Reconfigurable MISO-VLC via Joint Light Source Identification and Localization Using a Receiver With Spatial LCD Filter by Andrej Harlakin, Jan Mietzner, Peter A. Hoeher

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Spatial Variability in Soil Attributes and Multispectral Indices in a Forage Cactus Field Irrigated with Wastewater in the Brazilian Semiarid Region by Eric Gabriel Fernandez A. da Silva, Thayná Alice Brito Almeida, Raví Emanoel de Melo, Mariana Caroline Gomes de Lima, Lizandra de Barros de Sousa, Jeferson Antônio dos Santos da Silva, Marcos Vinícius da Silva, Abelardo Antônio de Assunção Montenegro

“…Cokriging improved the spatial prediction of EC and TOC by reducing uncertainty and increasing mapping accuracy. …”
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High-resolution population mapping by fusing remote sensing and social sensing data considering the spatial scale mismatch issue by Peijun Feng, Zheng Ma, Jining Yan, Leigang Sun, Nan Wu, Luxiao Cheng, Dongmei Yan

“…Meanwhile, a deep learning model based on transformer feature attention convolution net (TFACNet) is employed to aggregate each geographic unit's global and local spatial relationships, integrating complementary features learned from multi-source heterogeneous data in an end-to-end manner. …”
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Climate change will cause the spatial mismatch between sexually deceptive beetle daisy (Gorteria diffusa, Asteraceae) and its pollinator by Marta Kolanowska

“…This study used ecological niche modelling to investigate the effects of global warming on the spatial overlap between the South African beetle daisy (Gorteria diffusa) and its sole pollen vector (Megapalpus capensis, beetledaisy fly). …”
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A spatial-temporal description of the SARS-CoV-2 infections in Indonesia during the first six months of outbreak. by Dewi Nur Aisyah, Chyntia Aryanti Mayadewi, Haniena Diva, Zisis Kozlakidis, Siswanto, Wiku Adisasmito

“…This is the very first manuscript using a spatial-temporal model to describe the SARS-CoV-2 transmission in Indonesia, as well as providing a patient profile for all confirmed COVID-19 cases.…”
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The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM by Fan Hou, Yue Zhang, Xinli Fu, Lele Jiao, Wen Zheng

“…Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). …”
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Interleukin Family-Based Signature Relates to Cancer-Associated Fibroblasts Spatial Distribution and Immune Therapy Response in Pancreatic Carcinoma by Cheng Y, Xiao S, Li X, Wang B, Lei Y, Sun P, Ma L, Zhu Y

“…Correlation between IL expression pattern, CAF infiltration, and immunotherapy response was evaluated using clinical tissue samples.Results: This study constructed the first IL family expression pattern to predict CAFs infiltration and prognosis in PC. The model was validated using clinical data and a meta-analysis of seven public PC datasets (HR= 1.27). …”
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Identification of common spatial and temporal trends in the epidemiology of cattle bovine tuberculosis and human extrapulmonary and drug-resistant tuberculosis in Malawi by Alfred Ngwira, Samuel Manda, Esron Daniel Karimuribo, Sharadhuli Iddi Kimera

“…Results: Disease specific spatial effects were higher in the southern half of the country, while the shared spatial effects were more dominant in both the south and western parts of the country. …”
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Spatial-temporal Analysis and Assessment of CMIP6 based Climate Simulation over the Qinghai-Xizang （Tibet） Plateau's Hinterland by Chunyu ZHANG, Aili LIU, Yanran LÜ, Tong JIANG, Min SUN

“…The hinterland of the Qinghai-Xizang （Tibet） Plateau is affected by two major circulation systems， Westerly Wind and Indian Ocean monsoon.The average altitude of the region is high， and the terrain is complex and changeable.It is extremely complicated that the temperature and precipitation conditions in this region as a comparison to other areas of the Qinghai-Xizang （Tibet） Plateau.In order to accurately obtain the temporal and spatial changes of temperature and precipitation in this region and predict future temperature and precipitation changes， based on the CN05.1 observation dataset， the ability of CMIP6 data to simulate temperature and precipitation in the hinterland of the Qinghai-Xizang （Tibet） Plateau was evaluated.CMIP6 was corrected using Spatial Disaggregation and Equidistant Cumulative Distribution Functions Method Temperature and precipitation conditions of 5 climate models and 7 scenarios in 2015-2100 were estimated.The results show that： （1） In the historical period （from 1961 to 2014）， the temperature and precipitation observation values of CMIP6 data have little deviation from the simulation values， and have strong space-time correlation.（2） In the future （from 2021 to 2100）， the annual average temperature and precipitation will show an overall upward trend.The percentage of temperature anomaly and precipitation anomaly in 2021-2100 of SSP3-7.0 and SSP5-8.5 scenarios increased significantly.The high value of temperatures anomaly is concentrated in the Qaidam Basin， and the high value of precipitation anomaly is located at the source of the Lancang River in the southeast.（3） In the future， the temperature will continue to increase in the four seasons， the precipitation will also show an overall trend of rise in four seasons.However， the degree of precipitation increase is distinct in different seasons and different scenarios.In the four seasons， the temperature increase of SSP5-8.5 scenario is the largest.The temperature of SSP5-8.5 scenario increases fastest in autumn； The precipitation of SSP3-7.0 scenario increases fastest in summer and winter， while that of SSP5-8.5 scenario increases fastest in spring and autumn.（4） Except for the SSP1-1.9 scenario， the temperature of each scenario from the recent period to the end of the period shows strong temporal and spatial similarity.Against to the historical period， the spatial distribution of temperature in spring and winter showed a consistently rising tendency is similar， and that in summer and autumn is similar.The precipitation increase is the largest in summer and the smallest in winter.Compared with the historical period， the spatial distribution of precipitation anomaly percentage shows a strong seasonality and regional feature.The high value area is mainly distributed in the southeast of the study area.…”
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A Neighborhood Approach for Using Remotely Sensed Data to Estimate Current Ranges for Conservation Assessments by Bethany A. Johnson, Gonzalo E. Pinilla‐Buitrago, Robert P. Anderson

“…ABSTRACT Species distribution modeling can be used to predict environmental suitability, and removing areas currently lacking appropriate vegetation can refine range estimates for conservation assessments. …”
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Spatio-Temporal Data Augmentation Method for Network Traffic Prediction by Sung Oh, Myeong-Jun Oh, Jong-Kyung Im, Ji-Yeon Park, Joung-Sik Kim, Na-Rae Yi, Myung-Ho Kim, Sung-Ho Bae

“…Despite this need, existing studies have largely overlooked data augmentation techniques that simultaneously address spatial and temporal features. Moreover, network traffic data often exhibits localized and granular patterns, meaning that augmented data with significant spatial deviations from the original distribution can undermine structural consistency, leading to severe performance degradation in prediction models. …”
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The Impact of Radiosounding Observations on Numerical Weather Prediction Analyses in the Arctic by T. Naakka, T. Nygård, M. Tjernström, T. Vihma, R. Pirazzini, I. M. Brooks

“…Abstract The radiosounding network in the Arctic, despite being sparse, is a crucial part of the atmospheric observing system for weather prediction and reanalysis. The spatial coverage of the network was evaluated using a numerical weather prediction model, comparing radiosonde observations from Arctic land stations and expeditions in the central Arctic Ocean with operational analyses and background fields (12‐hr forecasts) from European Centre for Medium‐Range Weather Forecasts for January 2016 to September 2018. …”
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Integration of geospatial techniques and machine learning in land parcel prediction by Nekkanti Haripavan, Subhashish Dey, Chimakurthi Harika Mani Chandana

“…Researchers and practitioners can customize their models by choosing the most pertinent variables for each land parcel forecasts from a wide range of spatial features. …”
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Examination of analytical shear stress predictions for coastal dune evolution by O. Cecil, N. Cohn, M. Farthing, S. Dutta, S. Dutta, A. Trautz

“…<p>Existing process-based models for simulating coastal foredune evolution largely use the same analytical approach for estimating wind-induced surface shear stress distributions over spatially variable topography. …”
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Impact of Thermospheric Mass Density on the Orbit Prediction of LEO Satellites by Changyong He, Yang Yang, Brett Carter, Kefei Zhang, Andong Hu, Wang Li, Florent Deleflie, Robert Norman, Suqin Wu

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Prediction of Landslide Susceptibility in the Karakorum under the Context of Climate Change by Yanqian Pei, Haijun Qiu, Yaru Zhu

“…In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. …”
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Crop Yield Prediction: Data Structure and Ai-Powered Methods by V. K. Kalichkin, K. Yu. Maksimovich, O. A. Aleshchenko, V. V. Aleshchenko

“…(Results and discussion) The study presents the core data structure and methods for data acquisition, along with a typical workflow for implementing predictive analytics models for crop yield prediction. …”
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Evidential deep learning-based drug-target interaction prediction by Yanpeng Zhao, Yuting Xing, Yixin Zhang, Yifei Wang, Mengxuan Wan, Duoyun Yi, Chengkun Wu, Shangze Li, Huiyan Xu, Hongyang Zhang, Ziyi Liu, Guowei Zhou, Mengfan Li, Xuanze Wang, Zhengshan Chen, Ruijiang Li, Lianlian Wu, Dongsheng Zhao, Peng Zan, Song He, Xiaochen Bo

“…Through EDL, EviDTI provides uncertainty estimates for its predictions. Experimental results on three benchmark datasets demonstrate the competitiveness of EviDTI against 11 baseline models. …”
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Prediction of Nitrogen Responses of Corn by Soil Nitrogen Mineralization Indicators by R.R. Simard, N. Ziadi, M.C. Nolin, A.N. Cambouris

“…Soil nitrogen mineralization potential (Nmin) has to be spatially quantified to enable farmers to vary N fertilizer rates, optimize crop yields, and minimize N transfer from soils to the environment. …”
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Modeling future projections of land use and land cover in the Kakataibo territory by J. E. Victorio Gonzales, K. Hopfgartner

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