Integrated analysis of single-cell, spatial and bulk RNA-sequencing identifies a cell-death signature for predicting the outcomes of head and neck cancer by Yue Pan, Lei Fei, Shihua Wang, Hua Chen, Changqing Jiang, Hong Li, Changsong Wang, Yao Yang, Qinggao Zhang, Yongwen Chen

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Effect of bin width on variogram model accuracy: a case study of teak tree volume specific to Solomon clone in Tawau, Sabah, Malaysia by Johannah Jamalul Kiram, Rossita Mohamad Yunus, Yani Japarudin, Mahadir Lapammu, Olivier Monteuuis, Doreen Kim Soh Goh

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Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies by Zhe Sun, Zhangxing Chen

“…This study systematically reviews advancements in formation damage prediction and diagnostics, focusing on wellsite diagnosis, experimental methods, imaging techniques, analytical approaches, numerical modeling, and artificial intelligence applications. …”
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USING THE SLEUTH MODEL TO SIMULATE FUTURE URBAN GROWTH IN THE GREATER EASTERN ATTICA AREA, GREECE by Stylianos Mathioulakis, Yorgos N. Photis

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CNN-based salient features in HSI image semantic target prediction by Vishal Srivastava, Bhaskar Biswas

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USING THE SLEUTH MODEL TO SIMULATE FUTURE URBAN GROWTH IN THE GREATER EASTERN ATTICA AREA, GREECE by Stylianos Mathioulakis, Yorgos N. Photis

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Spatial and temporal evolution of carbon stocks in Yulin City under changing environments by Guikai Sun, Yadong Li, Rui Huang, Chongxun Mo

“…Abstract Land use changes directly or indirectly affect the regional carbon balance. Investigating the spatial and temporal evolution of regional carbon stock and the contribution of land use driving factors is crucial for understanding the formation mechanisms of ecosystem carbon cycles and carbon budget balance.In this study, the researchers selected the model simulation method after comparing various carbon stock estimation methods. …”
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Enhancing proximal and remote sensing of soil organic carbon: A local modelling approach guided by spectral and spatial similarities by Qi Sun, Pu Shi

“…At large scale, increasing soil heterogeneity complicates the response relationship between soil spectra and SOC, making global models ineffective for local SOC predictions. Here, we propose a local learning approach that searches spectrally and spatially similar samples for site-specific SOC predictive modelling. …”
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Spatial analysis of annual precipitation of Khuzestan province; An approach of spatial regressions analysis by Saeed balyani

“…In this research, for determine of precipitation model and predicting of it with geographical factors e.g. altitude, slope and view shade and latitude- longitude by using spatial regressions analysis such as ordinary least squares (OLS) and geographical weighted regressions(GWR), 13 synoptic stations of Khuzestan province from establishment to 2010 were used. …”
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Evaluating Remote Sensing Resolutions and Machine Learning Methods for Biomass Yield Prediction in Northern Great Plains Pastures by Srinivasagan N. Subhashree, C. Igathinathane, John Hendrickson, David Archer, Mark Liebig, Jonathan Halvorson, Scott Kronberg, David Toledo, Kevin Sedivec

“…The developed methodology of RFE for feature selection and RF for biomass yield modeling is recommended for biomass and hay forage yield prediction.…”
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Survival Prediction of Esophageal Cancer Using 3D CT Imaging: A Context-Aware Approach With Non-Local Feature Aggregation and Graph-Based Spatial Interaction by Fuce Guo, Chen Huang, Shengmei Lin, Yongmei Dai, Qianshun Chen, Shu Zhang, Xunyu XU

“…In the current study, we aimed to develop an effective EC survival risk prediction using only 3D computed tomography (CT) images.The proposed model consists of two essential components: 1) non-local feature aggregation module(NFAM) that integrates visual features from tumor and lymph nodes at both local and global scales, 2) graph-based spatial interaction module(GSIM) that explores the latent contextual interactions between tumors and lymph nodes.The experimental results demonstrate that our model achieves superior performance compared to state-of-the-art survival prediction methods, emphasizing its robust predictive capability. …”
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Evolution and Predictive Analysis of Spatiotemporal Patterns of Habitat Quality in the Turpan–Hami Basin by Yaqian Li, Yongqiang Liu, Yan Qin, Kun Zhang, Reifat Enwer, Weiping Wang, Shuai Yuan

“…Additionally, the InVEST-PLUS coupling model was employed to forecast habitat conditions under three different scenarios in 2050. …”
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Modeling the effects of land use change on agricultural carrying capacity and food security by R. Harini, R. Rijanta, E.H. Pangaribowo, R.F. Putri, I. Sukri

“…Predictions of spatial land changes will reveal changes in land function, carrying capacity and food security between regions.METHODS: Land changes were studied using remote sensing imagery-based mapping methods and spatial simulations using the cellular automata approach. …”
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Enhancing Spatial Ability Assessment: Integrating Problem-Solving Strategies in Object Assembly Tasks Using Multimodal Joint-Hierarchical Cognitive Diagnosis Modeling by Jujia Li, Kaiwen Man, Joni M. Lakin

“…The MJ-DINA model consists of three sub-models: a Deterministic Inputs, Noisy “and” Gate (DINA) model for estimating spatial ability, a lognormal RT model for response time, and a Bayesian Negative Binomial (BNF) model for fixation counts. …”
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Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin by Jeffrey Huang, Simon Elias Bibri, Paul Keel

“…The LFM demonstrates its novelty in comprehensive urban modeling and analysis by completing impartial city data, estimating flow data in new locations, predicting the evolution of flow data, and offering a holistic understanding of urban dynamics and their interconnections. …”
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Improved crop row detection by employing attention-based vision transformers and convolutional neural networks with integrated depth modeling for precise spatial accuracy by Hassan Afzaal, Derek Rude, Aitazaz A. Farooque, Gurjit S. Randhawa, Arnold W. Schumann, Nicholas Krouglicof

“…Incorporating artificial intelligence (AI) within agricultural practices has fundamentally transformed the discipline by facilitating sophisticated data analysis, predictive modeling, and automation. This research presents a novel framework that integrates deep learning, precision agriculture, and depth modeling to detect crop rows and spatial information accurately. …”
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High-Resolution Mapping of Litter and Duff Fuel Loads Using Multispectral Data and Random Forest Modeling by Álvaro Agustín Chávez-Durán, Miguel Olvera-Vargas, Inmaculada Aguado, Blanca Lorena Figueroa-Rangel, Ramón Trucíos-Caciano, Ernesto Alonso Rubio-Camacho, Jaqueline Xelhuantzi-Carmona, Mariano García

“…Our modeling approach allows us to estimate the continuous high-resolution spatial distribution of litter and duff fuel loads, aligned with their ecological context, which dictates their dynamics and spatial variability. …”
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Generating a 30 m Hourly Land Surface Temperatures Based on Spatial Fusion Model and Machine Learning Algorithm by Qin Su, Yuan Yao, Cheng Chen, Bo Chen

“…In this study, focusing on Chengdu city, a framework combining a spatiotemporal fusion model and machine learning algorithm was proposed and applied to retrieve hourly high spatial resolution LST data from Chinese geostationary weather satellite data and multi-scale polar-orbiting satellite observations. …”
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Spatial Evaluation of <i>Salurnis marginella</i> Occurrence According to Climate Change Using Multiple Species Distribution Models by Jae-Woo Song, Jaho Seo, Wang-Hee Lee

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Geometric Investigation of Al-Wind Dam Reservoir Northeastern Iraq, using Digital Elevation Models and Spatial Analyses System by Sabbar A. Saleh, Iktifaa T. Abdul Qadir, Amin M. Ibrahim, Huda M. Hussain

“… Geometric analysis of Al-Wind dam reservoir in Diyala discussed in this paper as necessary and strategic subject, spatial analysis systems were used to extract the area of Al-Wind dam reservoir from the digital elevations model (DEM), at 26 selected water levels in the reservoir with one meter interval, from 195 up to 219.5 m.a.s.l., the geometric criteria used to ​​extract the essential negative geometric elements represented by the Negative Volume (NV) Negative Planner Area (NPA) and Negative Surface Area (NSA), the perimeter of water body, the depth of water column and the shape factor of the reservoir, as well as for the positive geometric elements as Positive Volume (PV), Positive Planner Area (PPA) and Positive Surface Area (PSA) of the islands within the perimeter of the reservoir. …”
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