A Lightweight CNN-Transformer Implemented via Structural Re-Parameterization and Hybrid Attention for Remote Sensing Image Super-Resolution by Jie Wang, Hongwei Li, Yifan Li, Zilong Qin

“…Moreover, the distinctive architectures of transformers, their extensive parameter counts, and their reliance on large-scale training datasets impose constraints on transformer applications in remote sensing image feature extraction tasks. …”
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Fossil Diatoms Reveal Natural and Anthropogenic History of Jackson Lake (Wyoming, USA) by John Dilworth, Jeffery R. Stone, Kevin M. Yeager, J. Ryan Thigpen, Michael M. McGlue

“…Paleoecological (diatoms) and geochemical datasets were developed on a well-dated sediment core and compared with available hydroclimate data from the region, to assess patterns of limnological change. …”
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On the lidar-turbulence paradox and possible countermeasures by A. Peña, G. G. Yankova, V. Mallini

“…The physics-based lidar-turbulence model is used to produce physics-based datasets, which are utilized to train neural networks. …”
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LAMB1 promotes proliferation and metastasis in nasopharyngeal carcinoma and shapes the immune-suppressive tumor microenvironment by Enzi Feng, Xinyu Yang, Jie Yang, Qianqian Qu, Xiaojiang Li

“…However, the role of LAMB1 in Nasopharyngeal Carcinoma (NPC) remains unknown. Methods: Three NPC datasets were utilized to identify LAMB1 as a targeted gene. …”
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The complete mitochondrial genomes of five critical phytopathogenic Bipolaris species: features, evolution, and phylogeny by Xinzheng Song, Yuehua Geng, Chao Xu, Jiaxin Li, Yashuang Guo, Yan Shi, Qingzhou Ma, Qiang Li, Meng Zhang

“…Phylogenetic analysis based on combined mitochondrial gene datasets showed that the five Bipolaris species formed well-supported topologies. …”
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A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features by Jiajia Luo, Xuan Wang, Xiaomao Fan, Yuxin He, Xiangjun Du, Yao-Qing Chen, Yang Zhao

“…Results Our approach for predicting influenza outbreaks achieves competitive performance on real-world datasets (Corr = 0.8202; RMSE = 0.0017; MAE = 0.0013; MAPE = 0.0966), surpassing established baselines. …”
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Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes by Fanqi Liang, Man Zheng, Jingjiu Lu, Peng Liu, Xinyu Chen

“…The study also examined the correlation between these hub PMGs and related genes, with validation conducted through expression level analysis using the GSE13904 and GSE65682 datasets. The study identified twelve PMGs correlated with sepsis, namely AK9, ENTPD3, NUDT16, GMPR2, PKM, RRM2B, POLR2J, POLE3, ADCY3, ADCY4, ADSSL1, and AMPD1. …”
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A systematic review of the impact of artificial intelligence on educational outcomes in health professions education by Eva Feigerlova, Hind Hani, Ellie Hothersall-Davies

“…There was insufficient information on training datasets and copyright issues. Conclusions The results of the analysis show that the current evidence regarding measurable educational outcomes of AI-powered interventions in health professions education is poor. …”
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A Scalable Approach to Internet of Things and Industrial Internet of Things Security: Evaluating Adaptive Self-Adjusting Memory K-Nearest Neighbor for Zero-Day Attack Detection by Promise Ricardo Agbedanu, Shanchieh Jay Yang, Richard Musabe, Ignace Gatare, James Rwigema

“…Through extensive testing, our proposed method demonstrates superior memory efficiency, with a memory footprint as low as 0.05 MB, while maintaining high accuracy and F1 scores across all datasets. The proposed method also recorded a detection rate of 1.00 across all simulated zero-day attacks. …”
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Efficient and Secure Traffic Scheduling Based on Private Sketch by Yang Chen, Huishu Wu, Xuhao Ren

“…Experimental evaluations on real and synthetic datasets demonstrate that ESTS significantly improves the accuracy of feature distribution estimation and privacy preservation. …”
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The use of artificial intelligence in adapting process of UI design system for end customer requirements by Kyrylo Polishchuk, Eugene Brezhniev

“…Careful consideration is given to the selection of datasets, ensuring that they meet rigorous criteria for evaluating the quality and efficacy of artificial intelligence learning. …”
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Geospatial Modelling for Estimation of PM2.5 Concentrations in Two Megacities in Peninsular India by V. P. Lavanyaa, S. Varshini, Souvik Sankar Mitra, Kiran M. Hungund, Rudrodip Majumdar, R. Srikanth

“…This model is based on covariates such as aerosol optical depth (AOD), meteorological parameters, and Land-use-Land-cover (LULC) variables and is validated with extensive datasets from continuous and manual air quality monitoring stations through a 10-fold cross-validation process. …”
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Shaping ability of NiTi reciprocating file systems R-motion and waveOne gold in mesial canals of mandibular molars; micro CT study by Mona Alsulaiman, Loai Alsofi, Mey A. Alhabib

“…Specimens were scanned using micro-CT scans before and after instrumentation to establish baseline and post-instrumentation datasets. The following parameters were evaluated, and values were calculated to compare the shaping ability of the two files: percentage of untouched canal walls, changes in surface area, canal volume, dentin thickness, canal angulation, structure model index, canal transportation, and centering ability ratio. …”
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Climate drives the long-term ant male production in a tropical community by Adriana Uquillas, Nathaly Bonilla, Stephany Arizala, Yves Basset, Héctor Barrios, David A. Donoso

“…However, predicting future responses requires long-term datasets, which are rarely available for insects, especially in the tropics. …”
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The Tasmanian Estuary Perch Population—A Threatened Species? by Jonah L. Yick, Bryan Van Wyk, James Haddy

“…This study also confirms this population has a delayed spawning season in comparison to mainland populations, with growth models and tag recaptures (32 fish) demonstrating a very slow but sexually dimorphic growth rate. By combining the datasets of the 2014/15 and 2023 survey, we present an updated understanding of the biology and population dynamics of this unique Tasmanian population, which has previously never been published. …”
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Application of a Multi‐Layer Artificial Neural Network in a 3‐D Global Electron Density Model Using the Long‐Term Observations of COSMIC, Fengyun‐3C, and Digisonde by Wang Li, Dongsheng Zhao, Changyong He, Yi Shen, Andong Hu, Kefei Zhang

“…The correlation coefficients of the predicted profiles all exceed 0.96 for the training, validation and test datasets, and the minimum root‐mean‐square error of the predicted residuals is 7.8 × 104 el/cm3. …”
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Association of objective and subjective socioeconomic markers with cognitive impairment among older adults: cross-sectional evidence from a developing country by Shobhit Srivastava, T Muhammad, TV Sekher

“…Objective This study explored how various markers of objective and subjective socioeconomic status (SES) are associated with cognitive impairment among older Indian adults.Design A cross-sectional study was conducted using large nationally representative survey data.Setting and participant This study used data from the Longitudinal Ageing Study in India (2017–2018). …”
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Impacts of Biochar Application on Inorganic Phosphorus Fractions in Agricultural Soils by Liwen Lin, Yutao Peng, Lin Zhou, Baige Zhang, Qing Chen, Hao Chen

“…Herein, we conducted a quantitative meta-analysis of 74 peer-reviewed datasets, drawing general conclusions and confirming the absence of publication bias through funnel plot statistics. …”
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Constructing meaningful code changes via graph transformer by Shikai Guo, Mengxuan Li, Xin Ge, Hui Li, Rong Chen, Tingting Li

“…For the quantitative analysis, GTCT relatively outperforms the baseline on six datasets by 210%, 342.86%, 135%, 29.41%, 109.09%, and 91.67% in terms of perfect prediction. …”
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Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards by Tünde Takáts, László Pásztor, Mátyás Árvai, Gáspár Albert, János Mészáros

“…Soil loss was formerly modeled by USLE, thus providing non-observation-based reference datasets for the calibration of parcel-specific prediction models using various ML methods (Random Forest, eXtreme Gradient Boosting, Regularized Support Vector Machine with Linear Kernel), which is a well-established approach in digital soil mapping (DSM). …”
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