Optimizing casting process using a combination of small data machine learning and phase-field simulations by Xiaolong Pei, Jiaqi Pei, Hua Hou, Yuhong Zhao

Get full text

NAP1L5 in acute myeloid leukemia: a prognostic biomarker and potential therapeutic target by Meng Wang, Meng Wang, Zhibin Xie, Yuanyuan Tan, Yan Zhou, Tingting Zhang, Yuqing Du, Huan Wu, Lili Zhou, Jian Ge

“…Notably, high NAP1L5 expression correlated with increased infiltration of resting CD4+ memory T cells, implicating its potential influence on the tumor immune microenvironment. A predictive model integrating NAP1L5 expression and clinical AML features exhibited robust prognostic utility. …”
Get full text

"Hacia una Escuela Nueva. Unidades de capacitación docente": manual de orientação para o professor rural colombiano by Sara Evelin Urrea Quintero, Elizabeth de Sá

“…The utilization of text books is one of the main characteristics, not only for the students' learning process, but also as pedagogical text books created for the teachers' academic training taking into account the school model features, and how to perform adequately within it. …”
Get full text

A large-scale ENIGMA multisite replication study of brain age in depression by Laura K.M. Han, Richard Dinga, Ramona Leenings, Tim Hahn, James H. Cole, Lyubomir I. Aftanas, Alyssa R. Amod, Bianca Besteher, Romain Colle, Emmanuelle Corruble, Baptiste Couvy-Duchesne, Konstantin V. Danilenko, Paola Fuentes-Claramonte, Ali Saffet Gonul, Ian H. Gotlib, Roberto Goya-Maldonado, Nynke A. Groenewold, Paul Hamilton, Naho Ichikawa, Jonathan C. Ipser, Eri Itai, Sheri-Michelle Koopowitz, Meng Li, Go Okada, Yasumasa Okamoto, Olga S. Churikova, Evgeny A. Osipov, Brenda W.J.H. Penninx, Edith Pomarol-Clotet, Elena Rodríguez-Cano, Matthew D. Sacchet, Hotaka Shinzato, Kang Sim, Dan J. Stein, Aslihan Uyar-Demir, Dick J. Veltman, Lianne Schmaal

“…Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2 = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. …”
Get full text

Graph‐Based Representation Approach for Deep Learning of Organic Light‐Emitting Diode Devices by Taeyang Lee, Jeongwhan Choi, Inyeob Na, Insun Yoo, Sungil Woo, Kwang Jong Kim, Mikyung Park, Joonghwan Yang, Jeongguk Min, Seokwoo Lee, Noseong Park, Joonyoung Yang

“…The performance prediction of organic light‐emitting diode (OLED) devices using artificial intelligence is significantly limited due to the lack of representational feature data. …”
Get full text

Target oxidative stress-induced disulfidptosis: novel therapeutic avenues in Parkinson’s disease by Junshi Zhang, Tingting Liu, Haojie Wu, Jianshe Wei, Qiumin Qu

“…LASSO regression was applied for feature selection, with subsequent experimental validation in MPTP-induced PD mouse models. …”
Get full text

MDO of Robotic Landing Gear Systems: A Hybrid Belt-Driven Compliant Mechanism for VTOL Drones Application by Masoud Kabganian, Seyed M. Hashemi

“…Compared to the single-revolute (<i>1R</i>) model, the implementation of the optimal <i>3R</i> Pseudo-Rigid-Body Model (PRBM) further improves accuracy by achieving a maximum tip deflection error of only 1.2%. …”
Get full text

Integrated analysis of bioinformatics, mendelian randomization, and experimental validation reveals novel diagnostic and therapeutic targets for osteoarthritis: progesterone as a p... by Ziyu Weng, Chenzhong Wang, Bo Liu, Yi Yang, Yueqi Zhang, Chi Zhang

“…An Artificial Neural Network (ANN) diagnostic model constructed based on these hub genes demonstrated excellent performance in both training and validation phases. …”
Get full text

Condition-Aware Autoencoder and Transfer Learning-Based Estimation of Milling Cutting Forces from Spindle Vibration Signals by Je-Doo Ryu, Jungmin Lee, Sung-Ryul Kim, Min Cheol Lee

“…To reflect machining parameters into the learning model, the input dataset was constructed by integrating material type, cutting speed, and cutting direction as additional inputs into each model’s inputs. …”
Get full text

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

“…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). …”
Get full text

A Review of YOLO Algorithm and Its Applications in Autonomous Driving Object Detection by Jiapei Wei, Azizan As'arry, Khairil Anas Md Rezali, Mohd Zuhri Mohamed Yusoff, Haohao Ma, Kunlun Zhang

“…Firstly, the development and distinctions among the YOLO series of detection algorithms are explained, and their performance is analyzed. Secondly, the strategies for improving YOLO-based models across the input, feature extraction, and prediction stages are summarized. …”
Get full text

Stratified Multisource Optical Coherence Tomography Integration and Cross-Pathology Validation Framework for Automated Retinal Diagnostics by Michael Sher, Riah Sharma, David Remyes, Daniel Nasef, Demarcus Nasef, Milan Toma

“…The framework advances AI-driven Optical Coherence Tomography diagnostics by harmonizing computational performance with patient-centered outcomes, enabling standardized disease detection across diverse clinical datasets through robust feature generalization.…”
Get full text

Determination of future gully erosion risk and its spatially quantitative interpretability of driving factors in regional scale using machine learning algorithms by Xin Liu, Dichen Wang, Mingming Guo, Xingyi Zhang, Zhuoxin Chen, Zhaokai Wan, Jielin Liu

“…This study proposed a new gully erosion risk modeling (GERM) method by combining GESM reflecting gully erosion potential and gully density modeling (GDM) reflecting current gully erosion situation in the whole rolling hilly region of northeast China with an area of 177,584 km2. …”
Get full text

Exploration of Fully‐Automated Body Composition Analysis Using Routine CT‐Staging of Lung Cancer Patients for Survival Prognosis by Marc‐David Künnemann, Christian Römer, Anne Helfen, Annalen Bleckmann, Marcel Kemper, Walter Heindel, Tobias J. Brix, Michael Forsting, Johannes Haubold, Marcel Opitz, Martin Schuler, Felix Nensa, Katarzyna Borys, René Hosch

“…Kaplan–Meier survival analysis, Cox proportional hazards modelling and machine learning‐based survival prediction were performed. …”
Get full text

Comparison of Deep Learning Techniques for RF-Based Human Posture Detection Systems by Eugene Casmin, Miriam Rodrigues, Americo Alves, Rodolfo Oliveira

“…To this effect, we propose four different solutions: one based on traditional signal processing (SP) techniques, where the detection is centred around a correlation of prior classification masks; a second based on a hybrid SP and deep learning (DL) technique, where the DL model is trained with supervised data gathered at a single distance to the target; a third based on a hybrid SP and DL technique trained with data gathered at multiple distances to the target; and a fourth that uses variational auto-encoder (VAE) for feature generation. …”
Get full text

Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol by Mauro Podda, Adolfo Pisanu, Gianluca Pellino, Adriano De Simone, Lucio Selvaggi, Valentina Murzi, Eleonora Locci, Matteo Rottoli, Giacomo Calini, Stefano Cardelli, Fausto Catena, Carlo Vallicelli, Raffaele Bova, Gabriele Vigutto, Fabrizio D’Acapito, Giorgio Ercolani, Leonardo Solaini, Alan Biloslavo, Paola Germani, Camilla Colutta, Savino Occhionorelli, Domenico Lacavalla, Maria Grazia Sibilla, Stefano Olmi, Matteo Uccelli, Alberto Oldani, Alessio Giordano, Tommaso Guagni, Davina Perini, Francesco Pata, Bruno Nardo, Daniele Paglione, Giusi Franco, Matteo Donadon, Marcello Di Martino, Dario Bruzzese, Daniela Pacella

“…Performance metrics such as accuracy, precision, recall, and area under the ROC curve (AUC) will be used to evaluate the model. …”
Get full text

Vital node searcher: find out critical node measure with deep reinforcement learning by Guanting Du, Fei Zhu, Quan Liu

“…The VNS method performed experiments on four datasets and obtained ANC scores that outperformed the other models respectively. …”
Get full text

Semi-Supervised Fault Diagnosis Method for Hydraulic Pumps Based on Data Augmentation Consistency Regularization by Siyuan Liu, Jixiong Yin, Zhengming Zhang, Yongqiang Zhang, Chao Ai, Wanlu Jiang

“…Comparative experiments based on data from two models of hydraulic pumps verify the superior recognition performance of this method under low label rate.…”
Get full text

Identifying the Dimensions and Components of Virtual Arabic Language Education in Iraq by Abbas Taher Allavi Gharabat, Nasrolah Ghashghaeizadeh, Jalal Shanta Jabir, Faranak mosavi

“…The progress and survival of any society depends on the quality and efficiency of technology in the education of that society, and virtual education, given its special and outstanding features, creates a unique and lasting opportunity for teaching and learning (Hwang et al, 2023). …”
Get full text

Spatiotemporal Determinants of Urban Leptospirosis Transmission: Four-Year Prospective Cohort Study of Slum Residents in Brazil. by José E Hagan, Paula Moraga, Federico Costa, Nicolas Capian, Guilherme S Ribeiro, Elsio A Wunder, Ridalva D M Felzemburgh, Renato B Reis, Nivison Nery, Francisco S Santana, Deborah Fraga, Balbino L Dos Santos, Andréia C Santos, Adriano Queiroz, Wagner Tassinari, Marilia S Carvalho, Mitermayer G Reis, Peter J Diggle, Albert I Ko

“…<h4>Methodology/principal findings</h4>We performed a prospective study of 2,003 slum residents in the city of Salvador, Brazil during a four-year period (2003-2007) and used a spatiotemporal modelling approach to delineate the dynamics of leptospiral transmission. …”
Get full text