul Hassan Shakil, Aka Das, Khalid Been Badruzzaman Biplob, Imran Mahmud, Syed Md. Minhaz Hossain

“…This dataset is designed to facilitate research in automated detection and classification of these conditions through machine learning algorithms. …”
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Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging by Hakan Ayyıldız, Okan İnce, Esin Korkut, Merve Gülbiz Dağoğlu Kartal, Atadan Tunacı, Şükrü Mehmet Ertürk

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