SIAT: Pedestrian trajectory prediction via social interaction-aware transformer by Chengdong Wang, Jianming Wang, Wenbo Gao, Lei Guo

“…The novel model framework establishes a new benchmark for mixed models in trajectory prediction.…”
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In situ and dynamic screening of extracellular vesicles as predictive biomarkers in immune-checkpoint inhibitor therapies by Yihe Wang, Yue Sun, Mengqi Liu, Chao Wang, Miao Huang, Jiaoyan Qiu, Ningkai Yang, Yu Zhang, Hong Liu, Lin Han

“…This platform enables in situ monitoring of EV secretion dynamics under ICI and chemotherapeutic treatments, capturing localized and temporal changes in EV release. Using predictive models, we identified EVs carrying programmed cell death ligand 1 (PD-L1) as the most robust predictors of spheroid viability during treatment. …”
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Urban morphology and climate vulnerability assessment in Kuwait: A spatio-temporal predictive analysis utilizing deep neural network-enhanced markov chain models for 2050 and 2100. by Walid Al-Shaar, Xavier Lehmann, Noha Saad, Gremina Elmazi, Mohamad Al-Shaar, Christelle Tohme

“…Utilizing historical LULC data from 1985, 2005, and 2022, along with various spatial drivers, the research predicts urban expansion patterns for 2050 and 2100. …”
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Random fields and up scaling, towards a more predictive probabilistic quantitative hydrogeology by Noetinger, Benoît

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Comparative convolutional neural networks for perovskite solar cell PCE predictions by Milan Harth, D. Kishore Kumar, Said Kassou, Kenza El Idrissi, Ritesh Kant Gupta, Yonatan Daniel, Ofry Makdasi, Iris Visoly-Fisher, Alessio Gagliardi

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Prediction of Urban Construction Land Carbon Effects (UCLCE) Using BP Neural Network Model: A Case Study of Changxing, Zhejiang Province, China by Qinghua Liao, Xiaoping Zhang, Zixuan Cui, Xunxi Yin

“…The results demonstrate that the BP neural network model effectively predicts the different types of UCLCE, with an average error rate of 30.10%. (1) The total effect and intensity effect exhibit different trends in the study area, and a carbon effect table for different types of UCL is established. (2) The spatial distribution characteristics of UCLCE reveal a distinct reverse-L pattern (“┙”-shaped layout) with positive spatial correlation (Moran’s I = 0.11, <i>p</i> < 0.001). (3) The model’s core practical value lies in enabling forward-looking assessment of carbon effects in urban planning schemes and precise quantification of emissions reduction benefits. …”
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Enhancing the Prediction of Dam Deformations: A Novel Data-Driven Approach by Jonas Ziemer, Gideon Stein, Carolin Wicker, Jannik Jänichen, Daniel Klöpper, Katja Last, Joachim Denzler, Christiane Schmullius, Maha Shadaydeh, Clémence Dubois

“…Second, by incorporating freely available PS time series into deformation prediction, dams can be monitored in higher spatial resolution, making PSI a valuable tool for dam operators. …”
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Hybrid machine learning algorithms accurately predict marine ecological communities by Luciana Erika Yaginuma, Luciana Erika Yaginuma, Fabiane Gallucci, Danilo Cândido Vieira, Paula Foltran Gheller, Simone Brito de Jesus, Thais Navajas Corbisier, Gustavo Fonseca

“…This study aims to predict the spatial distribution of nematode associations from 25 m to 2500 m water depth over an area of 350,000 km² and understand the major oceanographic processes influencing them. …”
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Integrating Higher Education Strategies into Urban Cluster Development: Spatial Agglomeration Analysis of China’s Key Regions by Yangguang Hu, Chuang Yang, Junfeng Ma

“…Using dynamic panel regression and spatial econometric models, the results show that HEA yields significant local and spatial spillover benefits, particularly in core cities that facilitate knowledge diffusion and resource sharing. …”
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A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks by Ye Hong, Yanan Xin, Simon Dirmeier, Fernando Perez-Cruz, Martin Raubal

“…The results demonstrate the effectiveness in producing location sequences with distinct mobility behaviors, thereby facilitating the simulation of diverse yet realistic spatial and temporal changes. These changes result in performance fluctuations in next location prediction networks, revealing impacts of critical mobility behavior factors, including sequential patterns in location transitions, proclivity for exploring new locations, and preferences in location choices at population and individual levels. …”
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Friend Link Prediction Method Based on Heterogeneous Multigraph and Hierarchical Attention by Aoxue Liu, Boyu Li, Yong Wang, Ziteng Yang

“…Key challenges include inadequate modeling of the intricate relationships between users and points of interest (POI), overlooking the significance of spatial-temporal information in user trajectories, and underutilizing rich edge features. …”
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Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction by Xin Zan, Jasmine Siu Lee Lam

“…Urban road networks have complex spatial and temporal correlations, driving a surge of research interest in spatial-temporal traffic flow prediction. …”
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Understanding the impact of covariates for trachoma prevalence prediction using geostatistical methods by Misaki Sasanami, Ibrahim Almou, Adam Nouhou Diori, Ana Bakhtiari, Nassirou Beidou, Donal Bisanzio, Sarah Boyd, Clara R. Burgert-Brucker, Abdou Amza, Katherine Gass, Boubacar Kadri, Fikreab Kebede, Michael P. Masika, Nicholas P. Olobio, Fikre Seife, Abdoul Salam Youssoufou Souley, Amsayaw Tefera, Amir B. Kello, Anthony W. Solomon, Emma M. Harding-Esch, Emanuele Giorgi

“…For each fitted model, we reported estimates of the parameters that quantify the strength of residual spatial correlation and 95% prediction intervals as the measure of uncertainty. …”
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Moisture prediction in chicken litter using hyperspectral data and machine learning by Ahmad Tulsi, Abdul Momin, Victoria Ayres

“…This study addresses that gap by evaluating the feasibility of combining HSI with machine learning models to predict moisture content in chicken litter. …”
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Spatial Weight Matrix Comparison of SAR-X Model using Casetti Approach by Almeira Tsanawafa, Dianne Amor Kusuma, Budi Nurani Ruchjana

“…The Spatial Autoregressive Exogenous (SAR-X) model with the Casetti approach is used to describe the influence of location and exogenous variables in the description and prediction of spatial observations, namely, people's habits and behavior towards culture in Java Island. …”
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Real-time temperature prediction of large-scale lithium battery module driven by data based on few measurement points by Jiajie HAN, Qingyang YUAN, Yu LI, Bo ZHANG, Ke XUE, Tian LAN

“…Accurate real-time temperature prediction in electrochemical energy storage systems plays a critical role in enhancing battery performance, extending lifespan, and preventing thermal runaway, a major safety concern. …”
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Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC by Yang Song, Xiaoming Li, Ying Zheng, Gui Zhang

“…Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. …”
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Temporal and spatial pattern analysis and forecasting of methane: Satellite image processing by Fatima Elshukri, Noor Hussam Abusirriya, Nathan Joseph Braganza, Abdulkarem Amhamed, Odi Fawwaz Alrebei

“…Atmospheric dispersion modeling is a critical tool in environmental research, offering insights into spatial and temporal patterns of pollutants. …”
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Spatial Modeling of Trace Element Concentrations in PM₁₀ Using Generalized Additive Models (GAMs) by Mariacarmela Cusano, Alessandra Gaeta, Raffaele Morelli, Giorgio Cattani, Silvia Canepari, Lorenzo Massimi, Gianluca Leone

“…A stepwise procedure was followed to determine the model with the optimal set of covariates. A leave-one-out cross-validation method was used to estimate the prediction error. …”
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Convolution of the physical point cloud for predicting the self-assembly of colloidal particles by Seunghoon Kang, Young Jin Lee, Kyung Hyun Ahn

“…The phases predicted by our model are not limited to liquid-like dispersions and solid–liquid phase separations, where thermodynamic equilibrium differs, but also include sample-spanning gel structures, where only kinetics differ while thermodynamics remain the same. …”
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