Thermal performance enhancement in a solar air heater fitted with flapped V-baffles: Numerical study by Chinnapat Turakarn, Pitak Promthaisong, Teerapat Chompookham

“…The finite volume method using the SIMPLE algorithm was used to resolve the problem. The goal was to enhance the thermal performance (in terms of the thermal enhancement factor, TEF) by increasing the heat transfer together with reducing the pressure loss, compared with a conventional V-baffles (CVB). …”
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Assessing the Impact of Land Use Changes on Ecosystem Service Values in Coal Mining Regions Using Google Earth Engine Classification by Shi Chen, Jiwei Qin, Shuning Dong, Yixi Liu, Pingping Sun, Dongze Yao, Xiaoyan Song, Congcong Li

“…Land use change dynamics were quantified via a transfer matrix, while changes in the ESV were evaluated using the ecosystem sensitivity index and ecological contribution rate. …”
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Machine Learning Models Decoding the Association Between Urinary Stone Diseases and Metabolic Urinary Profiles by Lin Ma, Yi Qiao, Runqiu Wang, Hualin Chen, Guanghua Liu, He Xiao, Ran Dai

“…Our analyses revealed that the Random Forest algorithm exhibited the highest predictive accuracy, with AUC values of 0.809 for kidney stones, 0.99 for ureter stones, and 0.775 for multiple location stones. …”
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Machine learning-based radiomics for differentiating lung cancer subtypes in brain metastases using CE-T1WI by Xueming Xia, Wei Du, Qiheng Gou

“…Among them, 173 individuals had NSCLC with 228 BMs, while 87 patients were diagnosed with SCLC with 142 BMs. …”
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Preclinical markers of carotid atherosclerosis and cardiovascular risk assessed by the ESH/ESC scale (2003, 2007, 2009) by S. Sh. Urazalina, A. N. Rogoza, T. V. Balakhonova, R. P. Myasnikov, T. E. Kolmakova, Yu. A. Karpov, V. V. Kukharchuk, S. A. Boytsov

“…In the “low-risk” and “intermediate-risk” groups, most patients had normal blood pressure levels (72,8 % and 83,5 %, respectively), while most patients in the “high-risk” group had arterial hypertension (56,7 %). …”
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The value of triglyceride–glucose index–related indices in evaluating migraine: perspectives from multi–centre cross–sectional studies and machine learning models by Zixuan Yan, Lincheng Duan, Hong Yin, Muchen Wang, Jingwen Li, Chenghua Li, Xiao Wang, Dingjun Cai, Fanrong Liang, Wenchuan Qi

“…TyG–BMI indicated a strong positive association beyond the threshold of 206, while TyG–WHtR demonstrated a significant positive link below the cutoff of 7.4. …”
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DeepD&Cchl: an AI tool for automated 3D single-cell chloroplast detection, counting, and cell type clustering by Qun Su, Le Liu, Zhengsheng Hu, Tao Wang, Huaying Wang, Huaying Wang, Qiuqi Guo, Xinyi Liao, Yan Sha, Feng Li, Zhao Dong, Zhao Dong, Shaokai Yang, Shaokai Yang, Ningjing Liu, Qiong Zhao, Qiong Zhao, Qiong Zhao

“…It utilizes You-Only-Look-Once (YOLO), a real-time detection algorithm, for accurate and efficient performance. …”
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Soil moisture dominates gross primary productivity variation during severe droughts in Central Asia by Tao Yu, Guli Jiapaer, Anming Bao, Ye Yuan, Jiayu Bao, Tim Van de Voorde

“…RF predictions also indicated that during severe drought, SM had a significant effect on GPP, while Srad, Tem, and VPD had a slight effect on GPP.…”
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Forecasting Insurance Company Commitments with Long Short-Term Memory Models by Negar Tehraniyazdi, Reza Vaezi, Saeed Setayeshi, Iman Raeesi Vanani

“…A Long Short-Term Memory (LSTM) neural network algorithm is employed, recognized for its strong predictive capability in time series data. …”
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Human-in-the-Loop Optimization of the Stiffness and Alignment of a Prosthetic Foot to Reduce the Metabolic Cost of Walking by Thijs Tankink, Han Houdijk, Johnnidel Tabucol, Marco Leopaldi, Juha M. Hijmans, Raffaella Carloni

“…This study aims to explore the potential of human-in-the-loop optimization to individually optimize prosthetic foot stiffness and alignment to decrease the metabolic cost of walking of transtibial amputees. 10 transtibial amputees underwent an optimization protocol while walking on a treadmill with an experimental prosthetic foot with tuneable stiffness and alignment. …”
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Comparison of Radiomics and conventional SUVr methods for Alzheimer’s disease classification using AV45 PET imaging by Haiyan Gao, Arui Tan, Junhao Wu, Zhen Cao, Ziyang Zhu, Wei Zhang

“…Feature importance was ranked using a random forest algorithm, and three models were constructed: an SUVr model, a radiomics model, and a combined model. …”
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A biphasic locomotor response to acute unsignaled high temperature exposure in Drosophila. by Daniela Ostrowski, Autoosa Salari, Melissa Zars, Troy Zars

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A One- or Two-Stage Revision of Fungal Prosthetic Joint Infection: A Review of Current Knowledge, Pitfalls and Recommendations by Hazem Alkhawashki, Joseph Benevenia, Lorenzo Drago, Yazan Kadkoy

“…Fungal prosthetic joint infection (fPJI) is one of the orthopaedic pathologies where there is no clear evidence, guidelines or algorithm to guide the surgeon in its management. This is in addition to the difficulty with which these infections are diagnosed, isolated and treated. …”
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Exploring the Shared Genetic Characteristics of Peri-Implantitis and Osteoporosis: A Transcriptomic Analysis Perspective by Churen Zhang, Feixiang Wang, Jianguo Ke, Cuihong Lin, Xiaohua Zeng, Qiuyan Yang, Qiaoling Cai

“…Finally, the CIBERSORT core algorithm was used to calculate immune cell infiltration levels. …”
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Proximal LiDAR Sensing for Monitoring of Vegetative Growth in Rice at Different Growing Stages by Md Rejaul Karim, Md Nasim Reza, Shahriar Ahmed, Kyu-Ho Lee, Joonjea Sung, Sun-Ok Chung

“…LiDAR offers high-resolution, non-destructive 3D canopy characterization, yet applications in rice cultivation across different growth stages remain underexplored, while LiDAR has shown success in other crops such as vineyards. …”
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Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

“…The k-fold cross-validation technique is utilized in every algorithm to mitigate the overfitting problem during the training of models. …”
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Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025 by Kelemua Aschale Yeneakal, Gizaw Hailiye Teferi, Temesgen T. Mihret, Abraham Keffale Mengistu, Sefefe Birhanu Tizie, Maru Meseret Tadele

“…The majority age group was between 40 and 59 years (n = 2152) 46.38% and 98.1% of patients had good adherence while 1.9% had poor adherence. Among the machine learning models tested, the gradient boosting algorithm performed better than all other algorithms with (Accuracy = 0.78, Sensitivity = 0.76, F1score = 0.78, AUC = 0.76). …”
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Conv1D-LSTM: Autonomous Breast Cancer Detection Using a One-Dimensional Convolutional Neural Network With Long Short-Term Memory by Mitanshi Rastogi, Meenu Vijarania, Neha Goel, Akshat Agrawal, Cresantus N. Biamba, Celestine Iwendi

“…Breast cancer is an increasingly serious problem in contemporary society, with millions of women and men worldwide affected by the disease. While traditional cancer detection strategies are at times effective, they typically require costly and time-intensive methods for implementation. …”
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Measuring River Surface Velocity Using UAS‐Borne Doppler Radar by Zhen Zhou, Laura Riis‐Klinkvort, Emilie Ahrnkiel Jørgensen, Christine Lindenhoff, Monica Coppo Frías, Alexander Rietz Vesterhauge, Daniel Haugård Olesen, Makar Lavish, Alexey Dobrovolskiy, Alexey Kadek, Niksa Orlic, Tomislav Grubesa, Luka Drmić, Henrik Grosen, Sune Nielsen, Daniel Wennerberg, Viktor Fagerström, Jenny Axén, David Gustafsson, Peter Bauer‐Gottwein

“…Results indicate that river flow velocity and propwash velocity caused by the drone can be found in XS where the flow velocity is low, while the drone‐induced propwash velocity can be neglected in fast and highly turbulent flows. …”
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Mosquito breeding water parameters are important determinants for Microsporidia MB in the aquatic stages of Anopheles species by Esinam A. Akorli, Nana Efua Andoh, Richardson K. Egyirifa, Christopher Dorcoo, Sampson Otoo, Seraphim N. A. Tetteh, Reuben Mwimson Pul, Derrick B. Sackitey, Stephen K. D. Oware, Samuel K. Dadzie, Jewelna Akorli

“…Detection of Microsporidia MB was achieved using quantitative PCR (qPCR), while mosquito species were molecularly identified using short interspersed nuclear elements (SINE), restriction fragment length polymorphism (RFLP) methods, and the ANOSPP algorithm. …”
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