Robust Driving Control Design for Precise Positional Motions of Permanent Magnet Synchronous Motor Driven Rotary Machines with Position-Dependent Periodic Disturbances by Syh-Shiuh Yeh, Zhi-Hong Liu

“…A position-dependent periodic disturbance model was developed using multiple position–sinusoidal signals and identified using a spatial fast Fourier transform. …”
Get full text

MySTOCKS: Multi-Modal Yield eSTimation System of in-prOmotion Commercial Key-ProductS by Cettina Giaconia, Aziz Chamas

“…Unlike traditional approaches, MySTOCKS leverages an advanced deep learning framework that incorporates transformer models with self-attention mechanisms and domain adaptation capabilities, enabling accurate temporal and spatial modeling tailored to the dynamic requirements of the agrifood supply chain. …”
Get full text

Soil moisture forecasting in wireless sensor networks via spatiotemporal graph convolutional networks by Weixuan Wang, Yating Wei, Longfei Hao, Zushuai Wei, Tianjie Zhao

“…However, due to the combined effects of internal factors like soil types, terrain, and vegetation cover, as well as external factors such as precipitation and temperature, soil moisture data in wireless sensor networks exhibit complex spatial and temporal interdependencies. Consequently, developing predictive models that can accurately capture these dependencies and enable precise forecasts of soil moisture within these networks poses a significant challenge. …”
Get full text

RMIS-Net: a fast medical image segmentation network based on multilayer perceptron by Binbin Zhang, Guoliang Xu, Yiying Xing, Nanjie Li, Deguang Li

“…To address the persistent challenges of computational complexity and efficiency limitations in existing methods, we propose RMIS-Net—an innovative lightweight segmentation network with three core components: a convolutional layer for preliminary feature extraction, a shift-based fully connected layer for parameter-efficient spatial modeling, and a tokenized multilayer perceptron for global context capture. …”
Get full text

Susceptibility and Remanent Magnetization Estimates from Orientation Tools in Borehole Imaging Logs by Julio Cesar S. O. Lyrio, Ana Patrícia C. C. Laier, Jorge Campos Junior, Ana Natalia G. Rodrigues, Luciano dos Santos Martins

“…This promising result shows the importance of our methodology in providing reliable information that can minimize uncertainties in forward modeling of magnetic data, which contributes to reduction of hydrocarbon exploration risks. …”
Get full text

An Optimally Oriented Coherence Attribute Method and Its Application to Faults and Fracture Sets Detection in Carbonate Reservoirs by Shuai Chen, Shengjun Li, Qi Ma, Lu Qin, Sanyi Yuan

“…Overall, the OOCA method offers a geologically consistent and computationally efficient solution for high-resolution fault interpretation and drilling risk prediction in structurally complex carbonate reservoirs.…”
Get full text

Ecological Zoning Based on Suitability Evaluation of Ecological Product Development from the Value-Risk-Cost-Demand Perspective by Ming Gao, Pei Du, Xinxin Zhou, Zhenxia Liu, Wen Luo, Zhaoyuan Yu, Linwang Yuan

“…The findings suggest that the integrated assessment model proposed in this study has produced more reasonable outcomes in terms of spatial layout, land use area, reduction of fragmentation and ecological risk. …”
Get full text

Evaluation of Urban Street Historical Appearance Integrity Based on Street View Images and Transfer Learning by Jiarui Xu, Yunxuan Dai, Jiatong Cai, Haoliang Qian, Zimu Peng, Teng Zhong

“…An Elo rating system was subsequently applied to quantify the scores of the indicators. A prediction model was developed based on transfer learning and feature fusion to estimate the scores of the indicators. …”
Get full text

Spatiotemporal Wind Energy Forecasting: A Comprehensive Survey and a Deep Equilibrium-Based Case Study With StemGNN by Luiza Scapinello Aquino, Laio Oriel Seman, Viviana Cocco Mariani, Leandro Dos Santos Coelho, Stefano Frizzo Stefenon, Gabriel Villarrubia Gonzalez

“…Results demonstrate that the proposed GNN with DEQ effectively models both spatial and temporal dynamics for Seq2Seq data, improving prediction accuracy while maintaining computational efficiency. …”
Get full text

Assessing the risk of traffic accidents in lisbon using a gradient boosting algorithm with a hybrid classification/regression approach by Nuno Alpalhão, Pedro Sarmento, Bruno Jardim, Miguel de Castro Neto

“…Effective emergency response planning requires sophisticated risk prediction tools with precise spatial and temporal resolution. …”
Get full text

GTN-GCN: Real-Time Traffic Forecasting Using Graph Convolutional Network and Transformer by Sadia Naj Jinia, Sumaiya Binte Azad, Rima Akter, Taivan Reza Dipto, Md. Khaliluzzaman

“…For longer prediction horizons (30, 45, and 60 min), the model consistently outperformed baseline methods, maintaining competitive RMSE and MAPE values. …”
Get full text

Progress and Challenges in Morphodynamics of Braided Rivers in Source Region of Yangtze River by LI Zhi-wei, OUYANG Wei-qi

“…[Conclusions] Overall, future research on braided rivers urgently needs to shift from qualitative understanding to quantitative prediction. An integrated research framework that combines process analysis, threshold identification, and evolutionary trend modeling should be established. …”
Get full text

Structural fluctuations of the Arctic Oscillation tied to the Atlantic Multidecadal Oscillation by Hainan Gong, Kangjie Ma, Bo Liu, Judah Cohen, Lin Wang

“…Understanding the drivers and mechanisms behind the AO’s spatial nonstationarity is critical for improving climate predictions related to the AO. …”
Get full text

Experimental Study of the Air Demand of a Spillway Tunnel with Multiple Air Vents by Hao Yang, Qiang Fan, Zhong Tian, Wei Wang

“…The resulting predictive model achieves ±25% agreement with two prototype case studies and model tests. …”
Get full text

Scale-dependent effects of tree species diversity on soil erosion resistance by Huaqing Liu, Xiaodong Gao, Xining Zhao

“…However, the understanding of the effects of tree species diversity on soil erodibility across spatial scales remains incomplete. This study employed the Universal Soil Loss Equation model to quantify soil erodibility and aligned it with tree species diversity data obtained from the Global Forest Biodiversity Initiative database. …”
Get full text

Artificial intelligence-powered precision: Unveiling the tumor microenvironment for a new frontier in personalized cancer therapy by Songwei Feng, Xia Yin, Yang Shen

“…Artificial intelligence (AI) provides unparalleled capabilities to analyze and decipher the complexities of the TME through multi-omics integration, spatial modeling, and predictive analytics. By combining computational innovations with clinical insights, AI is driving a new paradigm in precision medicine. …”
Get full text

Asthma prevalence and risk factors in Poonch and Rajouri districts, India: an epidemiological and geospatial analysis by Arshad Ahmed, Kheraj, Jorge Rocha, Alireza Mohammadi

“…The model exhibited strong fit (Nagelkerke R2 = 0.82) and high predictive accuracy (ROC-AUC = 0.98). …”
Get full text

Remote sensing techniques to assess badlands dynamics: insights from a systematic review by Rosa Colacicco, Marco La Salandra, Isabella Lapietra, Alberto Refice, Domenico Capolongo

“…Satellite imagery is a leading tool due to its extensive spatial and temporal coverage, enhancing land-use change monitoring and erosion modeling capabilities. …”
Get full text

Fusion of Remotely Sensed Data with Monitoring Well Measurements for Groundwater Level Management by César de Oliveira Ferreira Silva, Rodrigo Lilla Manzione, Epitácio Pedro da Silva Neto, Ulisses Alencar Bezerra, John Elton Cunha

“…In the realm of hydrological engineering, integrating extensive geospatial raster data from remote sensing (Big Data) with sparse field measurements offers a promising approach to improve prediction accuracy in groundwater studies. In this study, we integrated multisource data by applying the LMC to model the spatial relationships of variables and then utilized block support regularization with collocated block cokriging (CBCK) to enhance our predictions. …”
Get full text

Distribution and Habitat Suitability of the Malabar Slender Loris (<i>Loris lydekkerianus malabaricus</i>) in the Aralam Wildlife Sanctuary, India by Smitha D. Gnanaolivu, Joseph J. Erinjery, Marco Campera, Mewa Singh

“…Projections for 2070 under RCP 2.6, 4.5, and 8.5 scenarios predict habitat reductions of 52%, 13%, and 8%, respectively, signaling significant vulnerability under changing climatic conditions. …”
Get full text