An Autism Spectrum Disorder Identification Method Based on 3D-CNN and Segmented Temporal Decision Network by Zhiling Liu, Ye Chen, Xinrui Dong, Jing Liu

“…The concatenated spatiotemporal features are classified using Gradient Boosting Decision Trees (GBDTs), and finally, a voting mechanism is applied to determine whether the subject belongs to the ASD group based on the prediction results. This approach not only enhances the efficiency of spatiotemporal feature extraction but also improves the model’s ability to learn complex brain activity patterns. (3) Results: The proposed method was evaluated on the ABIDE dataset, which includes 1035 subjects from 17 different brain imaging centers. …”
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Application of Ensemble Kalman Filter With Covariance Localization in Data Assimilation of Radiation Belt Electrons by Yuan Lei, Xing Cao, Binbin Ni, Taorong Luo, Xiaoyu Wang

“…As a result, EnKF has emerged as an increasingly important tool for the reanalysis and prediction of highly dynamic variations of energetic electrons in Earth's radiation belt. …”
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Infra-3DRC-FusionNet: Deep Fusion of Roadside Mounted RGB Mono Camera and Three-Dimensional Automotive Radar for Traffic User Detection by Shiva Agrawal, Savankumar Bhanderi, Gordon Elger

“…These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. …”
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Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny by ZHANG Guanghua, LI Congfa, LI Gangying, LU Weidang

“…In addition, the ConvMixer layer is integrated into the prediction head; the depthwise and pointwise convolutions in ConvMixer capture the spatial and channel relationships in the feature information, improving the processing capability for small targets. …”
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LiDAR-Based Road Cracking Detection: Machine Learning Comparison, Intensity Normalization, and Open-Source WebGIS for Infrastructure Maintenance by Nicole Pascucci, Donatella Dominici, Ayman Habib

“…The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. …”
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Multi-task advanced convolutional neural network for robust lymphoblastic leukemia diagnosis, classification, and segmentation by Sercan Yalcin, Zuhal Cetin Yalcin, Muhammed Yildirim, Bilal Alatas

“…The proposed framework achieved an accuracy of 0.978, precision of 0.979, recall of 0.967, F1-score of 0.973, specificity of 0.991, Cohen’s kappa of 0.979, and negative predictive value (NPV) of 0.990. These metrics significantly outperform baseline models, highlighting the MTA-CNN’s ability to accurately identify and classify ALL cases. …”
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An Improved U-Net-Based Framework for Estimating River Surface Flow Velocity by 周继威, 安国成, 王根一

“…Existing deep learning models face challenges in generalization due to limited training samples and data heterogeneity across complex river environments. …”
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Pharmacological potential of 3,5-dimethyl-4-(3-(5-nitrofuran-2-yl)allylidenamino)-1-alkyl-1,2,4-triazolium bromides by A. S. Hotsulia, O. I. Panasenko, T. S. Brytanova

“…A molecular docking method that uses a variety of computational algorithms to predict and analyze interactions, including determining the presence of possible binding sites, estimating binding energies, and the spatial arrangement of molecules. …”
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Public health perspectives on green efficiency through smart cities, artificial intelligence for healthcare and low carbon building materials by Jingjing Sun, Xin Guan, Siqi Yuan, Yalin Guo, Yepei Tan, Yajuan Gao

“…Additionally, the effective use of advanced technologies like convolutional neural networks (CNN) in predicting and promoting low-carbon solutions in construction is gaining attention.MethodsThis study employs a dual approach: (1) A survey of 200 respondents was conducted to gauge public perceptions and attitudes toward smart cities, AI in medicine, and low-carbon building materials. (2) A CNN model was developed and implemented to predict the performance of low-carbon building materials. …”
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Wildlife students as community scientists provide statewide detection data to help non‐invasively monitor Oklahoma bobcats by Nathan J. Proudman, Michelle L. Haynie, Victoria L. Jackson, Jerrod Davis, W. Sue Fairbanks

“…Abstract Community science projects are ideal for collecting species data at large spatial scales and can connect the public with the management and conservation of native wildlife. …”
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A global overview of marine heatwaves in a changing climate by Antonietta Capotondi, Regina R. Rodrigues, Alex Sen Gupta, Jessica A. Benthuysen, Clara Deser, Thomas L. Frölicher, Nicole S. Lovenduski, Dillon J. Amaya, Natacha Le Grix, Tongtong Xu, Juliet Hermes, Neil J. Holbrook, Cristian Martinez-Villalobos, Simona Masina, Mathew Koll Roxy, Amandine Schaeffer, Robert W. Schlegel, Kathryn E. Smith, Chunzai Wang

“…To make progress on predicting and projecting marine heatwaves and their impacts, a more complete mechanistic understanding of these extremes over the full ocean depth and at the relevant spatial and temporal scales is needed, together with models that can realistically capture the leading mechanisms at those scales. …”
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A method for mapping hunting occurrence using publicly available, geographic variables by Conner R. Burke, M. Nils Peterson, David T. Sawyer, Christopher E. Moorman, Christopher Serenari, Krishna Pacifici

“…We describe one approach to address this challenge using spatially explicit logistic regression models that accurately predict whether each parcel of land in North Carolina, USA, was hunted using free and publicly available geographic predictors. …”
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Monitoring Anthropogenically Disturbed Parcels with Soil Erosion Dynamics Change Based on an Improved SegFormer by Zhenqiang Li, Jialin Li, Jie Li, Zhangxuan Li, Kuncheng Jiang, Yuyang Ma, Chuli Hu

“…The PR–MLP module improves feature extraction by capturing spatial contextual information, while the CSBC module enhances boundary prediction through high– and low–level semantic guidance. …”
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Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds by Joarder Jafor Sadique, Imtiaz Nasim, Ahmed S. Ibrahim

“…Furthermore, we introduce a separate model, the convolutional neural network (CNN) over Riemannian manifolds, which captures geometric relationships between satellites and users by extracting spatial features from the network topology across all links. …”
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Optimizing Fertilizer Use in Precision Agriculture with GIS and Remote Sensing by Fallah Mohammed Hussein, Ramadan Ghazi Mohamad, Venkatcharyulu S.

“…In the processing stage, spatial analysis is done to discover nutrient and crop health variations which lead to a fertilizer optimization model that allocates specific rates of fertilizer application. …”
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Quantifying Karenia brevis bloom severity and respiratory irritation impact along the shoreline of Southwest Florida. by Richard P Stumpf, Yizhen Li, Barbara Kirkpatrick, R Wayne Litaker, Katherine A Hubbard, Robert D Currier, Katherine Kohler Harrison, Michelle C Tomlinson

“…These findings highlighted the importance of onshore wind anomalies when predicting respiratory irritation impacts along beaches.…”
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Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India by Srikanth Bhoopathi, Nitish Kumar, Somesh, Manali Pal

“…In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. …”
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Disaster-causing mechanism and prevention and control vision orientation of different types of coal seam floor water disasters in China by Yifan ZENG, Huicong ZHU, Qiang WU, Houzhu WANG, Xianjie FU, Tieji WANG, Xirui WANG, Jiulin FAN, Rongjie HU, Xiangjun CAI, Xuedong KAN, Shengbao GAO

“…On the basis of summarizing the current concept of coal seam floor water disaster detection, prediction and control technology, combined with the frontier development direction, it is pointed out and suggested that the three-dimensional dynamic reconstruction of the whole life cycle mining-induced mutation characteristics of the background geological gene of the coal seam floor, the upgrading of the concept of coal seam floor water inrush prediction and prediction suitable for the dynamic geological environment of space-time differentiation, the application of low disturbance and strong intervention with mining\water control and mutual grouting treatment technology, and the establishment of long-term monitoring, diagnosis and treatment platform for geological ecosystem after restoration should be carried out to build a large system of full-time and space-time prevention and control of coal seam floor water disaster, and keep up with the development of new formats in the whole coal industry under the background of new productivity.…”
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Digital twin and contact analysis of ultra-long distance coiled tubing operation structures [version 2; peer review: 1 approved, 2 approved with reservations] by Wenlan Wei, Jiarui Cheng, Hao Qu, Maliang Wang, Wenyuan Wang

“…Results The research results show that the digital twin coiled tubing and contact algorithm can effectively predict the contact state of coiled tubing operations; the verification model shows that the contact algorithm can accurately analyze the contact state of different well trajectories. …”
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Challenges of COVID-19 Case Forecasting in the US, 2020-2021. by Velma K Lopez, Estee Y Cramer, Robert Pagano, John M Drake, Eamon B O'Dea, Madeline Adee, Turgay Ayer, Jagpreet Chhatwal, Ozden O Dalgic, Mary A Ladd, Benjamin P Linas, Peter P Mueller, Jade Xiao, Johannes Bracher, Alvaro J Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W Zorn, Sen Pei, Jeffrey Shaman, Teresa K Yamana, Samuel R Tarasewicz, Daniel J Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L Hill, Joshua Kaminsky, Elizabeth C Lee, Joseph C Lemaitre, Justin Lessler, Claire P Smith, Shaun Truelove, Matt Kinsey, Luke C Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T Davis, Kunpeng Mu, Ana Pastore Y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav Marathe, Akhil Sai Peddireddy, Przemyslaw Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V Prasad, Jo W Walker, Alexander E Webber, Rachel B Slayton, Matthew Biggerstaff, Nicholas G Reich, Michael A Johansson

“…We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. …”
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