Accelerated discovery of near-zero ablation ultra-high temperature ceramics via GAN-enhanced directionally constrained active learning by Wenjian Guo, Fayuan Li, Lingyu Wang, Li'an Zhu, Yicong Ye, Zhen Wang, Bin Yang, Shifeng Zhang, Shuxin Bai

“…The framework employs GAN for data augmentation, symbolic regression for feature weight derivation, and a self-developed EAI function that incorporates input feature importance weighting to quantify bidirectional deviations from zero ablation rate. …”
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Temporal Coupling of Brain Signals and Fine Motor Output Using Affordable EEG by Adam Gyula Nemes, Adam Gyulai, Adam Szarvas, Erno Nemeth, Kristof Tajti, Viktor Toth, Gyorgy Eigner

“…This multimodal approach enhances dataset quality by enabling millisecond-precise alignment between stimulus presentation, cortical activation patterns, and resultant motor behavior—a critical feature lacking in most publicly available motor-related EEG datasets. …”
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Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model by Fanchao Zeng, Qing Gao, Lifeng Wu, Zhilong Rao, Zihan Wang, Xinjian Zhang, Fuqi Yao, Jinwei Sun

“…Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), (2) a feature-optimized XGBoost variant incorporating Pearson correlation analysis (XGBoost2), and (3) an enhanced CPSO-XGBoost model integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection and parameter tuning. …”
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A deep learning model based on self-supervised learning for identifying subtypes of proliferative hepatocellular carcinoma from dynamic contrast-enhanced MRI by Hui Qu, Shuairan Zhang, Xuedan Li, Yuan Miao, Yuxi Han, Ronghui Ju, Xiaoyu Cui, Yiling Li

“…The model analyzes temporal and spatial patterns in DCE-MRI data to identify the proliferative subtype efficiently and accurately. …”
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Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysis by Helen Coupland, Neil Scheidwasser, Alexandros Katsiferis, Megan Davies, Seth Flaxman, Naja Hulvej Rod, Swapnil Mishra, Samir Bhatt, H. Juliette T. Unwin

“…However, significant gaps remain in understanding their utility and limitations, especially for sparse longitudinal life course data and how the influential patterns identified using explainability are linked to underlying causal mechanisms. …”
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Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review by Yunyun Cheng, Rong Cheng, Ting Xu, Xiuhui Tan, Yanping Bai

“…Since the outbreak of COVID-19, there has been an influx of research on predictive modelling, with artificial intelligence (AI) techniques, particularly machine learning (ML) methods, becoming the dominant research direction due to their superior capability in processing multidimensional datasets and capturing complex nonlinear transmission patterns. We systematically reviewed COVID-19 ML prediction models developed under the background of the epidemic using the PRISMA method. …”
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SYNESTHESIA IN THE NOVEL BY M. PAVIĆ: “LANDSCAPE PAINTED WITH TEA” by Nataliya L. Bilyk

“…The specific nature of certain components of the mechanism and the patterns of functioning inherent in synesthesia, and above all, meaning-making, should be accepted in a generalized aspect. …”
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Multi-scale machine learning model predicts muscle and functional disease progression by Silvia S. Blemker, Lara Riem, Olivia DuCharme, Megan Pinette, Kathryn Eve Costanzo, Emma Weatherley, Jeff Statland, Stephen J. Tapscott, Leo H. Wang, Dennis W. W. Shaw, Xing Song, Doris Leung, Seth D. Friedman

“…Abstract Facioscapulohumeral muscular dystrophy (FSHD) is a genetic neuromuscular disorder characterized by progressive muscle degeneration with substantial variability in severity and progression patterns. FSHD is a highly heterogeneous disease; however, current clinical metrics used for tracking disease progression lack sensitivity for personalized assessment, which greatly limits the design and execution of clinical trials. …”
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Laser‐Induced Structuring of Biocompatible Polymers for the Controlled Orientation of Multinucleated Myotubes by Clarissa Murru, Lucas Duvert, Daniel Ferry, Ahmed Al‐Kattan, Frederique Magdinier, Anne‐Patricia Alloncle, Stefano Testa, Adrien Casanova

“…These findings emphasize the potential of laser‐patterned polymer surfaces to guide muscle cell orientation and differentiation, providing a promising approach for developing functional surfaces in skeletal muscle tissue engineering.…”
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Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenari... by Cecilia Parracciani, Daniela Gigante, Onisimo Mutanga, Stefania Bonafoni, Marco Vizzari

“…However, the classification of shrublands proved challenging due to their mixed composition and unique spatial patterns, resulting in lower accuracies. Feature importance analysis demonstrated the value of the enhanced map composition, and applying the LandTrendr algorithm simplified the diachronic land use and land cover (LULC) classification and change analysis by supporting automatic training data collection. …”
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A protein-based classifier for differentiating follicular thyroid adenoma and carcinoma by Yaoting Sun, He Wang, Lu Li, Jianbiao Wang, Wanyuan Chen, Li Peng, Pingping Hu, Jing Yu, Xue Cai, Nan Yao, Yan Zhou, Jiatong Wang, Yingrui Wang, Liqin Qian, Weigang Ge, Mengni Chen, Feng Yang, Zhiqiang Gui, Wei Sun, Zhihong Wang, Minghua Ge, Yi He, Guangzhi Wang, Yongfu Zhao, Huanjie Chen, Xiaohong Wu, Yuxin Du, Wenjun Wei, Fan Wu, Dingcun Luo, Xiangfeng Lin, Haitao Zheng, Xin Zhu, Bei Wei, Jiafei Shen, Jincao Yao, Zhennan Yuan, Tong Liu, Jun Pan, Yifeng Zhang, Yangfan Lv, Qiaonan Guo, Qijun Wu, Tingting Gong, Ting Chen, Shu Zheng, Jingqiang Zhu, Hanqing Liu, Chuang Chen, Hong Han, Sathiyamoorthy Selvarajan, Michael Mingzhao Xing, Kennichi Kakudo, Erik K Alexander, Yijun Wu, Yu Wang, Dong Xu, Hao Zhang, Xiu Nie, Oi Lian Kon, N Gopalakrishna Iyer, Zhiyan Liu, Yi Zhu, Haixia Guan, Tiannan Guo, We-TEC Investigators

“…Abstract Differentiating follicular thyroid adenoma (FTA) from carcinoma (FTC) remains challenging due to similar histological features separate from invasion. This study developed and validated DNA- and/or protein-based classifiers. …”
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Behavioral Biases in Investor Decision-Making: A Comparative Meta-Analysis of Behavioral Finance Research by Seyed Amir Sabet, Saeed Aibaghi esfahani, Abdolmajid Abdolbaghi Ataabadi

“…Key findings reveal several important patterns. Overconfidence bias was universally validated. …”
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Distinct Neural Activities in Hippocampal Subregions Revealed Using a High-Performance Wireless Microsystem with PtNPs/PEDOT:PSS-Enhanced Microelectrode Arrays by Peiyao Jiao, Qianli Jia, Shuqi Li, Jin Shan, Wei Xu, Yu Wang, Yu Liu, Mingchuan Wang, Yilin Song, Yulian Zhang, Yanbing Yu, Mixia Wang, Xinxia Cai

“…A custom NeuroWireless platform was developed for seamless data reception and storage. …”
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Media Framing and Portrayals of Ransomware Impacts on Informatics, Employees, and Patients: Systematic Media Literature Review by Atiya Avery, Elizabeth White Baker, Brittany Wright, Ishmael Avery, Dream Gomez

“…First, an analysis of the geographic prevalence showed that the United States (34/48, 71%), followed to a lesser extent by India (4/48, 8%) and Canada (3/48, 6%), featured more prominently in our sample. Second, there were no apparent year-to-year patterns in the occurrence of reported events of ransomware attacks on health care provider information systems. …”
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Sparse Convolution FPGA Accelerator Based on Multi-Bank Hash Selection by Jia Xu, Han Pu, Dong Wang

“…This circuit is designed to detect and eliminate the computational effort associated with zero values in the sparse convolutional kernels, thereby enhancing energy efficiency. (2) The data access patterns in convolutional neural networks introduce significant pressure on the high-latency off-chip memory access process. …”
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A novel measurement system for unattended, in situ characterization of carbonaceous aerosols by A. Keller, P. Specht, P. Steigmeier, E. Weingartner

“…To address this issue we have developed the fast thermal carbon totalizator (FATCAT). …”
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Topology-aware pathway analysis of spatial transcriptomics by Siras Hakobyan, Maria Schmidt, Hans Binder, Arsen Arakelyan

“…To support interactive exploration of results, we developed the PSF Spatial Browser, an R Shiny application for visualizing pathway activities, gene expression patterns, and deregulated pathway networks.…”
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Advertisements of ultra-processed products outside food outlets: field evidence from Montevideo, Uruguay by Gastón Ares, Florencia Alcaire, Gerónimo Brunet, Tobias Otterbring, María Costa, Sofía Verdier, María Rosa Curutchet, Luciana Bonilla, Sergio Turra, Fernanda Risso, Leticia Vidal

“… Abstract Objectives: To evaluate the prevalence of advertisements for ultra-processed products outside food outlets in Montevideo (Uruguay) and explore the patterns of these advertisements across areas with different socio-economic statuses (SES). …”
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A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…QSVM employs quantum kernels to map data into a higher-dimensional Hilbert space, so that the model can detect complex patterns in MM drug resistance. qPCA reduces dimensionality without loss of important variance, and thus improves computation efficiency. …”
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Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks by Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K. Ochab, Jeremi K. Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T. Podolak, Paweł Oświęcimka, Paweł Oświęcimka, Paweł Oświęcimka

“…The best performance was achieved by the LGBM classifier with 1-time point input data during memory retrieval and a convolutional neural network during the encoding phase. Additionally, we developed an algorithm that took into account feature correlations to estimate the most important brain regions for the model's accuracy. …”
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