Impact of Underground Space Height and BMI on Children’s Fatigue During Ascending Evacuation: An Experimental Study and Intelligent Assistive Implications by Ming Liu, Hu Zhang, Xin Guo, Yongbo Feng, Xiaochen Zhao, Changzheng Xuan, Xiaohu Jia

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Predictive Mathematical Emergency Information System (EIS) Using GIS, GPS and Digital Photogrammetry (DP) by Abdullah M. Al-Garni

“…Predictive Traffic Response Emergency Information System (PTREIS) was developed based on proven mathematical models. …”
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Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III–IV d-MMR/MSI-H colorectal cancer by Xuan Zhang, Zhenhui Li, Yiwen Zhang, Yanli Li, Xi Zhong, Wenjing Jiang, Xiaobo Chen, Zaiyi Liu, Liebin Huang, Caixia Zhang, Lizhu Liu, Ruimin You, Xiaoping Yi

“…This study aimed to develop an interpretable radiomics model guided by immunophenotypes to predict response to preoperative immunotherapy in CRC, with the goal of enabling more precise and personalized treatment strategies.Methods First, we retrospectively collected 108 patients with CRC from the center who underwent preoperative CT and RNA sequencing. …”
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Machine learning approach for water quality predictions based on multispectral satellite imageries by Vicky Anand, Bakimchandra Oinam, Silke Wieprecht

“…The main objective of this study to retrieve and map the water quality parameters from Sentinel-2 and ResourceSat-2 [Linear Imaging Self-Scanning Sensor (LISS)–IV] multi-spectral satellite data, using Support Vector Machines (SVM), Random Forests (RF), and Multi-Linear regression (MLR) models. This study represents the first attempt to demonstrate the applicability and performance of high-spatial resolution ResourceSat-2 remote sensing satellite's LISS-4 sensor, which operates in three spectral bands in the Visible and Near Infrared Region (VNIR), to predict water quality. …”
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MaxEnt Modeling of Future Habitat Shifts of <i>Itea yunnanensis</i> in China Under Climate Change Scenarios by Jinxin Zhang, Xiaoju Li, Suhang Li, Qiong Yang, Yuan Li, Yangzhou Xiang, Bin Yao

“…The optimized model (RM = 3.0, FC = QHPT) significantly reduced overfitting risk (ΔAICc = 0) and achieved high prediction accuracy (AUC = 0.968). …”
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Temporal–Spatial Partial Differential Equation Modeling of Land Cover Dynamics via Satellite Image Time Series and Sparse Regression by Ming Kang, Zheng Zhang, Zhitao Zhao, Keli Shi, Junfang Zhao, Ping Tang

“…Land cover dynamics play a critical role in understanding environmental changes, but accurately modeling these dynamics remains a challenge due to the complex interactions between temporal and spatial factors. …”
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Pedestrian Trajectory Prediction Based on Transformer and Multi-relation Graph Convolutional Networks by LIU Guihong, ZHOU Zongrun, MENG Xiangfu

“…To address this, a pedestrian trajectory prediction model combining Transformer and multi-relation graph convolutional network (GCN) is proposed. …”
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Spatial Analysis of Rural Architecture Structure in Passive Defense by VIKOR Modeling; Case study: Yaseh Chai Village by Kourosh Momeni, Mostafa Mohebian, Elias Mavedat

“…Based on the results obtained from the VIKOR modeling, the spatial analysis of the architectural and urban structure of Yaseh Chai village is based on non-operational defense criteria, such as "hiding the village's appearance with local materials" and "predicting the stair-shaped form of houses to reduce damage caused by the destruction of houses" as well as "suitable village location based on suitable and fertile soil for agriculture, horticulture and farming to provide for the economic needs of the inhabitants." …”
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Short term prediction of photovoltaic power with time embedding temporal convolutional networks by Jingxin Wang, Guohan Li, Jin Gu, Zhengyi Xu, Xinrong Chen, Jianming Wei

“…Abstract The incorporation of both spatial and temporal characteristics is vital for improving the predictive accuracy of photovoltaic (PV) power generation forecasting. …”
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Scenario-based validation and prediction of land use changes in Birjand watershed in 1404 by Elham Yusefiroobiat, Fatemeh Jahanishakib

“…Then, using the CA-Markov Model, land use changes in 2014 were predicted and modeled. …”
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Dynamic Graph-Based Clustering for Non-Stationary Spatio-Temporal Event Prediction by Vasavi M., Murugan A., Sharma K. Venkatesh

“…Representation of Graph gives us the crime data analysis with location wise and helps us to predict the next occurrence instance. An alternate way of modeling the objects in data sets is to represent those using graphs. …”
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3D long time spatiotemporal convolution for complex transfer sequence prediction by Qiu Yunan, Cui Yingjie, Tang Haibo, Chen Zhongfeng, Lu Zhenyu, Xue Feng

“…However, two challenges still exist in the existing methods: 1) Most of the existing spatio-temporal prediction tasks focus on extracting temporal information using recurrent neural networks and using convolution networks to extract spatial information, but ignore the fact that the forgetting of historical information still exists as the input sequence length increases. 2) Spatio-temporal sequence data have complex non-smoothness in both temporal and spatial, such transient changes are difficult to be captured by existing models, while such changes are often particularly important for the detail reconstruction in the image prediction task. …”
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GMTP: Enhanced Travel Time Prediction with Graph Attention Network and BERT Integration by Ting Liu, Yuan Liu

“…(1) Background: Existing Vehicle travel time prediction applications face challenges in modeling complex road network and handling irregular spatiotemporal traffic state propagation. (2) Methods: To address these issues, we propose a Graph Attention-based Multi-Spatiotemporal Features for Travel Time Prediction (GMTP) model, which integrates an enhanced graph attention network (GATv2) and Bidirectional Encoder Representations from Transformers (BERT) to analyze dynamic correlations across spatial and temporal dimensions. …”
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Overview of Applications and Research Directions of Deep Learning Methods for Wind Power Prediction by LIU Tan, LIU Na, LIU Guiping, LIU Kunjie, LIU Min, ZHUANG Xufei, ZHANG Zhonghao

“…The application of deep learning technology in wind power prediction is reviewed, and on the basis of making a careful division of deep learning technology, it focuses on analyzing the overcome problems and performance by spatial structure-based deep learning models and time-based deep learning models and their related variants, and summarizes the limitations of the proposed modeling methods and the corresponding solutions. …”
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Spatio-temporal transformer and graph convolutional networks based traffic flow prediction by Jin Zhang, Yimin Yang, Xiaoheng Wu, Sen Li

“…To address these issues, a novel deep learning-based traffic flow prediction model, TDMGCN, is proposed. It integrates the Transformer and a multi-graph GCN to tackle the limitations of long-term prediction and the challenges of using the predefined adjacency matrices for spatial correlation extraction. …”
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A method for spatial interpretation of weakly supervised deep learning models in computational pathology by Abhinav Sharma, Bojing Liu, Mattias Rantalainen

“…Such information is also needed for any further spatial interpretation of predictions from such models. …”
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Historical data analysis and future prediction of lung cancer in Zhejiang province, China by Yang Gao, Mengqi Zhang, Guangyuan Liu, Huiyue Lin, Hong Huang, Hongli Xie

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Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN by Haijun Liu, Yan Ma, Huijun Le, Liangchao Li, Rui Zhou, Jian Xiao, Weifeng Shan, Zhongxiu Wu, Yalan Li

“…These models may result in fuzzy prediction results due to neglecting spatial memory, as spatial memory is crucial for capturing the correlations of TEC within the TEC neighborhood. …”
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Prediction and Risk Assessment of Extreme Weather Events Based on Gumbel Copula Function by Peng-Hui Yang, Yao Yu, Feng Gu, Meng-Jie Qu, Jia-Ming Zhu

“…Finally, a wavelet neural network model is constructed to predict the probability of extreme weather events throughout the Americas.…”
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GAN-based image prediction of maize growth across varieties and developmental stages by Xinyi Wang, Shilong Liu, Zhihao Wang, Zedong Geng, Weikun Li, Chengxiu Wu, Yingjie Xiao, Wanneng Yang, Lingfeng Duan

“…Results This article proposed a visualized growth prediction method based on an improved Pix2PixHD network, incorporating spatial attention mechanisms, an improved loss function, and a modified dropout strategy to enhance prediction accuracy and visual fidelity. …”
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