A novel predictive model for capturing threats for facilitating effective social distancing in COVID-19

Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.

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 purpose of this research is to propose a knowledge-based strategy to realize grassland dynamic distribution mapping and analysis of grassland disturbance changes in the region that are suitable for the Qinghai-Tibet Plateau. The purpose of this study is to propose an analysis algorithm that uses first annual mapping and then establishes temporal disturbance rules, which is applicable to the integrated exploration of disturbance changes in highland-type grasslands. The characteristic indexes of greenness and disturbance indices in the growing period were constructed and integrated with deep neural network learning to dynamically map the grassland for many years. The overall accuracy of grassland mapping was 94.11% and that of Kappa was 0.845. The results show that the area of grassland increased by 11.18% from 2001 to 2017. Then, the grassland disturbance change analysis method is proposed in monitoring the grassland distribution range, and it is found that the area of grassland with significant disturbance change accounts for 10.86% of the total area of the Qinghai-Tibet Plateau, and the disturbance changes are specifically divided into seven types. Among them, the type of degradation after disturbance mainly occurs in Tibet, whereas the main types of vegetation greenness increase in Qinghai and Gansu. At the same time, the study finds that climate change, altitude, and human grazing activities are the main factors affecting grassland disturbance changes in the Qinghai-Tibet Plateau, and there are spatial differences.

The Effect of Parental Social Integration on the Physical Examination Utilization for Young Migrant Children: A National Cross-Sectional Study in China

Purpose: Physical examination is a key component of child health management. Migrant children are a vulnerable group with lower healthcare service utilization, and this study aims to explore the effect of parental social integration on the physical examination service utilization for young migrant children under 6 years old in China.Method: This study conducted a secondary data analysis of the 2014 National Internal Migrant Dynamic Monitoring Survey in China. A total of 2,620 participants were included in this study. A total of 22 indicators were selected to measure social integration. Multivariate logistic regression was used to explore the association between parental social integration and physical examination use of young migrant children.Results: More than half (66.4%) of the migrant children aged 0–6 years had used free physical examination. Parental social integration, especially structural integration, was associated with the physical examination utilization of migrant children. Specifically, those migrant children's parents who had medical insurance (P < 0.05; OR = 1.29), who had participated in local activities (P < 0.001; OR = 1.98), who had registered local residents as neighbors (P < 0.05; OR = 1.34), and who had a deep sense of self-identity (P < 0.05; OR = 1.09) were more likely to take children to use physical examination.Conclusions: This study provided evidence that parental social integration was associated with migrant children's physical examination utilization, and this association was multifaceted, lying in the dimensions of economic, structural, and psychological integration. Improving the social integration of migrant parents would be effective to enhance the migrant children's healthcare service utilization.

Comparison of Winter Wheat Extraction Methods Based on Different Time Series of Vegetation Indices in the Northeastern Margin of the Qinghai–Tibet Plateau: A Case Study of Minhe, China

The northeastern margin of the Qinghai–Tibet Plateau (QTP) is an agricultural protection area in China’s new development plan, and the primary region of winter wheat growth within QTP. Winter wheat monitoring is critical for understanding grain self-sufficiency, climate change, and sustainable socioeconomic and ecological development in the region. However, due to the complex terrain and high altitude of the region, with discontinuous arable land and the relatively low level of agricultural development, there are no effective localization methodologies for extracting and monitoring the detailed planting distribution information of winter wheat. In this study, Sentinel-2A/B data from 2019 to 2020, obtained through the Google Earth Engine platform, were used to build time series reference curves of vegetation indices in Minhe. Planting distribution information of winter wheat was extracted based on the phenology time-weighted dynamic time warping (PT-DTW) method, and the effects of different vegetation indices’ time series and their corresponding threshold parameters were compared. The results showed that: (1) the three vegetation indices—normalized difference vegetation index (NDVI), normalized differential phenology index (NDPI), and normalized difference greenness index (NDGI)—maintained high mapping potential; (2) under the optimal threshold, >88% accuracy of index identification for winter wheat extraction was achieved; (3) due to improved extraction accuracy and resulting boundary range, NDPI and its corresponding optimal parameter (T = 0.05) performed the best. The process and results of this study have certain reference value for the study of winter wheat planting information change and the formulation of dynamic monitoring schemes in agricultural areas of QTP.

Probing dissolved CO2(aq) in aqueous solutions for CO2 electro-reduction and storage

CO2 dissolved in aqueous solutions is of wide ranging importance from CO2 capture, storage and photo-/electro-reduction in the fight against global warming, to CO2 analysis in various liquids including natural waterbodies and consumer drinking products. Here we developed micro-scale infrared (IR) spectroscopy for in-situ dynamic monitoring and quantitating CO2(aq) in aqueous solutions with high time resolutions under various conditions including CO2 gas bubbling and high pressures. The quantized CO2(g) rotational state transitions were observed to quench when dissolved in water to form CO2(aq) solvated by water molecules, accompanied by increased H2O IR absorption. An accurate CO2 molar extinction coefficient ε was derived for in-situ CO2(aq) quantification up to 58 atm. For the first time, we directly measured CO2(aq) concentrations in electrolytes under CO2(g) bubbling and high pressure conditions. In KHCO3 electrolytes with CO2(aq) > ~ 1 M, CO2 electroreduction (CO2RR) to formate reaches > 98% Faradaic efficiencies on copper (Cu2O/Cu) based electrocatalyst. Further, we probed CO2 dissolution/desolvation kinetics important to energy and environmental applications dynamically, revealing large hysteresis and ultra-slow reversal of CO2(aq) supersaturation in water, with implications to CO2 capture, storage and supersaturation phenomena in natural water bodies.

Production Monitoring of Multilateral Wells by Quantum Marker Systems

Reliable information about the inflow composition and distribution in a multilateral well is of great importance and an existing challenge in the oil and gas industry. In this paper, we present an innovative method for dynamic monitoring of inflow profile based on quantum marker technology in a multi-lateral well located in West Siberia. Marker systems were placed in the well during the well reconstruction by horizontal side tracking with the parent borehole remaining in production. This way of reconstruction allows development of the reservoir drainage area with a lateral hole and bringing the oil reserves from the parent borehole into production, which results in an increased flow rate and improved oil recovery rate. Placement of marker systems into parent borehole and side-track for fluid distribution monitoring allows to evaluate the flow rate from every borehole and estimate the effectiveness of performed well reconstruction. Marker systems are placed into the parent borehole as a downhole sub installed into the well completion string. For the side-track polymer-coated marked proppant was injected during hydraulic fracturing to place markers. The developed method was reliably used for an accurate and fast determination of the inflow distribution in a multi-lateral well which allows more efficient field development and also enabled us to provide effective solutions for following challenges: Providing tools for timely water cut diagnostics in multilateral wells and information for water shut-off method selection; Selecting the optimal well operating mode for effective field development and premature flooding prevention in one or both boreholes; Evaluating whether well construction was performed efficiently, and an increased production rate was achieved; Leading to a considerable economic savings in capital expenditure.

Visualization and monitoring of damaging-healing processes of polymers by AIEgen-loaded multifunctional microcapsules

Polymeric materials play an essential and ubiquitous role in modern societies, but they are inevitably damaged during service, which can lead to compromised performance or even direct failure. The sensitive detection and dynamic monitoring of the health states of polymers is thus crucial to increase their reliability, safety, and lifetime. Herein, a facile fluorescence-based approach that can achieve the nondestructive, on-site, real-time, full-field, and sensitive visualization and monitoring of damaging-healing processes of polymers is demonstrated. By embedding novel UV-blocking microcapsules containing a diisocyanate solution of aggregation-induced emission luminogens (AIEgens) into a polymer matrix, the damaged regions of the composite show turn-on fluorescence and dual signal changes in both fluorescence intensity and fluorescence color can be observed during the healing processes. The invisible information of the static health states and dynamic healing processes can be directly and semi-quantitatively visualized by naked eyes based on the collective effects of AIE and twisted intramolecular charge transfer. In addition to the autonomous damage-reporting, self-healing, and health indication functionalities, the microcapsule-embedded polymeric coatings possess excellent photo- and water-protection capabilities, which are appealing to various practical applications.

Dynamic monitoring and evaluation of ecological restoration in mining area based on GNSS+RS technology

The continuous development of mineral resources is increasingly damaging the ecological environment, so it is of great significance to ecological restoration and dynamic monitoring of the mining area. In this paper, dynamic monitoring and evaluation method of ecological restoration in the mining area are proposed, which integrates GNSS + RS (Global Navigation Satellite System + Remote Sensing) technology. According to the Precipitable Water Vapor (PWV) retrieved by GNSS and NDVI (Normalized Vegetation Index) can monitor the ecological environment and introduce machine learning to improve the accuracy of the model. The dynamic assessment of ecological restoration was carried out by using temperature, rainfall, NPP (Net Primary Productivity), NDVI and PWV. The results show that: (1) the modeling effect of machine learning is better than that of the least square regression. (2) The comprehensive ecological evaluation index proposed can better reflect the ecological situation of the mining area. Therefore, the environmental monitoring and assessment of mining area based on GNNS + RS technology proposed in this paper have important reference significance.