Releasing the octoPus, an open-source digital tool to promote Integrated Pest Management by Simone Bregaglio, Eugenio Rossi, Lorenzo Ascari, Gabriele Mongiano, Eleonora Del Cavallo, Sofia Bajocco, Luisa Maria Manici, Antonio Gerardo Pepe, Chiara Bassi, Rocchina Tiso, Fabio Pietrangeli, Giovanna Cattaneo, Camilla Nigro, Marco Secondo Gerardi, Simone Bussotti, Angela Sanchioni, Danilo Tognetti, Mariangela Sandra, Irene De Lillo, Paolo Framarin, Sandra Di Ferdinando, Riccardo Bugiani

“…However, they consistently identified a moisture and thermal north-south suitability gradient in Italy, accurately detecting grapevine seasons with low or high downy mildew pressure. …”
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Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration by T. A. Rajaperumal, C. Christopher Columbus

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Estimating latent heat flux of subtropical forests using machine learning algorithms by Harekrushna Sahu, Pramit Kumar Deb Burman, Palingamoorthy Gnanamoorthy, Qinghai Song, Yiping Zhang, Huimin Wang, Yaoliang Chen, Shusen Wang

“…Moderate Resolution Imaging Spectroradiometer (MODIS) offers a gap‐filled biophysical product for LE at 8‐day temporal and 500‐meter spatial resolutions. …”
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Molecular docking study and pharmacokinetic insights of rifampicin in pure and capsule dosage forms by Nahla A. Alassaf, Azhar S. Hamody, Majid S. Jabir, Suresh Ghotekar, Ayman A. Swelum

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Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in Rwanda by Fabrice Nkurunziza, Richard Kabanda, Patrick McSharry

“…Among the 12 classifiers evaluated, multiple kernel support vector machines, eXtreme gradient boosting, and multinomial logit demonstrated the highest predictive accuracy, ranging between 86.6% and 88.5%. …”
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Trans-scale interphase engineering for enhanced interfacial reinforcement between ‘rigid’ HMX crystals and ‘soft’ binders by Jie Li, Chunbo Shi, Shiliang Huang, Shichun Li, Jinjiang Xu, Shaohua Jin, Yu Liu

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Exploring botanical diversity, ecological traits, and edaphic determinants in Kala Chitta National Park, Pakistan: Implications for conservation and management by Muhammad Waheed, Um e Hani, Fahim Arshad, Asma Jabeen, Saud Alamri, Manzer H. Siddiqui, Shiekh Marifatul Haq, Muhammad Shoaib, Rainer W. Bussmann

“…To fill this knowledge gap, this study investigates the vegetation dynamics and edaphic factors that influence plant distribution in Kala Chitta National Park of Pakistan. …”
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Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: a synoptic review by Sunshine A. De Caires, Chaney St Martin, Melissa A. Atwell, Fuat Kaya, Glorious A. Wuddivira, Mark N. Wuddivira

“…This synoptic review evaluates the evolution of classical and modern geostatistical methods, spanning 2000 to 2024, and their integration with machine learning (ML) and remote sensing (RS) technologies. …”
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All-Weather Precipitable Water Vapor Retrieval over Land Using Integrated Near-Infrared and Microwave Satellite Observations by Shipeng Song, Mengyao Zhu, Zexing Tao, Duanyang Xu, Sunxin Jiao, Wanqing Yang, Huaxuan Wang, Guodong Zhao

“…Spatiotemporal analysis revealed that the retrieved PWV product exhibits a clear latitudinal gradient and seasonal variability, consistent with physical expectations. …”
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Effect of air layer thickness on melting kinetics and heat transfer in horizontally oriented hemispherical phase change material enclosures by Abbas Fadhil Khalaf, Farhan Lafta Rashid, Mudhar A. Al-Obaidi, Hayder I. Mohammed, Arman Ameen, Ephraim Bonah Agyekum

“…Phase change materials (PCMs) in thermal energy storage systems often encounter unintended air gaps that critically affect performance, yet their effects in hemispherical enclosures remain unexplored. …”
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Construction of Carbon Functional Zone in the Demonstration Zone of Green and Integrated Ecological Development of the Yangtze River Delta Based on Carbon Density Correction by Tian ZHANG, Zhen SHEN, Tingting SU, Daixin DAI

“…This research focuses on the critical need to evaluate the spatiotemporal dynamics of carbon storage under rapid land use changes, with a focus on improving the accuracy of carbon density estimation, and to construct a three-tiered zoning framework based on the carbon density gradient. The objectives are to overcome the limitations of static carbon density assumptions inherent in traditional methodologies and to bridge the gap between theoretical carbon storage models and practical governance strategies, thereby identifying and providing a framework for regional carbon management.MethodsTo achieve these objectives, the research develops a novel carbon density correction method that integrates climatic factors and land use characteristics, overcoming the limitations of fixed carbon density values in traditional approaches. …”
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Unraveling Spatial Nonstationary and Nonlinear Dynamics in Life Satisfaction: Integrating Geospatial Analysis of Community Built Environment and Resident Perception via MGWR, GBDT,... by Di Yang, Qiujie Lin, Haoran Li, Jinliu Chen, Hong Ni, Pengcheng Li, Ying Hu, Haoqi Wang

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Interpretable GBDT Model for Analysing Ridership Mechanisms in Urban Rail Transit: A Case Study in Shenzhen by Wenjing Wang, Haiyan Wang, Jian Xu, Chengfa Liu, Shipeng Wang, Qing Miao

“…However, existing studies predominantly rely on linear models, neglecting the nonlinear effects and spatial heterogeneity of built environment factors on ridership. To address this gap, this study integrates the Multiscale Geographically Weighted Regression (MGWR) model and the Gradient Boosting Decision Tree (GBDT) model to analyse the impact of built environment factors on total, inbound, and outbound ridership in Shenzhen. …”
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Inverse Gini indexed averaging: A multi-leveled ensemble approach for skin lesion classification using attention-integrated customized ResNet variants by Anwar Hossain Efat, SM Mahedy Hasan, Md Palash Uddin, Faysal Hossain Emon

“…For interpretability, we employ gradient class activation map to highlight the regions responsible for classification dominance, enhancing the model’s transparency. …”
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High-resolution energy consumption forecasting of a university campus power plant based on advanced machine learning techniques by Saad A. Alsamraee, Sanjeev Khanna

“…Several ML algorithms – Decision Tree (DT), Random Forest (RF), Support Vector Regressor (SVR), K-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost) – were rigorously trained, validated, and benchmarked. …”
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Inconsistency of a single-point evaluation of traction on a fault discretized with triangular elements and several improved methods by Hiroyuki Noda

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Rethinking composite quantification by capturing biological and ecological diversity across multiple dimensions by Ioan Sîrbu, Ana-Maria Benedek, Monica Sîrbu

“…Still, very few research targets combining different measures to quantify diversity under its various aspects and here we aim to fill this gap. We defined and measured overall biodiversity by integrating taxonomic, functional, genetic-, and niche-based diversity, lambda diversity, which expresses the cumulative sequential differences in communities along a gradient, and xi ecological integrative diversity combining community, spatial, and environmental heterogeneity. …”
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Reinforcement learning for autonomous underwater vehicles (AUVs): navigating challenges in dynamic and energy-constrained environments by Mohab M. Eweda, Karim ElNaggar

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Research on the Evolution Characteristics of Building Space in the Central Urban Area of Tianjin Based on Multi-Source Data Collaboration: 2021–2024 by Yicheng Zhang, Guorui Chen, Xue Yang

“…This study addresses these gaps through an integrated framework combining point-of-interest kernel density analysis and satellite imagery segmentation (2021–2024 data). …”
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A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes by Cosmina-Mihaela Rosca, Adrian Stancu

“…Thus, this paper identifies gaps in the current research, discusses ML algorithms in the context of optimizing wind energy production processes, and identifies future directions for increasing the efficiency of wind turbines through integrated predictive methods.…”
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