Mechanisms and spatial spillover effects of science and technology financial ecosystem on carbon emission reduction from multiple perspectives: evidence from China by Jinyue Zhang, Zhenglin Sun

“…Based on the panel data of 284 prefecture-level cities from 2011 to 2020, the dynamic spatial Durbin model is adopted to examine the spatial spillover effect of the STFE on carbon emission reduction and its influencing mechanism. …”
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Combination of the Improved Diffraction Nonlocal Boundary Condition and Three-Dimensional Wide-Angle Parabolic Equation Decomposition Model for Predicting Radio Wave Propagation by Ruidong Wang, Guizhen Lu, Rongshu Zhang, Weizhang Xu

“…Then we propose a wide-angle three-dimensional parabolic equation (WA-3DPE) decomposition algorithm in which the improved diffraction nonlocal BC is applied and we utilize it to predict the wave propagation problems in the complex environment. …”
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Difference Equation Model-Based PM2.5 Prediction considering the Spatiotemporal Propagation: A Case Study of Bohai Rim Region, China by Ceyu Lei, Xiaoling Han, Chenghua Gao

“…On this basis, we propose a special difference equation model, especially the use of nonlinear diffusion equations to characterize the temporal and spatial dynamic characteristics of PM2.5 propagation between and within clusters for real-time prediction. …”
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Research on a hybrid deep learning model based on two-stage decomposition and an improved whale optimization algorithm for air quality index prediction by Hangyu Zhou, Yongquan Yan

“…A hybrid deep learning model is developed for AQI prediction, incorporating two-stage decomposition and hyperparameter optimization. …”
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Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data by Cesilia Mambile, Shubi Kaijage, Judith Leo

“…This study develops and evaluates advanced Deep Learning (DL) models for FF prediction by integrating spatiotemporal vegetation indices, environmental data, and human activity indicators. …”
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Convolutional Neural Networks—Long Short-Term Memory—Attention: A Novel Model for Wear State Prediction Based on Oil Monitoring Data by Ying Du, Hui Wei, Tao Shao, Shishuai Chen, Jianlei Wang, Chunguo Zhou, Yanchao Zhang

“…However, the complexity of lubricating oil monitoring data often poses challenges in extracting discriminative features, limiting the accuracy of wear state prediction. To address this, a CNN–LSTM–Attention network is specially constructed for predicting wear state, which hierarchically integrates convolutional neural networks (CNNs) for spatial feature extraction, long short-term memory (LSTM) networks for temporal dynamics modeling, and self-attention mechanisms for adaptive feature refinement. …”
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Prediction of sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors by Qing Wang, Ke Shao, Zhibo Cai, Yingpu Che, Haochong Chen, Shunfu Xiao, Ruili Wang, Yaling Liu, Baoguo Li, Yuntao Ma

“…However, traditional methods are constrained by reliance on empirical knowledge, time-consuming processes, resource intensiveness, and spatial-temporal variability in prediction accuracy. …”
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Temperature and Precipitation Assessment and Extreme Climate Events Prediction based on the Coupled Model Intercomparison Project Phase 6 over the Qinghai-Xizang Plateau by Bo FENG, Xianhong MENG, Xianyu YANG, Mingshan DENG, Lin ZHAO, Zhaoguo LI, Lunyu SHANG

“…The Coupled Model Intercomparison Project （CMIP） provides reliable scientific data for predicting ecology， hydrology and climate under the backdrop of global change.However， there are large biases in current climate models， especially on the Qinghai-Xizang Plateau （QXP）.In this study， we employed Detrended Quantile Mapping （DQM） and Quantile Delta Mapping （QDM） methods to correct and evaluate the precipitation and temperature data of eight CMIP6 models with better simulation performance， utilizing the China Meteorological Forcing Dataset （CMFD）.The results showed that Both methods had corrected the simulation biases of the models， and the correction effects for temperature and precipitation data over the QXP were relatively consistent between the two methods.Then， based on the corrected multi-model ensemble mean （MME） results from QDM method， we analyzed the spatial and temporal variation characteristics of extreme high temperature events， low temperature events， atmospheric dryness and precipitation over the QXP in the early 21st century （2015 -2057） and later 21st century （2058-2100）.Under different emission scenarios in the future， extreme high temperature events strengthen， especially in the southeast of the QXP.Extreme high temperature events enhance with the increase of radiation.Extreme low temperature events decrease， with no occurrence in the later 21st century under high emission scenarios （SSP370 and SSP585）.Under different emission scenarios， precipitation and saturated vapor pressure difference both exhibit a significant increasing trend on the QXP.With global warming， the increase of precipitation does not mitigate atmospheric drought.The atmospheric dryness increases significantly under the future scenarios， especially in summer， at 1.3 to 2 times compared to annual average.…”
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A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks by Zhixin Xia, Zhangqi Zheng, Feiyang Wei, Yongshan Liu, Lu Yu

“…Therefore, to effectively utilize the information of the dynamic network and improve the prediction efficiency as well as the prediction accuracy, this paper proposes a spatio-temporal tensor graph neural network model, which learns graph structural features from both spatial and temporal aspects to capture the evolution of the dynamic network. …”
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Modeling of spatial spread of COVID-19 pandemic waves in Russia using a kinetic-advection model by V. V. Aristov, A. V. Stroganov, A. D. Yastrebov

“…This paper studies the development of epidemic events in Russia, starting from the third and including the most recent fifth and sixth waves. Our twoparameter model is based on a kinetic equation. The investigated possibility of predicting the spatial spread of the virus according to the time lag of reaching the peak of infections in Russia as a whole as compared to Moscow is connected with geographical features: in Russia, as in some other countries, the main source of infection can be identified. …”
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Application of a land use regression (LUR) model to the spatial modelling of air pollutants in Esfahan city by Maryam Sharifi Sadeh, Mozhgan Ahmadi Nadoushan

“…Thus, LUR predicts the concentrations of pollution based on surrounding land use and traffic characteristics within circular areas (buffers) as predictors of measured concentrations. …”
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Impacts of Spatial Expansion of Urban and Rural Construction on Typhoon-Directed Economic Losses: Should Land Use Data Be Included in the Assessment? by Siyi Zhou, Zikai Zhao, Jiayue Hu, Fengbao Liu, Kunyuan Zheng

“…Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. …”
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Leveraging Next‐Generation Satellite Remote Sensing‐Based Snow Data to Improve Seasonal Water Supply Predictions in a Practical Machine Learning‐Driven River Forecast System by Sean W. Fleming, Karl Rittger, Catalina M. Oaida Taglialatela, Indrani Graczyk

“…We test a new space‐based remote sensing product, spatially and temporally complete (STC) MODSCAG fractional snow‐covered area (fSCA), as input for the Natural Resources Conservation Service (NRCS) operational US West‐wide WSF system. fSCA data were considered alongside traditional SNOTEL predictors, in both statistical and AI‐based NRCS operational hydrologic models, throughout the forecast season, in four test watersheds (Walker, Wind, Piedra, and Gila Rivers in California, Wyoming, Colorado, and New Mexico). …”
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Advancement of a diagnostic prediction model for spatiotemporal calibration of earth observation data: a case study on projecting forest net primary production in the mid-latitude... by Eunbeen Park, Hyun-Woo Jo, Gregory Scott Biging, Jong Ahn Chun, Seong Woo Jeon, Yowhan Son, Florian Kraxner, Woo-Kyun Lee

“…This study introduced a diagnostic prediction concept as a generalized modeling framework for enhancing modeling precision and interpretability and demonstrate a case study of estimating forest net primary production (NPP) in a mid-latitude region (MLR) by developing a diagnostic NPP diagnostic prediction model (DNPM). …”
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Age, Spatial, and Temporal Variations in Hospital Admissions with Malaria in Kilifi County, Kenya: A 25-Year Longitudinal Observational Study. by Polycarp Mogeni, Thomas N Williams, Gregory Fegan, Christopher Nyundo, Evasius Bauni, Kennedy Mwai, Irene Omedo, Patricia Njuguna, Charles R Newton, Faith Osier, James A Berkley, Laura L Hammitt, Brett Lowe, Gabriel Mwambingu, Ken Awuondo, Neema Mturi, Norbert Peshu, Robert W Snow, Abdisalan Noor, Kevin Marsh, Philip Bejon

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Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan by Ahmed Emara, Sameh A. Kantoush, Mohamed Saber, Tetsuya Sumi, Vahid Nourani, Emad Mabrouk

“…Results indicate that the XGBoost model effectively predicts 2D spatial abrasions in SBTs, achieving an overall accuracy of 0.864, exceeding 0.9 in some sections. …”
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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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Modeling Spatial Distribution of Snow Water Equivalent Using Transfer Learning Across Mountainous Basins by Lama El Halabi, Utkarsh Mital, Dipankar Dwivedi

“…By conducting an exploratory factor analysis, we validated this hypothesis and refined our TL model, which incorporated data based on 80 snowpack maps from California to predict SWE in Colorado. …”
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Spatial Modeling of Yellowfin Tuna in the Banda Sea Based on Oceanographic Factors Using MaxEnt by Sunarwan Asuhadi, Mukti Zainuddin, Safruddin Safruddin, Musbir Musbir

“…This study models the spatial distribution of yellowfin tuna (YFT) in the Banda Sea using the MaxEnt approach, addressing critical questions about its predictive capability, the influence of environmental variables such as sea surface temperature (SST) and chlorophyll-a concentration, and temporal patterns. …”
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Modeling the spatial distribution of African buffalo (Syncerus caffer) in the Kruger National Park, South Africa. by Kristen Hughes, Geoffrey T Fosgate, Christine M Budke, Michael P Ward, Ruth Kerry, Ben Ingram

“…Spatial distribution models were created using buffalo census information and archived data from previous research. …”
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