Construction and Preliminary Validation of the Children's Academic Readiness Test to Enter Primary School in Afghanistan Country by Mohammad Naeem Rasooli, Ahmad Abedi, Mohammad Ashori

“…All the general model evaluation indices, in line with the predetermined desirable values, suggest the adequacy of the factorial model for the components of numerical reasoning, attention and memory, picture completion, quantitative reasoning, retrieval, associative recall, sentence repetition and counting, visual-spatial reasoning, problem-solving (connecting dots game), and visual-motor skills. …”
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Decoupling Urban Street Attractiveness: An Ensemble Learning Analysis of Color and Visual Element Contributions by Tao Wu, Zeyin Chen, Siying Li, Peixue Xing, Ruhang Wei, Xi Meng, Jingkai Zhao, Zhiqiang Wu, Renlu Qiao

“…To address this gap, this study employs semantic segmentation and color computation on a massive street-view image dataset encompassing 56 cities worldwide, comparing eight machine learning models in predicting Visual Aesthetic Perception Scores (VAPSs). …”
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Urban subsidence zones prone to flooding: mitigated deformation trends post-2024 Guilin megaflood by Pinglang Kou, Xu Dong, Qiang Xu, Ali P. Yunus, Ying Xia, Yuxiang Tao, Rui Liu, Qin Ji, Ruikun Chen, Zhengwu Yuan

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SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n by Yue Fang, Chenbo Yang, Jie Li, Jingmin Tu

“…The proposed model effectively detects rape flower clusters with minimal computational overhead, offering technical support for yield prediction and elite cultivar selection in rapeseed breeding.…”
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Estimation of Soil Salinity by Combining Spectral and Texture Information from UAV Multispectral Images in the Tarim River Basin, China by Jiaxiang Zhai, Nan Wang, Bifeng Hu, Jianwen Han, Chunhui Feng, Jie Peng, Defang Luo, Zhou Shi

“…Spatial distribution maps of soil salinity were then generated for each model. …”
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AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images by Binyao Wang, Ya’nan Zhou, Weiwei Zhu, Li Feng, Jinke He, Tianjun Wu, Jiancheng Luo, Xin Zhang

“…To improve detection accuracy in these contexts, this study proposes AAMS-YOLO, a YOLO-based farmland parcel detection model. In the feature extraction stage, the model incorporates an Adaptive Mix Attention (AMA) Block, balancing robust feature extraction with low computational overhead through spatial mixing and Efficient Multi-Scale Attention (EMA). …”
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LoadSeer: Exploiting Tensor Graph Convolutional Network for Power Load Forecasting With Spatio-Temporal Characteristics by Jiahao Zhang, Bin Yu, Hanbin Lai, Lin Liu, Jinghui Zhou, Fengliang Lou, Yili Ni, Yan Peng, Ziheng Yu

“…Existing spatio-temporal prediction methods can only handle one factor in each dimension of time and space. …”
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UAV-Based Remote Sensing Monitoring of Maize Growth Using Comprehensive Indices by Tingrui Yang, Jinghua Zhao, Ming Hong, Mingjie Ma, Shijiao Ma, Yingying Yuan

“…SHAP analysis revealed that GNDVI contributed most significantly to the predictive accuracy of the growth model. Spatial imaging distribution of maize inversion using the optimal CGMICT-RF model revealed discernible differences in overall crop growth. …”
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A lightweight mechanism for vision-transformer-based object detection by Yanming Ye, Qiang Sun, Kailong Cheng, Xingfa Shen, Dongjing Wang

“…This design reduces the computational complexity from quadratic to linear while preserving spatial context awareness. XFCOS enhances the original TSP-FCOS (Transformer-based Set Prediction with FCOS) model by integrating XFA into the transformer encoder, creating a CNN-ViT hybrid architecture, significantly reducing computational costs without sacrificing accuracy. …”
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Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence. by Andrea L Araujo Navas, Nicholas A S Hamm, Ricardo J Soares Magalhães, Alfred Stein

“…<h4>Background</h4>Spatial modelling of STH and schistosomiasis epidemiology is now commonplace. …”
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When do citizen scientists record biodiversity? Non‐random temporal patterns of recording effort and associated factors by Inês T. Rosário, Patrícia Tiago, Sergio Chozas, César Capinha

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Biomass distribution law of winter wheat in mining-affected area based on UAV remote sensing by Jing WANG, Wenbing GUO, Zhichao CHEN, Erhu BAI

“…The results show that: ① The selected vegetation indices and texture features were significantly correlated with biomass, and the combination of vegetation indices and texture features as input variables achieved the highest estimation accuracy. The SVR model had the highest prediction accuracy. ② Biomass in regions III (414–661 g/m2) and IV (662–822 g/m2) accounted for 66.4% of the total, indicating that most samples concentrated in the middle and high biomass range. …”
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Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computing by Xiaolu Zhu, Jinglin Li, Zhihan Liu, Fangchun Yang

“…This article proposes a deep learning method to predict car-sharing demand constructed by a stacked auto-encoder model and a logistic regression layer. …”
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Tower-to-global upscaling of terrestrial carbon fluxes driven by MODIS-LAI, Sentinel-3-LAI and ERA5-Land data by Pablo Reyes-Muñoz, Dávid D.Kovács, Jochem Verrelst

“…We applied Gaussian process regression (GPR) models to upscale TCF products from tower-to-global scale and studied the predictive capacity of climate variables and leaf area index (LAI) across biomes (2004–2023). …”
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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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Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

“…The time-distributed vision transformer learns the spatial feature and then feeds the result to the transformer to learn the temporal feature and make the final prediction afterward. …”
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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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The application of suitable sports games for junior high school students based on deep learning and artificial intelligence by Xueyan Ji, Shamsulariffin Bin Samsudin, Muhammad Zarif Bin Hassan, Noor Hamzani Farizan, Yubin Yuan, Wang Chen

“…This study intends to develop a Spatial Temporal-Graph Convolutional Network (ST-GCN) action detection algorithm based on the MediaPipe framework. …”
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Leaf area index-based phenotypic assessment of sweet potato varieties using UAV multispectral imagery and a hybrid retrieval approach by Philemon Tsele, Abel Ramoelo, Lucy Moleleki, Sunette Laurie, Whelma Mphela, Natasha Tshuma

“…However, the BRT performance in-comparison to KRR, captured more spatial variability of observed LAI with a better prediction accuracy across the 20 sweet potato varieties. …”
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Mechanisms and Optimization of Foam Flooding in Heterogeneous Thick Oil Reservoirs: Insights from Large-Scale 2D Sandpack Experiments by Qingchun Meng, Hongmei Wang, Weiyou Yao, Yuyang Han, Xianqiu Chao, Tairan Liang, Yongxian Fang, Wenzhao Sun, Huabin Li

“…Key controlling factors for gas channeling (injection rate, foam quality, permeability contrast) are identified, and a nonlinear predictive model for plugging strength ((<i>S</i> = 0.70<i>C</i>^0.6 <i>k</i>_r^−0.28) (<i>R</i>² = 0.91)) is established. …”
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