Spatio-temporal modeling

Driven by the goal of achieving sustainable development and carbon neutrality. Addressing environmental pollution and remediating land damage have become critical challenges in resource-based cities and regions with low land use efficiency. As a response, this study focuses on the 23 provinces where China's coal resource-based cities are situated. Utilizing data from 2014 to 2020, this research employs the SBM-Undesirable model, which considers undesirable outputs in efficiency calculations, and the Tobit regression test...

OBJECTIVE: The aim of this study was to analyze the spatiotemporal evolution of the incidence rates of human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) in the state of Paraná, Brazil. METHODS: An ecological study with an analytical component of time series analysis was conducted in the state of Paraná from 2007 to 2022. The data source was the Notifiable Diseases Information System. To study the trend, the Prais-Winsten generalized linear regression model was used by decomposing the time series, and for spatial analysis, the Moran's index was applied...

BACKGROUND: Resting state (RS) brain activity is inherently non-stationary. Hidden Semi-Markov Models (HsMM) can characterize continuous RS data as a sequence of recurring and distinct brain states along with their spatio-temporal dynamics. NEW METHOD: Recent explorations suggest that HsMM state dynamics in the alpha frequency band link to auditory hallucination proneness (HP) in non-clinical individuals. The present study aimed to replicate these findings to elucidate robust neural correlates of hallucinatory vulnerability...

Training deep learning models for image registration or segmentation of dynamic contrast enhanced (DCE)-MRI data is challenging. This is mainly due to the wide variations in contrast enhancement within and between patients. To train a model effectively, a large dataset is needed, but acquiring it is expensive and time consuming. Instead, style transfer can be used to generate new images from existing images.
 
In this study, our objective is to develop a style transfer method that incorporates spatio-temporal information to either add or remove contrast enhancement from an existing image...

BACKGROUND AND OBJECTIVE: The effective segmentation of esophageal squamous carcinoma lesions in CT scans is significant for auxiliary diagnosis and treatment. However, accurate lesion segmentation is still a challenging task due to the irregular form of the esophagus and small size, the inconsistency of spatio-temporal structure, and low contrast of esophagus and its peripheral tissues in medical images. The objective of this study is to improve the segmentation effect of esophageal squamous cell carcinoma lesions...

INTRODUCTION: Radiographic analysis is necessary for the assessment and the surgical planning in adults with spinal deformity (ASD). Restoration of global alignment is key to improving patient's quality of life. However, the large number of existing global alignment parameters can be confusing for surgeons. RESEARCH QUESTION: To determine the most clinically and functionally relevant global alignment parameters in ASD. MATERIAL AND METHODS: ASD and controls underwent full body biplanar X-ray to calculate global alignment parameters: odontoid to hip axis angle (OD-HA), global sagittal angle (GSA), global tilt (GT), SVA, center of auditory meatus to hip axis (CAM-HA), SSA, T1-tilt and T9-tilt...

BACKGROUND: The association between long-term exposure to ozone (O3 ) and adult-onset asthma (AOA) remains inconclusive, and analysis of causality is lacking. OBJECTIVES: To examine the causal association between long-term O3 exposure and AOA. METHODS: A prospective cohort study of 362,098 participants was conducted using the UK Biobank study. Incident cases of AOA were identified using health administrative data of the National Health Services...

The study proposes a dynamic spatio-temporal profile of the distribution of tuberculosis incidence and air pollution in Romania, where this infectious disease induces more than 8,000 new cases annually. The descriptive analysis for the years 2012-2021 assumes an identification of the structuring patterns of mycobacterium tuberculosis risk in the Romanian population, according to gender and age, exploiting spatial modeling techniques of time series data. Through spatial autocorrelation, the degree of similarity between the analyzed territorial systems was highlighted and the relationships that are built between the analysis units in spatial proximity were investigated...

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform...

With the rapid urbanization and industrialization, heavy metal contamination in urban soil and surface dust has received particular attention due to its negative effects on the eco-environment and human health. Contamination and spatio-temporal characteristics, contamination sources, and source apportionment methods, as well as the ecological and health risks of heavy metals in urban soil and surface dust were reviewed. The knowledge gaps in current research and prospects of future works were proposed. Four key points were presented, including improving the research on the interaction mechanism of heavy metals in urban soil and surface dust under complex conditions, enriching verification methods to improve the source apportionment reliability of anthropogenic metals by receptor models, strengthening the research on chemical forms of heavy metals from different sources and their short-term accumulation processes in surface dust, and raising the credibility of ecological and health risk forecast of heavy metals by integrating the improved exposure parameters and chemical forms...

The biodiversity of an ecosystem is greatly influenced by the spatio-temporal pattern of the landscape. Understanding how landscape type affects habitat quality (HQ) is important for maintaining environmental and ecological sustainability, preserving biodiversity, and guaranteeing ecological health. This research examined the relationship between the HQ and landscape pattern. The study presented an interpretation of the biodiversity variation associated with the landscape pattern in the Zayanderud Dam watershed area by integrating the Land Change Modeler and the InVEST model...

Current malaria elimination targets must withstand a colossal challenge-resistance to the current gold standard antimalarial drug, namely artemisinin derivatives. If artemisinin resistance significantly expands to Africa or India, cases and malaria-related deaths are set to increase substantially. Spatial information on the changing levels of artemisinin resistance in Southeast Asia is therefore critical for health organisations to prioritise malaria control measures, but available data on artemisinin resistance are sparse...

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study...

In order to solve the problem of coordinated development among mining economy, social governance and environmental conservation in global resource-based cities, we choose Guangxi Zhuang Autonomous Region as the research area. The advantage of resource endowment and resource industry was measured by location quotient and input-output method. The panel data related to mining governance from 2010 to 2021 were selected to build the evaluation and coupling analysis model between mining economic, social governance and environmental conservation, and the spatial-temporal heterogeneity and coupling effect of them were analyzed by comprehensive empowerment evaluation, spatial autocorrelation analysis and barrier degree methods...

BACKGROUND: There is an increasing body of evidence associating short-term ambient nitrogen dioxide (NO2 ) exposure with asthma-related hospital admissions in children. However, most studies have relied on temporally resolved exposure information, potentially ignoring the spatial variability of NO2 . We aimed to investigate how daily NO2 estimates from a highly resolved spatio-temporal model are associated with the risk of emergency hospital admission for asthma in children in England...

Worldwide manufacturing industries are significantly affected by COVID-19 pandemic because of their production characteristics with low-cost country sourcing, globalization, and inventory level. To analyze the correlated time series, spatial-temporal model becomes more attractive, and the graph convolution network (GCN) is also commonly used to provide more information to the nodes and its neighbors in the graph. Recently, attention-adjusted graph spatio-temporal network (AGSTN) was proposed to address the problem of pre-defined graph in GCN by combining multi-graph convolution and attention adjustment to learn spatial and temporal correlations over time...

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that sleep oscillations can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear...

UNLABELLED: At the current juncture with climate change, centennial projections of species distributions in biodiversity hotspots, using dynamic vegetation models may provide vital insight into conservation efforts. This study aims to answer: (1) if climate change progresses under a business-as-usual scenario of anthropogenic emissions for this century, how may the forest ranges be affected? (2) will there be potential regional extinctions of the taxa simulated? (3) may any site emerge as a potential refugium? STUDY AREA: Anatolian Peninsula and its surroundings, longitudes 24-50° E, latitudes 33-46° N...

In order to determine the distribution area and amount of Artemisia annua Linn. ( A. annua ) in China, this study estimated the current amount of A. annua specimens based on the field survey sample data obtained from the Fourth National Census of Chinese Medicinal Resources. The amount was calculated using the maximum entropy model (MaxEnt model) and spatio-temporal kriging interpolation. The influencing factors affecting spatial variations in the amount were studied using geographic probes. The results indicated that the amount of A...

We present a novel data set for drought in the continental US (CONUS) built to enable computationally efficient spatio-temporal statistical and probabilistic models of drought. We converted drought data obtained from the widely-used US Drought Monitor (USDM) from its native geo-referenced polygon format to a 0.5 degree regular grid. We merged known environmental drivers of drought, including those obtained from the North American Land Data Assimilation System (NLDAS-2), US Geological Survey (USGS) streamflow data, and National Oceanic and Atmospheric Administration (NOAA) teleconnections data...