Graph-based analysis of histopathological images for lung cancer classification using GLCM features and enhanced graph by Imam Dad, JianFeng He, Zulqarnain Baloch

“…Our methodology leverages Gray-Level Co-occurrence Matrix (GLCM) features to quantify tissue texture, constructs a Sparse Cosine Similarity Matrix (SCSM) to model spatial relationships, and employs DeepWalk embeddings to capture topological patterns. …”
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KDM1A epigenetically enhances RAD51 expression to suppress the STING-associated anti-tumor immunity in esophageal squamous cell carcinoma by Qingyuan Yang, Shiyin Wei, Cen Qiu, Chenjie Han, Zunguo Du, Ning Wu

“…Cell growth was assessed by colony formation assays in vitro and subcutaneous xenograft models in vivo. High-throughput transcriptomics and spatial immune proteomics were performed using bulk RNA sequencing and digital spatial profiling techniques, respectively. …”
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The temporal scale of energy maximization explains allometric variations in movement decisions of large herbivores by Daniel Fortin, Christopher F. Brooke, Hervé Fritz, Jan A. Venter

“…Abstract Empirical testing of energy maximization models has been used to clarify the drivers of resource partitioning among large herbivores. …”
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Long‐term data reveals increase in vehicle collisions of endangered birds in Hokkaido, Japan by Kazuya Kobayashi, Annegret Moto Naito‐Liederbach, Toshio Sadakuni, Yuta Morii

“…These results suggest that long‐term data accumulation over large spatial scales allows us to understand the dynamics of accidents and predict potential factors underlying collision risks.…”
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Effect of protrusion structure on the performance of an advanced hydrodynamic cavitation reactor: An entropy-based analysis by Gaoju Xia, Sivakumar Manickam, Jingwei Li, Zhiqiang Yin, Wenlong Wang, Xun Sun

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Responsiveness to the Effect of Fluoxetine in Male and Female Rats Exposed to Single Prolonged Stress: A Behavioral, Biochemical, Molecular and Histological Study by Reza Eshaghi-Gorji, Sareh Rashidi, Sakineh Shafia, Fereshteh Talebpour Amiri, Mansoureh Mirzae, Moslem Mohammadi

“…Conclusion: We observed that male and female rats with PTSD, show a reduction of the levels of serum IGF-1, impaired spatial memory in a recognition location memory task and enhanced apoptotic-related factors expression in the hippocampus, and decreased hippocampal dendritic branches. …”
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Machine learning approaches for imputing missing meteorological data in Senegal by Mory Toure, Nana Ama Browne Klutse, Mamadou Adama Sarr, Md Abul Ehsan Bhuiyan, Annine Duclaire Kenne, Wassila Mamadou Thiaw, Daouda Badiane, Amadou Thierno Gaye, Ousmane Ndiaye, Cheikh Mbow

“…XGB consistently outperformed all methods across variables and scenarios, achieving the highest average predictive accuracy with R2 values up to [95 % CI: 0.82–0.88], along with lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). …”
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Individual bacteria in structured environments rely on phenotypic resistance to phage. by Erin L Attrill, Rory Claydon, Urszula Łapińska, Mario Recker, Sean Meaden, Aidan T Brown, Edze R Westra, Sarah V Harding, Stefano Pagliara

“…This survival strategy is in contrast with the emergence of genetic resistance in the absence of ephemeral refuges in well-mixed environments. Predictions generated via a mathematical modelling framework to track bacterial response to phages reveal that the presence of spatial refuges leads to fundamentally different population dynamics that should be considered in order to predict and manipulate the evolutionary and ecological dynamics of bacteria-phage interactions in naturally structured environments.…”
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mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R by Patrick Schratz, Marc Becker, Michel Lang, Alexander Brenning

“… Spatial and spatiotemporal machine-learning models require a suitable framework for their model assessment, model selection, and hyperparameter tuning, in order to avoid error estimation bias and over-fitting. …”
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Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization by Venkata Nagaraju Thatha, M. Ganesh Karthik, Venu Gopal Gaddam, D. Pramodh Krishna, S. Venkataramana, Kranthi Kumar Lella, Udayaraju Pamula

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Long-Term (2015–2024) Daily PM_2.5 Estimation in China by Using XGBoost Combining Empirical Orthogonal Function Decomposition by Jiacheng Jiang, Jiaxin Dong, Yu Ding, Wenjia Ni, Jie Yang, Siwei Li

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A Three-Level Meta-Frontier Framework with Machine Learning Projections for Carbon Emission Efficiency Analysis: Heterogeneity Decomposition and Policy Implications by Xiaoxia Zhu, Tongyue Feng, Yuhe Shen, Ning Zhang, Xu Guo

“…Methodologically, we introduce two novel projection combinations—“exogenous-exogenous-accumulation (E-E-A) and exogenous-exogenous-consistent (E-E-C)”—to resolve the inconsistency of technology gap ratios (TGRs > 1) in traditional nonradial directional distance function (DDF) models. Reinforcement learning (RL) optimizes dynamic direction vectors, whereas graph neural networks (GNNs) encode spatial interdependencies to constrain the TGR within [0, 1]. …”
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Accounting for transience in the baseline climate state changes the surface climate response attributed to stratospheric aerosol injection by Alistair Duffey, Peter J Irvine

“…However, relative to the hypothetical scenario with lower CO _2 concentrations that would achieve a stabilised climate at the same temperature, SAI produces a 69% larger reduction in global precipitation. Accounting for stabilisation can also meaningfully change the spatial pattern of surface temperature response attributable to SAI. …”
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Impact of modulating surface heat flux through sea ice leads on Arctic sea ice in EC-Earth3 in different climates by T. Tian, R. Davy, L. Ponsoni, S. Yang

“…<p>This sensitivity study examines the impact of modulating surface sensible heat flux over leads – open-water areas within sea ice cover – to approximate finer-scale processes that are often underrepresented in climate models. We aim to assess how this parameterization (referred to as ECE3L) influences the persistent positive bias in Arctic sea ice (concentration and thickness) in the global climate model EC-Earth3 (ECE3). …”
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GOES‐R PM2.5 Evaluation and Bias Correction: A Deep Learning Approach by Alqamah Sayeed, Pawan Gupta, Barron Henderson, Shobha Kondragunta, Hai Zhang, Yang Liu

“…Abstract Estimating surface‐level fine particulate matter from satellite remote sensing can expand the spatial coverage of ground‐based monitors. This approach is particularly effective in assessing rapidly changing air pollution events such as wildland fires that often start far away from centralized ground monitors. …”
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PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion by Heqi Yang, Junming Dong, Cancan Wang, Zhida Lian, Hui Chang

“…To address these issues, this paper proposes PCES-YOLO, an enhanced YOLOv11-based model. First, a developed Pre-convolution Receptive Field Enhancement (PRFE) module replaces C3k in the C3k2 module. …”
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Canal Geometry, Flow Velocity, Dallisgrass (Paspalum dilatatum Poir.) Density and Soil Phosphorous Effects on Hydraulic Resistance of Vegetated Canals by Abolfazl Nasserı

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Integration of UAV Multi-Source Data for Accurate Plant Height and SPAD Estimation in Peanut by Ning He, Bo Chen, Xianju Lu, Bo Bai, Jiangchuan Fan, Yongjiang Zhang, Guowei Li, Xinyu Guo

“…This study aimed to develop an optimized estimation framework for peanut plant height and SPAD values through machine learning-driven integration of UAV multi-source data while evaluating model generalizability across temporal and spatial domains. …”
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Defining disease heterogeneity to guide the empirical treatment of febrile illness in resource poor settings. by Lisa J White, Paul N Newton, Richard J Maude, Wirichada Pan-ngum, Jessica R Fried, Mayfong Mayxay, Rapeephan R Maude, Nicholas P J Day

“…<h4>Findings</h4>The model predicted a negative correlation between number of appropriate treatments and the level of spatial heterogeneity. …”
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Stacking data analysis method for Langmuir multi-probe payload by Jin Wang, Jin Wang, Duan Zhang, Qinghe Zhang, Qinghe Zhang, Xinyao Xie, Fangye Zou, Qingfu Du, Qingfu Du, V. Manu, Yanjv Sun

“…This study uses a stacking algorithm to process m-NLP data and incorporates the International Reference Ionosphere (IRI) model to correct the predicted electron density (Ne) values. …”
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