A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti, Hafsa Binte Kibria, Zareen Tasnim Pear, Md Nahiduzzaman, Md. Faysal Ahamed, Khandaker Reajul Islam, Jaya Kumar, Muhammad E. H. Chowdhury

“…Three different publicly available datasets have been used based on the age group to create the best predicting model for each case. After handling missing values, balancing the dataset, and analyzing the classifier’s performance, it is found that tree-based algorithms, particularly RF, perform better for all the datasets. …”
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Causes of embryo implantation failure: A systematic review and metaanalysis of procedures to increase embryo implantation potential by Francesco M. Bulletti, Romualdo Sciorio, Alessandro Conforti, Roberto De Luca, Carlo Bulletti, Antonio Palagiano, Marco Berrettini, Giulia Scaravelli, Roger A. Pierson

“…Subsequent studies ought to concentrate on modulating endometrial responses immunologically and developing algorithms to improve the precision of predicting implantation success; as well as the timing of endometrial receptivity and the occurrence of dormant embryo phenomena also warrants further investigation.…”
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Bioinformatics analysis of comorbid mechanisms between ischemic stroke and end stage renal disease by Shuhong Wang, Zhongda Li, Xiao Wang, Jiexue Zhou, Shandong Meng, Jinyang Zhuang, Yan Zhou, Qin Zhao, Chunli Zhu, Yusheng Zhang, Sheng Shen

“…Transcriptional regulatory networks were predicted using RcisTarget and miRcode. Key gene expressions were validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in clinical samples. …”
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Enhancing ovarian cancer prognosis with an artificial intelligence-derived model: Multi-omics integration and therapeutic implications by You Wu, Kunyu Wang, Yan Song, Bin Li

“…Results: The AIDPI model demonstrated superior accuracy in predicting ovarian cancer prognosis compared to existing models. …”
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Magnetic resonance imaging of the liver and spleen in the diagnosis of storage diseases by Z. M. Shapieva, O. V. Kucheruk, V. E. Sinitsyn, E. A. Mershina

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Social Media Suicide Watch by Katherine Prothro

“…Ethical Analysis The strongest argument in favor of apps like Samaritan’s Radar is their potential to predict individuals at risk for suicide and self-harm accurately. …”
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Deep Learning Based Large‐Area Contact Sensing for Safe Human–Robot Interaction Using Conformal Kirigami Structure‐Enabled Robotic E‐Skin by Rui Jiao, Zhengjun Wang, Ruoqin Wang, Qian Xu, Jiacheng Jiang, Boyang Zhang, Simin Yang, Yang Li, Yik Kin Cheung, Fan Shi, Hongyu Yu

“…Additionally, a convolutional neural network‐based deep learning algorithm is implemented to decode the features of guided wave signals and predict the contact location and energy intensity on the robot surface. …”
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The Forecasting Yield of Highland Barley and Wheat by Combining a Crop Model with Different Weather Fusion Methods in the Study of the Northeastern Tibetan Plateau by Peng Li, Liang He, Xuetong Wang, Mengfan Zhao, Fan Li, Ning Jin, Ning Yao, Chao Chen, Qi Tian, Bin Chen, Gang Zhao, Qiang Yu

“…The weather data fusion strategy for yield forecasts offered reliable prediction accuracy without the need for full-cycle weather observation.…”
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Temperature Compensation Method for MEMS Ring Gyroscope Based on PSO-TVFEMD-SE-TFPF and FTTA-LSTM by Hongqiao Huang, Wen Ye, Li Liu, Wenjing Wang, Yan Wang, Huiliang Cao

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Biomechanical Modeling of Selected Methods of Load Carriage to Improve Military Capabilities of Troops by Mostafa Hajlotfalian, Alireza Redaei, Heydar Sadeghi

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An Adaptive Opportunistic Network Coding Mechanism in Wireless Multimedia Sensor Networks by Hang Shen, Guangwei Bai, Lu Zhao, Zhenmin Tang

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Capacity Estimation of Lithium-Ion Battery Systems in Fuel Cell Ships Based on Deep Learning Model by Xiangguo Yang, Jia Tang, Qijia Song, Yifan Liu, Lin Liu, Xingwei Zhou, Yuelin Chen, Telu Tang

“…To address the challenge of accurately estimating lithium-ion battery capacity under complex operating conditions, this study extracts universal health factors from battery data under varied charging and discharging scenarios and combines these with a deep learning model to enhance prediction accuracy. First, battery data from three complex conditions are analyzed, extracting partial charge and discharge data. …”
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Discrepancy in Metabolic Dysfunction–Associated Steatotic Liver Disease Prevalence in a Large Northern California Cohort by Luis A. Rodriguez, Lue-Yen S. Tucker, Varun Saxena, Theodore R. Levin

“…Annual MASLD prevalence was identified based on International Classification of Diseases, Ninth or Tenth Revision, Clinical Modification diagnosis codes, the application of natural language processing of all radiology imaging report text that included the liver, and the application of the Dallas Steatosis Index, a MASLD prediction algorithm. Results: Between 2009 and 2018, the estimated MASLD prevalence ranged from 0.37% to 0.95% using diagnosis codes, 0.88%–1.37% using imaging, and 6.14%–11.27% using the Dallas Steatosis Index. …”
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Hydrodynamic optimisation of a horizontal-axis hydrokinetic turbine for the River Ganga using a semi-analytical approach by Kripal, Anoop Kanjirakat

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Energy-Balanced Data Gathering and Aggregating in WSNs: A Compressed Sensing Scheme by Xiaofei Xing, Dongqing Xie, Guojun Wang

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Computational Thinking in Elementary School Students: A Bibliometric Review by Swasti Maharani, Irna Tri Yuniahastuti, Ihtiari Prastyaningrum, Vera Dewi Susanti, Hendrisa Rizqie Romandoni

“…By encouraging CT in primary education, students better understand technology and versatile skills that can be applied in various fields. …”
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A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization by Lizhi Cui, Zhihao Ling, Josiah Poon, Simon K. Poon, Junbin Gao, Paul Kwan

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YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images by Ke Zhang, Ningxuan Zhang, Chaojun Shi, Qiaochu Lu, Xian Zheng, Yujie Cao, Xiaoyun Zhang, Jiyuan Yang

“…On the Satellite Remote Sensing Power Tower Dataset (SRSPTD), the YOLOv9-GDV algorithm achieves an mAP of 80.2%, representing a 4.7% improvement over the baseline algorithm. …”
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Expression Characteristics and Prognostic Value of KLRG2 in Endometrial Cancer: A Comprehensive Analysis Based on Multi-Omics Data by Xiaoyan Huang, Ailian Li, Dianbo Xu

“…High KLRG2 expression independently predicted worse overall survival (HR = 3.08, 95% CI = 1.92–4.96) and progression-free interval (HR = 1.98, 95% CI = 1.37–2.87). …”
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Impacts of Spatial Expansion of Urban and Rural Construction on Typhoon-Directed Economic Losses: Should Land Use Data Be Included in the Assessment? by Siyi Zhou, Zikai Zhao, Jiayue Hu, Fengbao Liu, Kunyuan Zheng

“…Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. This reveals that implicitly classifying typhoon disaster loss types through prototype learning can significantly improve assessment accuracy in data-scarce scenarios. (2) By designing a dual-path uncertainty quantification mechanism, we realized high-reliability risk assessment, with 95.45% of actual loss values falling within predicted confidence intervals (theoretical expectation: 95%). …”
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