Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning. by Elizabeth A Cromwell, Joshua C P Osborne, Thomas R Unnasch, Maria-Gloria Basáñez, Katherine M Gass, Kira A Barbre, Elex Hill, Kimberly B Johnson, Katie M Donkers, Shreya Shirude, Chris A Schmidt, Victor Adekanmbi, Olatunji O Adetokunboh, Mohsen Afarideh, Ehsan Ahmadpour, Muktar Beshir Ahmed, Temesgen Yihunie Akalu, Ziyad Al-Aly, Fahad Mashhour Alanezi, Turki M Alanzi, Vahid Alipour, Catalina Liliana Andrei, Fereshteh Ansari, Mustafa Geleto Ansha, Davood Anvari, Seth Christopher Yaw Appiah, Jalal Arabloo, Benjamin F Arnold, Marcel Ausloos, Martin Amogre Ayanore, Atif Amin Baig, Maciej Banach, Aleksandra Barac, Till Winfried Bärnighausen, Mohsen Bayati, Krittika Bhattacharyya, Zulfiqar A Bhutta, Sadia Bibi, Ali Bijani, Somayeh Bohlouli, Mahdi Bohluli, Oliver J Brady, Nicola Luigi Bragazzi, Zahid A Butt, Felix Carvalho, Souranshu Chatterjee, Vijay Kumar Chattu, Soosanna Kumary Chattu, Natalie Maria Cormier, Saad M A Dahlawi, Giovanni Damiani, Farah Daoud, Aso Mohammad Darwesh, Ahmad Daryani, Kebede Deribe, Samath Dhamminda Dharmaratne, Daniel Diaz, Hoa Thi Do, Maysaa El Sayed Zaki, Maha El Tantawi, Demelash Abewa Elemineh, Anwar Faraj, Majid Fasihi Harandi, Yousef Fatahi, Valery L Feigin, Eduarda Fernandes, Nataliya A Foigt, Masoud Foroutan, Richard Charles Franklin, Mohammed Ibrahim Mohialdeen Gubari, Davide Guido, Yuming Guo, Arvin Haj-Mirzaian, Kanaan Hamagharib Abdullah, Samer Hamidi, Claudiu Herteliu, Hagos Degefa de Hidru, Tarig B Higazi, Naznin Hossain, Mehdi Hosseinzadeh, Mowafa Househ, Olayinka Stephen Ilesanmi, Milena D Ilic, Irena M Ilic, Usman Iqbal, Seyed Sina Naghibi Irvani, Ravi Prakash Jha, Farahnaz Joukar, Jacek Jerzy Jozwiak, Zubair Kabir, Leila R Kalankesh, Rohollah Kalhor, Behzad Karami Matin, Salah Eddin Karimi, Amir Kasaeian, Taras Kavetskyy, Gbenga A Kayode, Ali Kazemi Karyani, Abraham Getachew Kelbore, Maryam Keramati, Rovshan Khalilov, Ejaz Ahmad Khan, Md Nuruzzaman Nuruzzaman Khan, Khaled Khatab, Mona M Khater, Neda Kianipour, Kelemu Tilahun Kibret, Yun Jin Kim, Soewarta Kosen, Kris J Krohn, Dian Kusuma, Carlo La Vecchia, Van Charles Lansingh, Paul H Lee, Kate E LeGrand, Shanshan Li, Joshua Longbottom, Hassan Magdy Abd El Razek, Muhammed Magdy Abd El Razek, Afshin Maleki, Abdullah A Mamun, Ali Manafi, Navid Manafi, Mohammad Ali Mansournia, Francisco Rogerlândio Martins-Melo, Mohsen Mazidi, Colm McAlinden, Birhanu Geta Meharie, Walter Mendoza, Endalkachew Worku Mengesha, Desalegn Tadese Mengistu, Seid Tiku Mereta, Tomislav Mestrovic, Ted R Miller, Mohammad Miri, Masoud Moghadaszadeh, Abdollah Mohammadian-Hafshejani, Reza Mohammadpourhodki, Shafiu Mohammed, Salahuddin Mohammed, Masoud Moradi, Rahmatollah Moradzadeh, Paula Moraga, Jonathan F Mosser, Mehdi Naderi, Ahamarshan Jayaraman Nagarajan, Gurudatta Naik, Ionut Negoi, Cuong Tat Nguyen, Huong Lan Thi Nguyen, Trang Huyen Nguyen, Rajan Nikbakhsh, Bogdan Oancea, Tinuke O Olagunju, Andrew T Olagunju, Ahmed Omar Bali, Obinna E Onwujekwe, Adrian Pana, Hadi Pourjafar, Fakher Rahim, Mohammad Hifz Ur Rahman, Priya Rathi, Salman Rawaf, David Laith Rawaf, Reza Rawassizadeh, Serge Resnikoff, Melese Abate Reta, Aziz Rezapour, Enrico Rubagotti, Salvatore Rubino, Ehsan Sadeghi, Abedin Saghafipour, S Mohammad Sajadi, Abdallah M Samy, Rodrigo Sarmiento-Suárez, Monika Sawhney, Megan F Schipp, Amira A Shaheen, Masood Ali Shaikh, Morteza Shamsizadeh, Kiomars Sharafi, Aziz Sheikh, B Suresh Kumar Shetty, B Suresh Kumar Shetty, Jae Il Shin, K M Shivakumar, Biagio Simonetti, Jasvinder A Singh, Eirini Skiadaresi, Amin Soheili, Shahin Soltani, Emma Elizabeth Spurlock, Mu'awiyyah Babale Sufiyan, Takahiro Tabuchi, Leili Tapak, Robert L Thompson, Alan J Thomson, Eugenio Traini, Bach Xuan Tran, Irfan Ullah, Saif Ullah, Chigozie Jesse Uneke, Bhaskaran Unnikrishnan, Olalekan A Uthman, Natalie V S Vinkeles Melchers, Francesco S Violante, Haileab Fekadu Wolde, Tewodros Eshete Wonde, Tomohide Yamada, Sanni Yaya, Vahid Yazdi-Feyzabadi, Paul Yip, Naohiro Yonemoto, Hebat-Allah Salah A Yousof, Chuanhua Yu, Yong Yu, Hasan Yusefzadeh, Leila Zaki, Sojib Bin Zaman, Maryam Zamanian, Zhi-Jiang Zhang, Yunquan Zhang, Arash Ziapour, Simon I Hay, David M Pigott

“…The ROC analysis identified a mean environmental suitability index of 0·71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50·2% exceed this threshold for suitability in at least one 5 × 5-km location. …”
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Mathematical Modelling of the Spatial Epidemiology of COVID-19 with Different Diffusion Coefficients by Benedict Barnes, Ishmael Takyi, Bright Emmanuel Owusu, Francis Ohene Boateng, Augustine Saahene, Emmanuel Saarah Baidoo, Jennifer Aduko Adombire

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Multiphysics property prediction from hyperspectral drill core data by A. V. Kamath, S. T. Thiele, M. Kirsch, R. Gloaguen

“…These findings lay the groundwork for building deep learning models that predict physical and mechanical rock properties from hyperspectral data. …”
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Spatial modelling of vector-borne diseases: Where? When? How many? by Mr Cedric Marsboom

“…Avia-GIS R&D team has an extensive expertise in the spatial modeling of vector-borne diseases (VBDs) to address critical concerns regarding the epidemiology and control of VBDs. …”
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A model of spatially restricted transcription in opposing gradients of activators and repressors by Michael A White, Davis S Parker, Scott Barolo, Barak A Cohen

“…This model quantitatively predicts the boundaries of gene expression within OARGs. …”
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Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model by Jinghang Cai, Hui Chi, Nan Lu, Jin Bian, Hanqing Chen, Junkeng Yu, Suqin Yang

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Latent spectral-spatial diffusion model for single hyperspectral super-resolution by Yingsong Cheng, Yong Ma, Fan Fan, Jiayi Ma, Yuan Yao, Xiaoguang Mei

“…To address these issues, we propose a novel latent spectral-spatial diffusion model (LSDiff) for single hyperspectral SR. …”
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GreenNav: Spatiotemporal Prediction of CO₂ Emissions in Paris Road Traffic Using a Hybrid CNN-LSTM Model by Youssef Mekouar, Imad Saleh, Mohammed Karim

“…By merging their outputs, we leverage both spatial and temporal dependencies, ensuring more accurate predictions. …”
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A hybrid deep learning model for predicting atmospheric corrosion in steel energy structures under maritime conditions based on time-series data by Mohamed El Amine Seghier Ben, Tam T. Truong, Christian Feiler, Daniel Höche

“…By leveraging both the feature extraction strengths of Convolutional layers, which capture spatial hierarchies from input, and the ability of Gated Recurrent Unit (GRU) layers to learn long-term dependencies, the proposed CGRU model can capture both spatial and temporal features of atmospheric corrosion data within time-series signals, resulting in precise predictions. …”
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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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Impact of Urban Expansion on School Quality in Compulsory Education: A Spatio-Temporal Study of Dalian, China by Zhenchao Zhang, Weixin Luan, Chuang Tian, Min Su

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