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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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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Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times by L. Zhang, L. Zhang, L. Yang, T. W. Crowther, C. M. Zohner, S. Doetterl, G. B. M. Heuvelink, G. B. M. Heuvelink, A. M. J.-C. Wadoux, A.-X. Zhu, Y. Pu, F. Shen, H. Ma, Y. Zou, C. Zhou, C. Zhou

“…<p>The turnover time (<span class="inline-formula"><i>τ</i></span>) of global soil organic carbon is central to the functioning of terrestrial ecosystems. Yet our spatially explicit understanding of the depth-dependent variations and environmental controls of <span class="inline-formula"><i>τ</i></span> at a global scale remains incomplete. …”
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Multi-scale attention-enhanced deep learning approach for detecting seven trunk pests and diseases in Shanghai’s urban plane trees by Tianyang Song, Guohua Hu, Tianci Yu, Xing Meng, Yanting Zhang, Ruiqing Yang, Benyao Wang, Xia Li

“…Trained on 3,983 annotated samples from Shanghai, the model achieved a 3.8% increase in mean Average Precision at a 50% Intersection over Union threshold (mAP50) and a significant reduction in missed detections compared to the baseline YOLOv8. …”
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Mapping Mountain Permafrost via GPR-Augmented Machine Learning in the Northeastern Qinghai–Tibet Plateau by Yao Xiao, Guangyue Liu, Guojie Hu, Defu Zou, Ren Li, Erji Du, Tonghua Wu, Xiaodong Wu, Guohui Zhao, Yonghua Zhao, Lin Zhao

“…Thirteen classification algorithms were evaluated using 5-fold cross-validation repeated 40 times, with LightGBM, CatBoost, XGBoost, and RF achieving top performance (F1 > 0.98). Elevation-based spatial comparisons revealed that LightGBM and CatBoost produced more terrain-adaptive predictions at high altitudes and slope transitions. …”
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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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Research Progress and Prospects of Urban-Rural Integration Based on Scientific Knowledge Map by Gong Weixia, Ma Shujiao

“…This study proposes the following recommendations: (1) Strengthen multi-disciplinary, multi-dimensional, and multi-scale research: this includes enhancing interdisciplinary collaboration, innovating theories and methods, and building multilevel research systems to explore the spatial and temporal evolution of urban-rural integration across different scales; (2) Deepen research on the coupling and coordination between new types of urbanization and comprehensive rural revitalization: this involves exploring coordination mechanisms, refining the flow of key elements, discovering new models of urban-rural industrial integration, and promoting cultural exchange and integration; (3) Strengthen regional coordination research; expanding research fields, summarizing integration models from different regions, enhancing regional coordination and joint development, and building multiscale regional coordination mechanisms are essential; and (4) Advanced technological innovation and data-driven research: this includes using cutting-edge technologies to reveal trends in urban-rural integration, scientifically predicting urbanization processes, and driving industrial synergy and transformation.…”
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SOC: clay ratio: A mechanistically-sound, universal soil health indicator across ecological zones and land use categories? by Walter W. Wenzel, Alireza Golestanifard, Olivier Duboc

“…The set of relevant SOC drivers and their relative importance vary with spatial scale (entire study region versus agroecological and soil units), the aridity index (defined as MAT: MAP) and the state of soil development, as reflected by soil pH. …”
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Monitoring and Mapping a Decade of Regenerative Agricultural Practices Across the Contiguous United States by Matthew O. Jones, Gleyce Figueiredo, Stephanie Howson, Ana Toro, Soren Rundquist, Gregory Garner, Facundo Della Nave, Grace Delgado, Zhuang-Fang Yi, Priscilla Ahn, Samuel Jonathan Barrett, Marie Bader, Derek Rollend, Thaïs Bendixen, Jeff Albrecht, Kangogo Sogomo, Zam Zam Musse, John Shriver

“…Monitor incorporates three datasets: CropID, a deep learning transformer model using Sentinel-2 and USDA Cropland Data Layer (CDL) data from 2018 to 2023 to predict annual crop types; the living root data, which use Normalized Difference Vegetation Index (NDVI) data to determine cover crop presence through regional parameterization; and residue cover (RC) data, which uses the Normalized Difference Tillage Index (NDTI) and crop residue cover (CRC) index to assess tillage intensity. …”
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Orchard variable-rate spraying method integrating GNSS and wind-excited audio-conducted leaf area density by Hangxing Zhao, Hangxing Zhao, Hangxing Zhao, Shenghui Yang, Shenghui Yang, Shenghui Yang, Wenwei Li, Wenwei Li, Han Feng, Han Feng, Shijie Jiang, Shijie Jiang, Weihong Liu, Weihong Liu, Jingbin Li, Yongjun Zheng, Yongjun Zheng, Songchao Zhang

“…A variable-rate spray control model and algorithm were then constructed to regulate spray flow according to the spatial distribution of leaf area density across the orchard.ResultsField experiments demonstrated that the system achieved a mean relative error of only 5.52% in spray flow rate regulation. …”
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Study on the Disturbed Mechanical Behavior and Energy Evolution Characteristics of Deep Roof Rock Considering Spatio-Temporal Effects by WANG man, ZHANG Yang, XIE Jing, NIU Zehua, DENG Huchao, YANG Bengao, ZHAO Lijuan, D ING Xiaogang, GAO Mingzhong

“…This model generalizes the disturbance stress path generated by the multi-working face mining of coal seam group in the longitudinal dimension as a cyclic loading and unloading process, the increase of the disturbance stress concentration factor caused by the reduction of the distance between the coal seams is reflected in the stress path as the increase of the peak stress of cyclic loading and unloading. …”
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Arsenic toxicity exacerbates China’s groundwater and health crisis by Xin Liu, Fu-Jun Yue, Wei Wen Wong, Shao-Chong Lin, Tian-Li Guo, Si-Liang Li

“…A random forest analysis identified chemical properties (including oxidation–reduction potential, pH, total manganese ion, total iron ion, total dissolved solids, and sulfate ion) as the most influential drivers, contributing 56% to the model’s explanatory power, followed by geographical factors at 28%, climatic factors at 10%, and human activities at 6%. …”
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Water quality management of heavily contaminated urban rivers using water quality analysis simulation program by J. Jiang, T. Ri, T. Pang, Y. Wang, P. Wang

“…This study presented a water quality analysis simulation program model-based approach for dynamical load reduction in Ashi River, highly contaminated tributaries of Songhua River, China. …”
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Absorbing Aerosol Effects on Hyperspectral Surface and Underwater UV Irradiances from OMI Measurements and Radiative Transfer Computations by Alexander Vasilkov, Nickolay Krotkov, Matthew Bandel, Hiren Jethva, David Haffner, Zachary Fasnacht, Omar Torres, Changwoo Ahn, Joanna Joiner

“…To provide insight into the temporal and spatial variability of absorbing aerosols, we consider a monthly global AAOD climatology derived from the OMI UV aerosol algorithm. …”
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Scientific geovisualization of the dynamics of Sargassum dispersion and landfall in the Caribbean, based on satellite imagery and numerical forecasts. by Francisco Javier Osorno-Covarrubias, Jorge Prado Molina, Gabriela Gómez-Rodríguez, Uriel Mendoza, Stéphane Couturier

“…For statistical purposes, rafts that land or drift outside the simulation range are logged with details of location, date, and time. 4) Animation Generation: Four animations are produced to visualize: a) Rafts movement, b) Rafts trajectories, c) The dynamics of surface forcings (currents, tides, and waves), and d) The dynamics of above-surface factors (i.e. wind drag, modeled as a percentage of wind speed). 5) Interactive 3D Visualization: All elements are integrated into an interactive globe featuring 3D bathymetry, allowing users to explore sargassum dispersion and landfall predictions (or hindcasts) for specific satellite observation dates. …”
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Spatiotemporal patterns of desertification sensitivity and influencing factors across the Western Inner Mongolia Plateau, China by Yang Chen, Long Ma, Xixi Wang, Tingxi Liu, Zixu Qiao

“…This study focuses on the Western Inner Mongolia Plateau in China as a case study to examine the evolution of desertification and its driving factors using a multifaceted approach, including the Mediterranean Desertification and Land Use (MEDALUS) model. Results show that the desertification sensitivity index (DSI) across the plateau ranged from 1.12 in prairie regions to 1.87 in desert areas, with a spatial gradient decreasing from west to east. …”
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