Suitable habitat prediction and desertified landscape remediation potential of three medicinal Glycyrrhiza species in China by Fanyan Ma, Xiang Huang, Zhenan Yang, Panxin Niu, Keyao Pang, Mei Wang, Guangming Chu

“…This study employed the MaxEnt model to predict the potential habitats of these three species in China under climate change, and examined the relationship between their distribution and desert ecosystems. …”
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Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel Settlement by Qingmeng Shen, Yuming Wu, Limin Wan, Qian Chen, Yue Li, Zichao Liao, Wenbo Wang, Feng Li, Tao Li, Jiajun Shu

“…The settlement values of subway tunnels during the construction period exhibit significant nonlinear and spatial–temporal variation characteristics. To overcome the problems of historical data interference and spatiotemporal characteristics in tunnel settlement prediction models, this paper proposes a tunnel settlement prediction method based on data decomposition, reconstruction, and optimization. …”
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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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Predicting spatio-temporal dynamics of dengue using INLA (integrated nested laplace approximation) in Yogyakarta, Indonesia by Marko Ferdian Salim, Tri Baskoro Tunggul Satoto, Danardono

“…Its incidence fluctuates due to spatial and temporal factors, necessitating robust modeling approaches for prediction and risk mapping. …”
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Automated Traumatic Bleeding Detection in Whole-Body CT Using 3D Object Detection Model by Rizki Nurfauzi, Ayaka Baba, Taka-aki Nakada, Toshiya Nakaguchi, Yukihiro Nomura

“…In this study, we propose a new automated method for detecting traumatic bleeding in CT images using a three-dimensional object detection model enhanced with an atrous spatial pyramid pooling (ASPP) module. …”
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Spatial and temporal epidemiology of SARS-CoV-2 virus lineages in Teesside, UK, in 2020: effects of socio-economic deprivation, weather, and lockdown on lineage dynamics by Moss, E.D., Rushton, S.P., Baker, P., Bashton, M., Crown, M.R., dos Santos, R.N., Nelson, A., O’Brien, S.J., Richards, Z., Sanderson, R.A., Yew, W.C., Young, G.R., McCann, C.M., Smith, D.L.

“…The relationships between positive tests and covariates varied greatly between lineages, likely due to the strong heterogeneity in their spatial and temporal distributions. Cases during the second wave appeared to be higher in areas that recorded fewer first-wave cases, however, an additional model showed the number of first-wave cases was not predictive of second-wave cases. …”
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Ultrasonic Experimental Evaluation of the Numerical Model of the Internal Fluid Flow in the Kidney Cooling Jacket by Barbara Gambin, Ilona Korczak-Cegielska, Wojciech Secomski, Eleonora Kruglenko, Andrzej Nowicki

“…It was important for justifying the use of numerical modelling in designing the baffles distribution (internal walls in the flow space) for obtaining the most spatially uniform field of flow velocity.…”
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Multi-view fusion of diffusion MRI microstructural models: a preterm birth study by Rosella Trò, Monica Roascio, Domenico Tortora, Mariasavina Severino, Andrea Rossi, Andrea Rossi, Eleftherios Garyfallidis, Gabriele Arnulfo, Gabriele Arnulfo, Marco Massimo Fato, Shreyas Fadnavis

“…Furthermore, we investigated discriminative patterns of preterm birth using multiple analysis methods, drawn from two only seemingly divergent modeling goals, namely inference and prediction. We thus resorted to (i) a traditional univariate voxel-wise inferential method, as the Tract-Based Spatial Statistics (TBSS) approach; (ii) a univariate predictive approach, as the Support Vector Machine (SVM) classification; and (iii) a multivariate predictive Canonical Correlation Analysis (CCA).Main resultsThe TBSS analysis revealed significant differences between preterm and term cohorts in several white matter areas for multiple HARDI features. …”
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Temporal Forecasting with a Bayesian Spatial Predictor: Application to Ozone by Yiping Dou, Nhu D. Le, James V. Zidek

“…One of these approaches adapts a multivariate method originally designed for spatial prediction. The second is based on a state-space modeling approach originally developed and used in a case study involving one week in Mexico City with ten monitoring sites. …”
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Efficient solving of Schrödinger equation using deep convolutional neural network model with an attention mechanism and transfer learning by Ziyi Zhao, Shishun Zhao, Mingjun Zhou, Yujun Yang

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A Global Irradiance Prediction Model Using Convolutional Neural Networks, Wavelet Neural Networks, and Masked Multi-Head Attention Mechanism by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Salah Hannechi

“…However, traditional models struggle to capture the complex spatial and temporal dependencies in irradiance data, limiting prediction accuracy under varying weather conditions. …”
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A multi-dimensional data-driven ship roll prediction model based on VMD-PCA and IDBO-TCN-BiGRU-Attention by Huifeng Wang, Jianchuan Yin, Jianchuan Yin, Nini Wang, Lijun Wang, Lijun Wang

“…As such, the study proposes a combined prediction model. This model integrates data decomposition, dimensionality reduction, deep learning, and optimization techniques.MethodsThe model uses the variational mode decomposition (VMD) method to break down the ship’s roll motion data into components at different scales. …”
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Spatial complex correlation characteristics of carbon emissions and carbon transboundary transfer: Assessment of the carbon footprint in four mega-urban agglomerations in China by Chengxue Yu, Haimeng Lei, Hongbo Zheng, Tong Xu, Zhanfeng Dong

“…This study investigates these issues from the dual perspectives of ''carbon sources'' and ''carbon sinks,'' utilizing a modified gravity model to construct a spatial correlation matrix of carbon footprint pressure. …”
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Multi-omics approach reveals the impact of prognosis model-related genes on the tumor microenvironment in medulloblastoma by Dongming Han, Dongming Han, Xuan Chen, Xuan Chen, Xin Jin, Jiankang Li, Dongyang Wang, Dongyang Wang, Ziwei Wang

“…This study aimed to develop a TME-associated risk score(TMErisk) model using RNA sequencing data to predict patient outcomes and elucidate biological mechanisms.MethodsRNA sequencing data from 322 Tiantan and 763 GSE85217 MB samples were analyzed. …”
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A data-driven reduced-order model for fast prediction of resonant acoustic flow under vertical vibration based on secondary decomposition by Yuqi Gao, Ning Ma, Shifu Zhu, Pengchao Zhang, Hongxing Liu, Zhongyuan Xie

“…Accurate dimensionality reduction models are crucial for constructing real-time computational digital twin systems for process equipment. …”
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Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City by Afsaneh Ghasemi, Jamil Amanollahi

“…Lightning is one of the most severe weather hazards that will cause significant economic, social and environmental damage each year. The prediction of a lightning is a very difficult task due to the spatial and temporal expansion of weather either physically or dynamically. …”
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The impact of transport infrastructure on regional carbon emissions by Jiayun Nie, Jia Shen, Xiaohong Ren

“…An in-depth exploration of the impact mechanisms of transportation infrastructure on carbon emissions is crucial for formulating effective emission reduction policies. Utilizing panel data spanning from 2007 to 2019 for 30 provinces in China, this study employs panel fixed effects models, panel threshold models, and spatial spillover effect models to delve into the influence of transportation infrastructure on regional carbon emissions. …”
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Prediction of Land Use Change and Carbon Storage in Lijiang River Basin Based on InVEST-PLUS Model and SSP-RCP Scenario by Jing Jing, Feili Wei, Hong Jiang, Zhantu Chen, Shuang Lv, Tengfang Li, Weiwei Li, Yi Tang

“…Previous studies have not combined different climate scenarios and land use patterns to predict carbon storage. Using scenarios from both the InVEST-PLUS model and SSP-RCP, combined with multi-source remote sensing data, this study takes the Lijiang River Basin as the study area to explore the dynamic changes in land use and carbon storage under different climate scenarios. …”
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Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm by Kanghui SUN, An XIAO, Houjie XIA

“…In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures， this study establishes a 24-hour national station daily maximum （minimum） temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum （minimum） temperature forecast is consistent with the observed trend， and the annual average forecast is better than that of three numerical models， ECMWF， CMA-SH9 and CMA-GFS， two machine learning products， RF and SVM， and subjective revision products.In terms of the spatial and temporal distribution of forecast errors， the model's daily maximum （minimum） temperature forecast errors in winter and spring are slightly larger than those in summer and autumn； the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north， and larger in the periphery than in the centre"， while the opposite is true for the daily minimum temperatures.In terms of important weather processes， the LightGBM model has the best prediction effect among the seven products in the high temperature process； in the strong cold air process， the LightGBM model is still better than the three numerical model products and the other two machine-learning models， but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process， the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction， and the results can be used as a reference for model improvement and temperature forecast product development.At present， the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.…”
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Short-term displacement prediction for newly established monitoring slopes based on transfer learning by Yuan Tian, Yang-landuo Deng, Ming-zhi Zhang, Xiao Pang, Rui-ping Ma, Jian-xue Zhang

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