Spatiotemporal data modeling and prediction algorithms in intelligent management systems by Xin Cao, Chunxiao Mei, Zhiyong Song, Hao Li, Jingtao Chang, Zhihao Feng

“…The author first makes a preliminary analysis of the wireless network data (mainly the data of cellular mobile networks) obtained by Internet service providers, reveals that the data of adjacent base stations have temporal and spatial correlations, then establishes a hybrid deep learning model for spatio-temporal prediction, and proposes a new spatial model training algorithm. …”
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An Exploration of the Use of Educational Robotics in Preschool Education by Veronica Mihaela Rus, Beatrice Almășan

“…The results show that the use of educational robots in different stages of teaching, learning and evaluation helps in the development process of children by understanding the cause-effect relationship, acquiring spatial and temporal orientation skills, developing communication skills and supporting socio-emotional interactions with children of the same age. …”
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ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data by Xianguang Jia, Weijie Fang, Yingying Lyu, Jinke Zhang, Mengyi Guo, Dong Li, Jie Qu, Fengxiang Guo

“…This model innovatively introduces the graph attention mechanism into the spatio-temporal graph network (ST-GNN), in the spatial dimension, the attention layer (GAL) dynamically learns the attention weights among nodes to adaptively capture the dynamic spatial dependencies in the transportation network, and in the temporal dimension, the temporal convolutional layer extracts the multiscale temporal patterns, which efficiently captures the complex spatiotemporal dependencies in the OD data. …”
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Phenological metrics of the grassland vegetation of Rio Grande do Sul, Brazil by Andreise Moreira, Denise C. Fontana, Tatiana M. Kuplich, Laurindo A. Guasselli

“…ABSTRACT Considering that plant phenology studies allow establishing relationships between phenological patterns of vegetation and changes caused by climate variability, the aim of this study was to obtain phenological metrics for the predominant grassland typologies in Rio Grande do Sul State, Brazil (latitudes 27º 05’ and 33º 45’ S and longitudes 49° 43’ and 57º 39’ W) and to evaluate the spatial-temporal distribution pattern of these metrics under the influence of the subtropical climate variability. …”
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Change of human settlement resilience and its obstacle degree in resource-based cities in the Yellow River Basin by JIANG Haining, ZHENG Shuting, YANG Qi, JIANG Dongru, YU Jianhui

“…The growth-type, mature-type, and regeneration-type cities all showed an agglomeration of high resilience cities, while the decline-type cities with high resilience level tended to be dispersed. (2) From the perspective of spatial evolution, the resilience level of urban human settlements presents a spatial pattern of “periphery is higher than center, downstream is higher than upstream. …”
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Spatiotemporal Land Use Change Detection Through Automated Sampling and Multi-Feature Composite Analysis: A Case Study of the Ebinur Lake Basin by Yi Yang, Liang Zhao, Ya Guo, Shihua Liu, Xiang Qin, Yixiao Li, Xiaoqiong Jiang

“…Based on land use classification results from three different periods, the spatial distribution and pattern changes of major land use types in the region over the past two decades were investigated through analyses of ellipses, centroid shifts, area changes, and transition matrices. …”
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Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning by Bimal Bhattarai, Rohan Kumar Yadav, Hui-Seon Gang, Jae-Young Pyun

“…We present long short-term memory DRNNs for spatial/temporal sequence learning of magnetic patterns and evaluate their positioning performance on our testbed datasets. …”
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Muscles Activity in the elderly with Balance Impairments in walking under Dual tasks by Elaheh Azadian, Hamidreza Taheri Torbati, Alireza saberi Kakhki

“…  Conclusions: Based on the results, it can be argued that walking under a dual task can change spatial-temporal parameters and muscle activity in gait pattern in the elderly with balance impairment. …”
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Spatiotemporal Correlation Analysis for the Incidence of Esophageal and Gastric Cancer From 2010 to 2019: Ecological Study by Zixuan Cui, Chen Suo, Yidan Zhao, Shuo Wang, Ming Zhao, Ruilin Chen, Linyao Lu, Tiejun Zhang, Xingdong Chen

“…We classified different risk areas based on the risk ratio (RR) of the 2 cancers in various countries to the global, and the correlation between their RR was evaluated using Pearson correlation coefficient. Temporal trends were quantified by calculating the average annual percent change (AAPC), and the correlation between the temporal trends of both cancers was evaluated using Pearson correlation coefficients. …”
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The intervention of music education on adolescents’ subjective well-being from the perspective of sustainable development: a bibliometric review based on literature from 2000 to 20... by Lan Shen, Weijia Yang, Weijia Yang

“…The field’s trajectory was deconstructed along three dimensions: temporal (annual publication output and author contribution levels), spatial (national participation and institutional collaboration density), and content (high-frequency keyword clustering and evolution of emerging themes). …”
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Energy-Efficient Fall-Detection System Using LoRa and Hybrid Algorithms by Manny Villa, Eduardo Casilari

“…This study introduces a hybrid system that integrates a threshold-based model for preliminary detection with a deep learning-based approach that combines a CNN (Convolutional Neural Network) for spatial feature extraction with a LSTM (Long Short-Term Memory) model for temporal pattern recognition, aimed at improving classification accuracy. …”
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SD-LSTM: A Dynamic Time Series Model for Predicting the Coupling Coordination of Smart Agro-Rural Development in China by Chunlin Xiong, Yilin Zhang, Weijie Wang

“…The entropy weight method and kernel density estimation are employed to evaluate indicator performance and capture dynamic distribution patterns. …”
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Vegetation Growth Simulation of the Community Land Model in the Southwest China by Lihuan WANG, Yaqiong LÜ, Ziyi WANG

“…Under the background of global warming， the temperature has increased frequently in the southwest， and the ecosystem in the southwest is vulnerable and sensitive to climate change in the past few decades.The southwest region is an important carbon sink area in China.Monitoring and simulation of vegetation variations is of great significance for an in-depth understanding of the carbon cycle mechanism and promoting sustainable economic development.Leaf Area Index （LAI） and Gross Primary Productivity （GPP）， as indicators of vegetation health and ecosystem stability， can be used to quantify vegetation studies and characterize dynamic changes of vegetation.Vegetation dynamic Model is one of the important means to study vegetation growth and change.Community Land Model （CLM） is one of the earliest land model with the function of vegetation dynamic simulation， the most developed and widely used land model in the world.Model evaluation is an indispensable part of model development， which provides a basis for model development and improvement.This study uses the Community Land Model version5 （CLM5） to simulate and analyze the spatial and temporal variations of the leaf area index （LAI） and total primary productivity （GPP） in the southwest region across 2000 -2016， and compare it with multiple sets of remote sensing data to evaluate LAI and GPP simulations of CLM5 in the southwest.The results showed that CLM5 could well simulate the seasonal variation of LAI and GPP in southwest China， but overestimated LAI in growing season.The CLM5 can reasonably simulate LAI of temperate deciduous broadleaf shrubs， LAI， GPP of alpine C3 meadow and GPP of C3 meadow.CLM5 could capture the spatial distribution pattern of LAI and GPP in the southwest， which is decreasing from southeast to northwest， but CLM5 overestimates LAI in the whole southwest region， especially in the karst landform area of Guizhou.Contrary to the overestimation of LAI simulation， CLM5's overall simulation of GPP in Southwest China is low， especially in Yunnan province.In addition， CLM5 has a poor simulation of LAI and GPP trend in the southwest.Especially in most parts of Yunnan， remote sensing data mainly shows an upward trend， while CLM5 simulation shows a downward trend.In a word， CLM5 can simulate the seasonal change and spatial distribution of LAI and GPP in the southwest， but the simulation of the trend in some areas of Yunnan and Guizhou is poor， and more in-depth parametric schemes for the development of farmland in Sichuan Basin， Yunnan forests， and karst vegetation in Guizhou are needed to improve the simulation.…”
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Investigating population genetic structure in a highly mobile marine organism: the minke whale Balaenoptera acutorostrata acutorostrata in the North East Atlantic. by María Quintela, Hans J Skaug, Nils Øien, Tore Haug, Bjørghild B Seliussen, Hiroko K Solvang, Christophe Pampoulie, Naohisa Kanda, Luis A Pastene, Kevin A Glover

“…The primary findings were: (1) No spatial or temporal genetic differentiations were observed for either class of genetic marker. (2) mtDNA identified three distinct mitochondrial lineages without any underlying geographical pattern. (3) Nuclear markers showed evidence of a single panmictic population in the NE Atlantic according STRUCTURE's highest average likelihood found at K = 1. (4) When K = 2 was accepted, based on the Evanno's test, whales were divided into two more or less equally sized groups that showed significant genetic differentiation between them but without any sign of underlying geographic pattern. …”
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Attention-enhanced hybrid CNN–LSTM network with self-adaptive CBAM for COVID-19 diagnosis by Fatin Nabilah Shaari, Aimi Salihah Abdul Nasir, Wan Azani Mustafa, Wan Aireene Wan Ahmed, Abdul Syafiq Abdull Sukor

“…Additionally, this study introduces five pre-trained-LSTM models, leveraging transfer learning to enhance CXR pattern recognition and serving as comparative models for the proposed Dual-Attention CNN-LSTM. …”
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Analysis of the spatiotemporal evolution and driving factors of land use change in Jinan springs area of the Northern Karst region, China from 1986 to 2022 by Shenting Gang, Xiaoyun Kong, Tao Jia, Minghui Lv, Lixia Li

“…A Random Forest model was employed for classifying the remote sensing data, while the land use dynamic and land use transfer matrix were used to analyse the scale, composition, morphology, pattern and spatial and temporal characteristics of land use changes from 1986 to 2022. …”
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Coupled coordination relationship and enhancement path between digital economy and essential public health services in China by Kunyu Chen, Kunyu Chen, Qunshan Tao, Qunshan Tao, Yang Wang, Yang Wang, Zili Ding, Zili Ding, Rui Fu, Rui Fu

“…However, the overall growth remains limited, indicating an urgent need to shift from a “scale expansion” model to a “quality-driven” approach. (2) Spatial Patterns: A distinct regional disparity is observed, characterized by an “eastern leading, central catching up, and western lagging behind” pattern. …”
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Analysis of Economic Vitality and Development Equilibrium of China’s Three Major Urban Agglomerations Based on Nighttime Light Data by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Zhenqing Wang, Zhuochen Wang, Yanchao Wang, Xinran Wang, Luoyao Hao, Futao Wang

“…Nighttime light (NTL) remote sensing data have been proven to be a good proxy variable for socio-economic development, and are widely used due to their advantages of convenient access and wide spatial coverage. Based on multi-source data, this study constructs an Economic Development Index (EDI) that comprehensively reflects regional economic vitality from two aspects, economic quality and development potential, combines the Nighttime Light Development Index (NLDI) as the evaluation indicators to measure the economic vitality and development equilibrium, analyzes the economic vitality and development equilibrium of 300 district and county units in China’s three major urban agglomerations from 2000 to 2020 and their temporal and spatial variation characteristics, and discusses the connotation of EDI and its availability. …”
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DDoS classification of network traffic in software defined networking SDN using a hybrid convolutional and gated recurrent neural network by Ahmed M. Elshewey, Safia Abbas, Ahmed M. Osman, Eman Abdullah Aldakheel, Yasser Fouad

“…The CNN-GRU model integrates a 1D convolutional layer for spatial pattern extraction and a GRU layer for temporal sequence learning, followed by dense layers with dropout regularization. …”
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Artificial intelligence-driven precipitation downscaling and projections over Thailand using CMIP6 climate models by Muhammad Waqas, Usa Wannasingha Humphries

“…Using the MPI-ESM1-2-LR and CAMS-CSM1-0 GCM outputs, DyNN-Mem showed better spatial and temporal characteristics of precipitation, translating into better capturing the precipitation’s spatial and temporal characteristics. …”
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