Predicting the Spatial-Temporal Distribution of Human Brucellosis in Europe Based on Convolutional Long Short-Term Memory Network.

Brucellosis is a chronic infectious disease caused by brucellae or other bacteria directly invading human body. Brucellosis presents the aggregation characteristics and periodic law of infectious diseases in temporal and spatial distribution. Taking major European countries as an example, this study established the temporal and spatial distribution sequence of brucellosis, analyzed the temporal and spatial distribution characteristics of brucellosis, and quantitatively predicted its epidemic law by using different traditional or machine learning models.

Discrete Missing Data Imputation Using Multilayer Perceptron and Momentum Gradient Descent.

Data are a strategic resource for industrial production, and an efficient data-mining process will increase productivity. However, there exist many missing values in data collected in real life due to various problems. Because the missing data may reduce productivity, missing value imputation is an important research topic in data mining.

Epidemic characteristics and transmission risk prediction of brucellosis in Xi'an city, Northwest China.

Human brucellosis (HB) has re-emerged in China since the mid-1990s, and exhibited an apparent geographic expansion shifted from the traditional livestock regions to the inland areas of China. It is often neglected in non-traditional epidemic areas, posing a serious threat to public health in big cities. We carried out a retrospective epidemiological study in Xi'an, the largest city in northwestern China.

Spatial and sentiment analysis of public opinion toward COVID-19 pandemic using twitter data: At the early stage of vaccination.

During the crisis of Coronavirus pandemic, social media, like Twitter, have been the platforms on which people have been able to share their opinions and obtain information. The present study provides a detailed spatial-temporal analysis of the Twitter online discourse (approximately 280 thousand tweets) in Ohio and Michigan at the early stage of vaccination rollout (January 2021, till March 2021). This work aims to explore how people were feeling about the pandemic, the most frequent topics people were talking about, and how the topics spatially were distributed.

Coarse sea spray inhibits lightning.

The known effects of thermodynamics and aerosols can well explain the thunderstorm activity over land, but fail over oceans. Here, tracking the full lifecycle of tropical deep convective cloud clusters shows that adding fine aerosols significantly increases the lightning density for a given rainfall amount over both ocean and land. In contrast, adding coarse sea salt (dry radius > 1 μm), known as sea spray, weakens the cloud vigor and lightning by producing fewer but larger cloud drops, which accelerate warm rain at the expense of mixed-phase precipitation.

Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder.

Accurate timely estimation of emissions of nitrogen oxides (NO) is a prerequisite for designing an effective strategy for reducing O and PM pollution. The satellite-based top-down method can provide near-real-time constraints on emissions; however, its efficiency is largely limited by efforts in dealing with the complex emission-concentration response. Here, we propose a novel machine-learning-based method using a physically informed variational autoencoder (VAE) emission predictor to infer NO emissions from satellite-retrieved surface NO concentrations.

[Spatial-temporal Distribution and Evolution Characteristics of Air Pollution in Beijing-Tianjin-Hebei Region Based on Long-term "Ground-Satellite" Data].

This study aimed to promote the coordinated development of regional social economy and ecological environment, build a better living environment, accurately prevent and control pollution, and carry out in-depth surveys and general surveys of air pollution in Beijing, Tianjin, and Hebei. Based on 6 years (June 2014 to December 2019) of ground environmental observation data and satellite data from 2000 to 2019, the distribution characteristics and evolution trend of air pollution in different time and spatial scales were analyzed. The results showed that:① according to the daily average concentration of PM at the sites, the pollution in the Beijing-Tianjin-Hebei region showed the characteristics of more days, heavy levels, and overall improvement.

Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan.

It is significant to explore the morbidity patterns and at-risk areas of the COVID-19 outbreak in megacities. In this paper, we studied the relationship among human activities, morbidity patterns, and at-risk areas in Wuhan City. First, we excavated the activity patterns from Sina Weibo check-in data during the early COVID-19 pandemic stage (December 2019~January 2020) in Wuhan.

Assessing myocardial infarction severity from the urban environment perspective in Wuhan, China.

Health inequalities are globally widespread due to the regional socioeconomic inequalities. Myocardial infarction (MI) is a leading health problem causing deaths worldwide. Yet medical services for it are often inequitably distributed by region.

QUantitative and Automatic Atmospheric Correction (QUAAC): Application and Validation.

The difficulty of atmospheric correction based on a radiative transfer model lies in the acquisition of synchronized atmospheric parameters, especially the aerosol optical depth (AOD). At the moment, there is no fully automatic and high-efficiency atmospheric correction method to make full use of the advantages of geostationary meteorological satellites in large-scale and efficient atmospheric monitoring. Therefore, a QUantitative and Automatic Atmospheric Correction (QUAAC) method is proposed which can efficiently correct high-spatial-resolution (HSR) satellite images.

A method for improving the estimation of extreme air temperature by satellite.

Air temperature (Ta) data obtained from meteorological stations were spatially discontinuous. Some satellite data have complete spatial coverage and strong relationships with Ta (e.g.

Livability assessment of 101,630 communities in China's major cities: A remote sensing perspective.

Unlabelled: Some of China's major cities have entered the middle and late stages of urbanization, and the development focus of these cities has gradually shifted from outward expansion to inward renewal. The community, as the basic unit of a city, is undoubtedly the main object of urban renewal. In order to efficiently and effectively address the problems in current community construction, it is necessary to conduct a large-scale in-depth assessment of the community livability, which can directly imply the satisfaction of residents with their quality of life.

An investigation of traffic density changes inside Wuhan during the COVID-19 epidemic with GF-2 time-series images.

In order to mitigate the spread of COVID-19, Wuhan was the first city to implement strict lockdown policy in 2020. Even though numerous researches have discussed the travel restriction between cities and provinces, few studies focus on the effect of transportation control inside the city due to the lack of the measurement and available data in Wuhan. Since the public transports have been shut down in the beginning of city lockdown, the change of traffic density is a good indicator to reflect the intracity population flow.

Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities.

The lockdown of cities against the COVID-19 epidemic directly decreases urban socioeconomic activities. Remotely sensed night-time light (NTL) provides a macro perspective to capture these variations. Here, taking 20 global megacities as examples, we adopted the NASA's Black Marble NTL data with a daily resolution to investigate their spatio-temporal changes.

From Outbreak to Near Disappearance: How Did Non-pharmaceutical Interventions Against COVID-19 Affect the Transmission of Influenza Virus?

Influenza shares the same putative transmission pathway with coronavirus disease 2019 (COVID-19), and causes tremendous morbidity and mortality annually globally. Since the transmission of COVID-19 in China, a series of non-pharmaceutical interventions (NPIs) against to the disease have been implemented to contain its transmission. Based on the surveillance data of influenza, Search Engine Index, and meteorological factors from 2011 to 2021 in Xi'an, and the different level of emergence responses for COVID-19 from 2020 to 2021, Bayesian Structural Time Series model and interrupted time series analysis were applied to quantitatively assess the impact of NPIs in sequent phases with different intensities, and to estimate the reduction of influenza infections.

The impact of anti-COVID-19 nonpharmaceutical interventions on hand, foot, and mouth disease-A spatiotemporal perspective in Xi'an, northwestern China.

Growing evidence has shown that anti-COVID-19 nonpharmaceutical interventions (NPIs) can support prevention and control of various infectious diseases, including intestinal diseases. However, most studies focused on the short-term mitigating impact and neglected the dynamic impact over time. This study is aimed to investigate the dynamic impact of anti-COVID-19 NPIs on hand, foot, and mouth disease (HFMD) over time in Xi'an City, northwestern China.

Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization.

Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results.

Evaluating water resource carrying capacity using the deep learning method: a case study of Yunnan, Southwest China.

Water resource carrying capacity (WRCC) is an important index for measuring the relations between water resource systems and socio-economic-environmental development. In view of the difficulty in describing the complex and nonlinear relationships between the WRCC and indicators using traditional methods, this study introduces deep learning theory and proposes a novel deep neural network named WRCC-Net for WRCC assessment. Unlike typical network structures, we constructed a hierarchical structure that can indicate the index system in WRCC evaluation.

Monitoring ecosystem services in the Guangdong-Hong Kong-Macao Greater Bay Area based on multi-temporal deep learning.

Assessment of ecosystem service supply and demand, as well as the budgets of ecosystem service supply and demand, is the basis of scientific urban planning. In the 20 years between the proposal and formation of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), the natural ecosystem has been degraded and the ecological balance has been destroyed. In this paper, in order to assess the changes in ecosystem services in the GBA, a deep learning method composed of deep change vector analysis and the ResUnet model is proposed to achieve land use/land cover (LULC) mapping for 2000 and 2020.

Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau.

Grassland is the vegetation type with the widest coverage on the Qinghai-Tibet Plateau. Under the influence of multiple factors, such as global climate change and human activities, grassland is undergoing temporal and spatially different disturbances and changes, and they have a significant impact on the grassland ecosystem of the Qinghai-Tibet Plateau. Therefore, timely and dynamic monitoring of grassland disturbances and distinguishing the reasons for the changes are essential for ecological understanding and management.

The impacts of landscape patterns spatio-temporal changes on land surface temperature from a multi-scale perspective: A case study of the Yangtze River Delta.

Unordered and speedy urbanization is the foremost cause of land surface temperature (LST) rise in an urban area. Understanding the effects of landscape changes on LST is crucial for the urban sustainable development. In this study, we retrieved the LSTs of 26 cities in the Yangtze River Delta Urban Agglomeration with the Landsat images during the summer time (from June to August) of 2000 and 2019.

24-Hour prediction of PM concentrations by combining empirical mode decomposition and bidirectional long short-term memory neural network.

Accurate prediction of the future PM concentration is crucial to human health and ecological environmental protection. Nowadays, deep learning methods show advantages in the prediction of PM concentration, but few of the studies can achieve accurate prediction of short term (within 6 h) concentration and also catch longer term (6-24 h) change trends. To address this issue, this study constructs a novel hybrid prediction model by combining the empirical mode decomposition (EMD) method, sample entropy (SE) index and bidirectional long and short-term memory neural network (BiLSTM) to predict 0-24 h PM concentrations.

Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning.

Background: Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield and causes heavy economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipments.

Extracting Rectified Building Footprints from Traditional Orthophotos: A New Workflow.

Deep learning techniques such as convolutional neural networks have largely improved the performance of building segmentation from remote sensing images. However, the images for building segmentation are often in the form of traditional orthophotos, where the relief displacement would cause non-negligible misalignment between the roof outline and the footprint of a building; such misalignment poses considerable challenges for extracting accurate building footprints, especially for high-rise buildings. Aiming at alleviating this problem, a new workflow is proposed for generating rectified building footprints from traditional orthophotos.

Spatiotemporal association of rapid urbanization and water-body distribution on hemorrhagic fever with renal syndrome: A case study in the city of Xi'an, China.

Hemorrhagic fever with renal syndrome (HFRS) is a zoonosis characterized by clinical features of high fever, hemorrhage, and renal damage. China has the largest number of HFRS cases worldwide, accounting for over 90% of the total reported cases. In this paper, we used surveyed HFRS data and satellite imagery to conduct geostatistical analysis for investigating the associations of rapid urbanization, water bodies, and other factors on the spatiotemporal dynamics of HFRS from year 2005 to 2018 in Xi'an City, Northwest China.