A state-of-the-art review on machine learning techniques for driving behavior analysis: clustering and classification approaches by Mohammad Hassan Mobini Seraji, Sami Shaffiee Haghshenas, Sina Shaffiee Haghshenas, Vladimir Simic, Dragan Pamucar, Giuseppe Guido, Vittorio Astarita

“…The review specifically explains unsupervised methods like fuzzy c-means, k-means, and density-based spatial clustering of applications with noise, as well as supervised techniques such as artificial neural networks, k-nearest neighbors, and support vector machines. …”
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Pervasive glacier retreats across Svalbard from 1985 to 2023 by Tian Li, Stefan Hofer, Geir Moholdt, Adam Igneczi, Konrad Heidler, Xiao Xiang Zhu, Jonathan Bamber

“…Abstract A major uncertainty in predicting the behaviour of marine-terminating glaciers is ice dynamics driven by non-linear calving front retreat, which is poorly understood and modelled. …”
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Suitable scale structures for urban multi-functions: an integrative approach grounded in socio-ecological system analysis by Wuyang Hong, Shuwen Yang, Renzhong Guo, Yelin Li, Lin Jiang, Xiaoming Li

“…Abstract Aims Research on the appropriate scale structure and spatial layout of ecological, urban, and agricultural spaces and the effective and methodical development and protection of urban space can provide a solid foundation for the sustainable development of the complex socio-economic urban environment. …”
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Predator guards on nest boxes improve nesting success of birds by Robyn L. Bailey, David N. Bonter

“…Across all species, top models predicting daily nest survival rates suggest a positive influence of predator guards, with a nearly 7% increase in nest success for attempts in boxes with guards versus attempts in boxes without guards. …”
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Significance of Cloud Microphysics and Cumulus Parameterization Schemes in Simulating an Extreme Flood-Producing Precipitation Event in the Central Himalaya by Ujjwal Tiwari, Andrew B. G. Bush

“…Precipitation was concentrated over the sub-Himalaya, and the established forecasting systems failed to predict the event. In this study, we evaluate the performance of six cloud microphysics schemes in the Weather Research and Forecasting (WRF) model forced with the advanced ERA5 dataset. …”
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Analysis of Dust Storm Intensity over Baghdad City by Asmaa K. Hammoodi, Ahmed S. Hassan, Jasim H. Kadhum, Xiu-Qun Yang

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Differences in the functional use of two migratory stopovers by humpback whales (Megaptera novaeangliae). by Raphael Mayaud, Joshua N Smith, David Peel, Craig Wilson, Wally Franklin, Tim Stevens, Susan Bengtson Nash

“…We examined population structure, behaviour, and habitat segregation, and developed spatial density surface models to predict density distribution patterns at each respective site. …”
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Evaluation of Solar Energy Performance in Green Buildings Using PVsyst: Focus on Panel Orientation and Efficiency by Seyed Azim Hosseini, Seyed Alireza Mansoori Al-yasin, Mohammad Gheibi, Reza Moezzi

“…Energy outputs derived from spreadsheet-based models and PVSyst simulations were compared to validate results. …”
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Indian summer monsoon’s role in shaping variability in Arctic sea ice by Jiawei Zhu, Zhiwei Wu

“…Additionally, from a dynamic standpoint, low-level wind-driven sea ice drift helps shape the spatial distribution and extent of sea ice cover. …”
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Beacon2Science: Enhancing STEREO/HI Beacon Data With Machine Learning for Efficient CME Tracking by J. Le Louëdec, M. Bauer, T. Amerstorfer, J. A. Davies

“…We maximize information coherence between consecutive frames with adapted model architecture and loss functions through the different steps. …”
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Comparative analysis of injection rate and spray characteristics of ammonia and diesel from multi-hole diesel injector by Seonho Park, Gyuhan Bae, Seoksu Moon

“…It was further found that the spray penetration of ammonia and diesel can be scaled and predicted based on a conventional momentum-conservation-based spray penetration model.…”
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FAMHE-Net: Multi-Scale Feature Augmentation and Mixture of Heterogeneous Experts for Oriented Object Detection by Yixin Chen, Weilai Jiang, Yaonan Wang

“…Current detectors struggle with integrating spatial and semantic information effectively across scales and often omit necessary refinement modules to focus on salient features. …”
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Environmental drivers of Culicoides phenology: how important is species-specific variation when determining disease policy? by Kate R Searle, James Barber, Francesca Stubbins, Karien Labuschagne, Simon Carpenter, Adam Butler, Eric Denison, Christopher Sanders, Philip S Mellor, Anthony Wilson, Noel Nelson, Simon Gubbins, Bethan V Purse

“…We conclude that the current treatment of Avaritia Culicoides as a single group inhibits understanding of environmentally-driven spatial variation in species phenology and hinders the development of models for predicting the SVFP from environmental factors. …”
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Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution by Bochao Jiang, Michael Dorosan, Justin Wen Hao Leong, Marcus Eng Hock Ong, Sean Shao Wei Lam, Tiing Leong Ang

“…We used convolutional neural network-based models pretrained on large-scale open-domain data to extract spatial features of CE images that were then used in a dense feed-forward neural network classifier. …”
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Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method by Fa Li, Qing Zhu, Kunxiaojia Yuan, Fujiang Ji, Arindam Paul, Peng Lee, Volker C. Radeloff, Min Chen

“…Abstract More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. …”
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Geospatial SHAP interpretability for urban road collapse susceptibility assessment: a case study in Hangzhou, China by Bofan Yu, Hui Li, Huaixue Xing, Weiya Ge, Liling Zhou, Jinrui Zhang, Meijun Xu, Cheng Yu

“…In addition to interpreting the contributions of evaluation factors through traditional SHAP summaries and bar plots, we displayed the SHAP values for each evaluation factor using map visualizations, and discussed the model’s sensitivity to different values. To validate the alignment between model predictions and physical collapse mechanisms, our study selected typical collapse cases, interpreted these cases combining map visualizations, SHAP force plots at collapse points, and the physical mechanisms of collapse. …”
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Early Warning of Nerve Agent Release in Large Indoor Environments Based on Encoder-Decoder Coupling Physics-Informed Neural Network by Shuobei Sun, Yang Peng, An Wang, Yiwen Xie, Yang Hu, Zhongyu Hou

“…Therefore, in this work, a new model called Encoder-Decoder Coupling Physics-Informed Neural Network is proposed, which learns from concentration data generated by experimentally validated CFD simulations and the spatio-temporal information to solve high-dimensional partial differential equations and provide predictions of nerve agents distribution that more closely align with objective physical laws. …”
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Evaluating Modified Soil Erodibility Factors with the Aid of Pedotransfer Functions and Dynamic Remote-Sensing Data for Soil Health Management by Pooja Preetha, Naveen Joseph

“…The results highlighted that the Kmlr model provided more accurate sediment yield (SY) predictions, particularly in agricultural areas, where traditional models overestimated erosion by upto 59.23 ton/ha. …”
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APPLICATION OF THE GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) METHOD IN FORECASTING THE CONSUMER PRICE INDEX IN FIVE CITIES OF SOUTH SULAWESI PROVINCE by Ahmad Zaki, Lutfiah Shafruddin, Irwan Thaha

“…The study focuses on five cities within South Sulawesi, where direct relationships between cities are possible, allowing the spatial model to be limited to the first-order. The data used in this study consists of monthly CPI data from January 2014 to March 2023. …”
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Development of an Augmented Reality Surgical Trainer for Minimally Invasive Pancreatic Surgery by Doina Pisla, Nadim Al Hajjar, Gabriela Rus, Bogdan Gherman, Andra Ciocan, Corina Radu, Calin Vaida, Damien Chablat

“…A convolutional neural network (CNN) model predicts forces without physical sensors, achieving a mean absolute error of 0.0244 N. …”
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