Using System‐Inspired Metrics to Improve Water Quality Prediction in Stratified Lakes by Kamilla Kurucz, Cayelan C. Carey, Peisheng Huang, Eduardo R. De Sousa, Jeremy T. White, Matthew R. Hipsey

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Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses by Gregorius Airlangga, Julius Bata, Oskar Ika Adi Nugroho, Boby Hartanto Pramudita Lim

“…The hybrid model integrates CNNs for spatial feature extraction and LSTMs for temporal dependency modeling, enhanced by a custom activation function and loss function tailored for the problem’s characteristics. …”
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Spatiotemporal Dynamics and Prediction of Habitat Quality Based on Land Use and Cover Change in Jiangsu, China by Ge Shi, Chuang Chen, Qingci Cao, Jingran Zhang, Jinghai Xu, Yu Chen, Yutong Wang, Jiahang Liu

“…This study utilizes the land use data of Jiangsu Province for the years 2000, 2010, and 2020, applying the FLUS model to investigate the driving force behind land expansion and to simulate a prediction for the land use of 2030. …”
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Spatial heterogeneity and spatial bias analyses in hedonic price models: some practical considerations by Khalid Haniza

“…Estimation of a hedonic price function using Malaysian dataset of agricultural land sale values indicates spatial disaggregation and spatial dependence. However, diagnostic tests and actual estimation of spatial models do not always provide unambiguous conclusions while predicted errors do not vary all that much from those generated by simpler models. …”
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Assessing the ecological risk and its driving forces on Islands using the Pressure-State-Response model by Meina Xu, Xiaochen Huang, Jiajun Li, Shaojun Qi, Ye Zhang, Xiaojie Zhang

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Statistical Analysis of Atmospheric Delay Gradient and Rainfall Prediction in a Tropical Region by Anik Naha Biswas, Yee Hui Lee, Wei Tao Yeo, Wai Chong Low, Ding Yu Heh, Shilpa Manandhar

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Spatial interpolation of cropland soil bulk density by increasing soil samples with filled missing values by Aiwen Li, Jinli Cheng, Dan Chen, Wendan Li, Yaruo Mao, Xinyi Chen, Bin Zhao, Wenjiao Shi, Tianxiang Yue, Qiquan Li

“…However, soil bulk density (BD) data in historical datasets is often incomplete, and it’s uncertain if filled values enhance spatial interpolation accuracy. Using 2,883 cropland soil BD samples from the Sichuan Basin in China, we developed the best prediction models from traditional pedotransfer function (PTF), multiple linear regression (MLR), random forest (RF), and radial basis function neural network (RBFNN) to fill missing BD values for 1,336 samples. …”
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Expanding cryospheric landform inventories – quantitative approaches for underestimated periglacial block- and talus slopes in the Dry Andes of Argentina by Tamara Köhler, Anna Schoch-Baumann, Rainer Bell, Johannes Buckel, Diana Agostina Ortiz, Dario Trombotto Liaudat, Lothar Schrott

“…Random forest models produce robust and transferable predictions of both target landforms, demonstrating a high predictive power (mean AUROC values ≥0.95 using non-spatial validation and ≥0.83 using spatial validation). …”
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Orthogonal intercellular signaling for programmed spatial behavior by Paul K Grant, Neil Dalchau, James R Brown, Fernan Federici, Timothy J Rudge, Boyan Yordanov, Om Patange, Andrew Phillips, Jim Haseloff

“…We used this model to predict optimal expression levels for receiver proteins, to create an effective two‐channel cell communication device. …”
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SPATIALLY INFORMED INSIGHTS: MODELING PERCENTAGE POVERTY IN EAST JAVA PROVINCE USING SEM WITH SPATIAL WEIGHT VARIATIONS by Ashabul Akbar Maulana, Achmad Fauzan

“…Diverse weighting schemes are applied based on both distance (1) and contiguity (2). The optimal predictive model utilized is the Spatial Error Model (SEM) incorporating a Distance Band Weighing (DBW) mechanism with a designated maximum distance ( ) of 75000 meters. …”
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Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction by Sung Jae Kim, Yongbok Cho

“…Using 3D universal kriging, the study interpolates missing HAB concentration values, transforming geospatial point data into spatially continuous grid images that serve as the foundation for predictive modeling. …”
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Spatiotemporal evolution and trend prediction of coupled coordination between digital technology and manufacturing green transformation from provinces in China by Xin Huang, Xin Huang, Hongbing Deng, Hongbing Deng

“…Based on this, this paper adopts the coupling coordination model, kernel density estimation, Dagum Gini coefficient decomposition, and spatial autocorrelation to conduct a spatiotemporal evolution analysis of the coupling coordination degree (the D‐G system) of digital technology and MGT in 30 provinces (municipalities, autonomous regions) of mainland China from 2011 to 2020, and adopting the spatial Markov chain to predict its evolutionary trend. …”
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Artificial Intelligence-Supported Spatial Scanning for Enhanced Real-Time Spectral Analysis of Heterogeneous Media by Bassem Mortada, Samir Abozyd, Bassam Saadany, Yasser M. Sabry, Diaa Khalil, Tarik Bourouina

“…The impact of sample scanning on artificial intelligence-based chemometrics model accuracy is rigorously assessed using heterogeneous feed samples, revealing remarkable enhancements in the accuracy of prediction models. …”
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Bayesian spatio-temporal modeling and prediction of malaria cases in Tanzania mainland (2016-2023): unveiling associations with climate and intervention factors by Lembris Laanyuni Njotto, Wilfred Senyoni, Ottmar Cronie, Anna-Sofie Stensgaard

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Spatial variability and convergence of the coupled relationship between agricultural carbon emission reduction and rural revitalization in China by Hongli Yang, Kai Feng

“…IntroductionPromoting the coupled and coordinated development of agricultural carbon emission reduction and rural revitalization is a key link and an inevitable choice to achieve the goal of “double carbon” and sustainable rural development.MethodsThis study takes 31 provinces (cities) in China (excluding Hong Kong, Macao and Taiwan) from 2010 to 2022 as the research object, and adopts the entropy value method, the coupling coordination degree model, the Gini coefficient and its decomposition, and the convergence degree model, etc., to analyze the level of coupling coordination between agricultural carbon emission reduction and rural revitalization in terms of spatial and temporal development characteristics, regional differences and convergence.Results and discussionThe study found that: (1) the coupling and coordination level of agricultural carbon emission reduction and rural revitalization at the national level and in the four major regions continues to improve, and the type of coupled coordination in the provinces is dominated by “primary coordination” in 2022; (2) inter-regional differences are the main source of differences in the coupling and coordination level of agricultural carbon emission reduction and rural revitalization in China; (3) There is no σ-convergence in the coupled coordination level of agricultural carbon emission reduction and rural revitalization at the national level, but there are significant absolute β-convergence and conditional β-convergence, and there are some differences in the regional convergence characteristics, and there is obvious regional heterogeneity in the development of external factors on the coupled coordination level in different regions. …”
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Analysis of Spatial Differences and Influencing Factors of Carbon-Emission Reduction Efficiency of New-Energy Vehicles in China by Lingyao Wang, Taofeng Wu, Fangrong Ren

“…This study evaluates the carbon-reduction efficiency of NEVs in 21 Chinese provinces using an improved three-stage DEA model, analyzes spatial disparities with the Dagum Gini coefficient, and decomposes carbon-emission factors using the LMDI method. …”
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Revisiting the "satisfaction of spatial restraints" approach of MODELLER for protein homology modeling. by Giacomo Janson, Alessandro Grottesi, Marco Pietrosanto, Gabriele Ausiello, Giulia Guarguaglini, Alessandro Paiardini

“…The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. …”
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Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios by Arifur Rahman Rifath, Md Golam Muktadir, Mahmudul Hasan, Md Ashraful Islam

“…This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. …”
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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

“…Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. …”
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A Temperature Noise Correction Method for CMOS Spatial Camera Using LSTM With Attention Mechanism by Long Cheng, Xueying Wang, Jing Xu

“…This study presents an innovative temperature-induced random noise correction method for complementary metal oxide semiconductor (CMOS) spatial cameras using an attention mechanism-enhanced long short-term memory (LSTM) model. …”
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