Improving Discharge Predictions in Ungauged Basins: Harnessing the Power of Disaggregated Data Modeling and Machine Learning by Aggrey Muhebwa, Colin J. Gleason, Dongmei Feng, Jay Taneja

“…Abstract Current machine learning methods for discharge prediction often employ aggregated basin‐wide hydrometeorological data (lumped modeling) for parametric and non‐parametric training. …”
Get full text

MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics by Brett Addison Emery, Xin Hu, Diana Klütsch, Shahrukh Khanzada, Ludvig Larsson, Ionut Dumitru, Jonas Frisén, Joakim Lundeberg, Gerd Kempermann, Hayder Amin

Subjects: Get full text

4D trajectory lightweight prediction algorithm based on knowledge distillation technique by Weizhen Tang, Jie Dai, Zhousheng Huang, Boyang Hao, Weizheng Xie

“…In the distillation process, soft labels from the teacher and hard labels from actual observations jointly guide student trainingResultsIn multi-step prediction experiments, the distilled RCBAM–TCN–LSTM model achieved average reductions of 40%–60% in MAE, RMSE, and MAPE compared with the original RCBAM and TCN–LSTM models, while improving R² by 4%–6%. …”
Get full text

Bifurcation Branch in a Spatial Heterogeneous Predator–Prey Model with a Nonlinear Growth Rate for the Predator by Lei Kong

“…A strongly coupled predator–prey model in a spatially heterogeneous environment with a Holling type-II functional response and a nonlinear growth rate for the predator is considered. …”
Get full text

From maps to models: Key concepts in Geographic Information Systems by Yohannes Shifera Daka, Kassaye Hussein, Ashenafi Yimam

“…These models help predict and analyze spatial dynamics across time by simulating real-world phenomena. …”
Get full text

Predicting Suitable Spatial Distribution Areas for Urban Trees Under Climate Change Scenarios Using Species Distribution Models: A Case Study of <i>Michelia chapensis</i> by Chenbin Shen, Xi Chen, Chao Zhou, Lingzi Xu, Mingyi Qian, Hongbo Zhao, Kun Li

Get full text

Learning behavior aware features across spaces for improved 3D human motion prediction by Ruiya Ji, Chengjie Lu, Zhao Huang, Jianqi Zhong

“…Additionally, we design an Euclidean Kinematic-Aware Extractor utilizing temporal-wise Kinematic-Aware Attention and spatial-wise Kinematic-Aware Feature Extraction. These two modules enhance and complement each other, leading to effective human motion prediction. …”
Get full text

Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies by Zhe Sun, Zhangxing Chen

“…This study systematically reviews advancements in formation damage prediction and diagnostics, focusing on wellsite diagnosis, experimental methods, imaging techniques, analytical approaches, numerical modeling, and artificial intelligence applications. …”
Get full text

CNN-based salient features in HSI image semantic target prediction by Vishal Srivastava, Bhaskar Biswas

Get full text

Spatial-temporal analysis of the international trade network by Donghai Liu, Ziwen Yang, Kun Qin, Kai Li

“…With the support of spatial-temporal data analysis technologies and network science, the International Trade Network (ITN) research has made significant progress, demonstrating broad application prospects in mining market evolution and predicting trade dynamics. …”
Get full text

Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

“…Abstract This study presents a comprehensive hybrid modeling framework that integrates computational fluid dynamics (CFD) with machine learning (ML) techniques to predict chemical concentration distributions during the adsorption of organic compounds onto porous materials. …”
Get full text

COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment by Lili Jiang, Liu Yang, Yang Huang, Yi Wu, Huixian Li, XiYan Shen, Meng Bi, Lin Hong, Yiting Yang, Zuping Ding, Wenjie Chen

Get full text

Spatial prediction and visualization of PM2.5 susceptibility using machine learning optimization in a virtual reality environment by Seyed Vahid Razavi-Termeh, Jalal Safari Bazargani, Abolghasem Sadeghi-Niaraki, X. Angela Yao, Soo-Mi Choi

“…The evaluation results of the VR systems from the Virtual Reality Neuroscience Questionnaire (VRNQ) and System Usability Scale (SUS) for spatial visualization showed that they had high graphics capabilities and equipment for the spatial prediction of PM2.5.…”
Get full text

Spectral Energy Density in an Axisymmetric Galaxy as Predicted by an Analytical Model for the Maxwell Field by Mayeul Arminjon

“…An analytical model for the Maxwell radiation field in an axisymmetric galaxy, proposed previously, is first checked for its predictions of the spatial variation of the spectral energy distributions (SEDs) in our Galaxy. …”
Get full text

Comprehensive propagation of errors for the prediction of woody biomass by Stephen H. Roxburgh, Keryn I. Paul

“…Recommendations for reducing errors in predicted biomass include increasing field survey sample size, adopting field survey designs that ensure spatial representativeness and improving moisture content measurement protocols and increasing the moisture content sample size during allometric model development. …”
Get full text

Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism by Yang Chen, Zeyang Tang, Yibo Cui, Wei Rao, Yiwen Li

“…The experimental results indicate that the proposed model has a more powerful comprehensive ability to capture spatiotemporal relationships and improve accuracy and stability in different prediction steps.…”
Get full text

Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity by Jiashan Li, Xinqi Gong

“…Methods This study leverages a pre-trained model with spatial awareness to enhance the prediction of protein-ligand binding affinity. …”
Get full text

ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data by Xianguang Jia, Weijie Fang, Yingying Lyu, Jinke Zhang, Mengyi Guo, Dong Li, Jie Qu, Fengxiang Guo

“…In this paper, this model is compared with the existing ST-GCN, DMS, GMM, PSAM-CNN, ST-Transformer, and COMD models, and the experimental results show that the predicted values of the ST-GAT model have a significant improvement in the prediction accuracy compared to other models. …”
Get full text

Conditional autoregressive model based on next scale prediction for missing data reconstruction by Shuang Wang, Xiangpeng Wang, Yuhan Yang, Peifan Jiang, Bin Wang, Yuanhao Li

“…Existing transformer-based autoregressive methods flatten two-dimensional seismic data into one-dimensional sequences, disrupting the inherent two-dimensional structure and compromising the spatial locality of seismic information. To address these limitations, we propose a conditional autoregressive model based on next-scale prediction. …”
Get full text

Prediction of quality software quality indicators with applied modifications of integrated gradiates methods by Anton Shantyr, Olha Zinchenko, Kamila Storchak, Andrii Bondarchuk, Yuriy Pepa

Get full text