Coarse-graining research of the thermal infrared anomalies before earthquakes in the Sichuan area on Google Earth engine

Seismo-induced Thermal infrared (TIR) anomalies has been proposed as a significant precursor of earthquakes. Several methods have been proposed to detect Thermal infrared anomalies that may be associated with earthquakes. However, there is no comparison of the influence for Thermal infrared extraction methods with a long time statistical analysis. To quantify the effects of various techniques used in Thermal infrared anomaly extraction, in this paper, we offer a complete workflow of their comparative impacts. This study was divided into three parts: anomaly detection, statistical analysis, and tectonic factor research. For anomaly detection, daily continuous nighttime surface temperature (ConLST) data was obtained from the Google Earth Engine (GEE) platform, and each different anomaly detection method was used to detect Thermal infrared outliers in the Sichuan region (27°-37°N, 97°-107°E). During statistical analysis, The heated core model was applied to explore Thermal infrared anomalies which is to filter anomalies unrelated to earthquakes by setting time-space-intensity conditions. The 3D error diagram offers scores to assume the best parameter set using training-test-validation steps. In the final part, we considered information on stresses, active faults, and seismic zones to determine the optimal parameters for extracting the Thermal infrared anomalies. The Kalman filter method detected the highest seismic anomaly frequency without considerating the heating core condition. The Autoencoder and Isolation Forest methods obtain the optimal alert type and parameter set to determine if the anomaly is likely earthquake-related. The RST method performs optimally in the final part of the workflow when it considers physical factors such as active faults, seismic zones, and stresses. However, The six methods we have chosen are not sufficient to contain the entire Thermal infrared anomaly extraction. The consideration of tectonic factors in the research remains poorly developed, as statistical methods were not employed to explore the role of constructive factors. Nevertheless, it is a significant factor in comparing anomaly extraction methods and precursor studies

Using smartphone-based virtual patients to assess the quality of primary healthcare in rural China: protocol for a prospective multicentre study.

INTRODUCTION: Valid and low-cost quality assessment tools examining care quality are not readily available. The unannounced standardised patient (USP), the gold standard for assessing quality, is costly to implement while the validity of clinical vignettes, as a low-cost alternative, has been challenged. Computerised virtual patients (VPs) create high-fidelity and interactive simulations of doctor-patient encounters which can be easily implemented via smartphone at low marginal cost. Our study aims to develop and validate smartphone-based VP as a quality assessment tool for primary care, compared with USP. METHODS AND ANALYSIS: The study will be implemented in primary health centres (PHCs) in rural areas of seven Chinese provinces, and physicians practicing at township health centres and village clinics will be our study population. The development of VPs involves three steps: (1) identifying 10 VP cases that can best represent rural PHCs' work, (2) designing each case by a case-specific development team and (3) developing corresponding quality scoring criteria. After being externally reviewed for content validity, these VP cases will be implemented on a smartphone-based platform and will be tested for feasibility and face validity. This smartphone-based VP tool will then be validated for its criterion validity against USP and its reliability (ie, internal consistency and stability), with 1260 VP/USP-clinician encounters across the seven study provinces for all 10 VP cases. ETHICS AND DISSEMINATION: Sun Yat-sen University: No. 2017-007. Study findings will be published and tools developed will be freely available to low-income and middle-income countries for research purposes

Application of 3D Error Diagram in Thermal Infrared Earthquake Prediction: Qinghai–Tibet Plateau

Earthquakes are the most dangerous natural disasters, and scholars try to predict them to protect lives and property. Recently, a long-term statistical analysis based on a “heating core” filter was applied to explore thermal anomalies related to earthquakes; however, some gaps are still present. Specifically, (1) whether there are differences in thermal anomalies generated by earthquakes of different magnitudes has not yet been discussed; and (2) thermal anomalies in high-spatial-resolution data are often distributed in spots, which is not convenient for statistics of thermal anomalies. To address these issues, in this study, we applied high-spatial-resolution thermal infrared data to explore the performance of the “heating core” for earthquake prediction at different magnitudes (i.e., 3, 3.5, 4, 4.5, and 5). The specific steps were as follows: first, the resampling and moving-window methods were applied to reduce the spatial resolution of the dataset and extract the suspected thermal anomalies; second, the “heating core” filter was used to eliminate thermal noise unrelated to the seismic activity in order to identify potential thermal anomalies; third, the time–distance–magnitude (TDM) windows were used to establish the correspondence between earthquakes and thermal anomalies; finally, the new 3D error diagram (false discovery rate, false negative rate, and space–time correlation window) and the significance test method were applied to investigate the performance under each minimum magnitude with training data, and the robustness was validated using a test dataset. The results show that the following: (1) there is no obvious difference in the thermal anomalies produced by earthquakes of different magnitudes under the conditions of a “heating core”, and (2) the best model with a “heating core” can predict earthquakes effectively within 200 km and within 20 days of thermal anomalies’ appearance. The binary prediction model with a “heating core” based on thermal infrared anomalies can provide some reference for earthquake prediction

Evaluation of Urban Spatial Structure from the Perspective of Socioeconomic Benefits Based on 3D Urban Landscape Measurements: A Case Study of Beijing, China

Urban spatial structures (USS) play an essential role in urbanization. Understanding the impact of USS patterns on their socioeconomic benefits is crucial to evaluating urban structure quality. Previous studies have, primarily, relied on statistical data and have significant temporal consistency and spatial accuracy limitations. Moreover, previous evaluation methods mainly determined the weight of indicators based on subjective assessments, such as the Delphi method, without integrating the actual socioeconomic benefits of complex urban systems. By measuring the two-dimensional (2D) urban functional landscape patterns and three-dimensional (3D) building forms of the city and considering the level of urban socioeconomic vitality as revealed by nighttime light intensity (NTLI), this study explores the influence of urban spatial structure on socioeconomic vitality. It provides a new perspective for evaluating the USS level. Furthermore, a comprehensive index, namely the Spatial Structure Socioeconomic Benefit Index (SSSBI), was constructed to quantify the socioeconomic benefits of USS. The results showed that (1) the impact of spatial structure on NTLI differs significantly with the distribution of urban functional landscape patterns and building forms. (2) The combined effect of any two spatial structure factors on NTLI was higher than the effect of each factor separately, indicating that multiple dimensions can improve urban spatial construction. (3) This study quantitatively extracts the characteristics of USS from multiple scales, which helps to find the optimal evaluation scale and build a scientific and objective evaluation model. The results showed that the USS assessment based on the SSSBI index is practical. This study could provide a reference for the government’s urban planning and land-use decisions

Evaluation of Urban Spatial Structure from the Perspective of Socioeconomic Benefits Based on 3D Urban Landscape Measurements: A Case Study of Beijing, China

Urban spatial structures (USS) play an essential role in urbanization. Understanding the impact of USS patterns on their socioeconomic benefits is crucial to evaluating urban structure quality. Previous studies have, primarily, relied on statistical data and have significant temporal consistency and spatial accuracy limitations. Moreover, previous evaluation methods mainly determined the weight of indicators based on subjective assessments, such as the Delphi method, without integrating the actual socioeconomic benefits of complex urban systems. By measuring the two-dimensional (2D) urban functional landscape patterns and three-dimensional (3D) building forms of the city and considering the level of urban socioeconomic vitality as revealed by nighttime light intensity (NTLI), this study explores the influence of urban spatial structure on socioeconomic vitality. It provides a new perspective for evaluating the USS level. Furthermore, a comprehensive index, namely the Spatial Structure Socioeconomic Benefit Index (SSSBI), was constructed to quantify the socioeconomic benefits of USS. The results showed that (1) the impact of spatial structure on NTLI differs significantly with the distribution of urban functional landscape patterns and building forms. (2) The combined effect of any two spatial structure factors on NTLI was higher than the effect of each factor separately, indicating that multiple dimensions can improve urban spatial construction. (3) This study quantitatively extracts the characteristics of USS from multiple scales, which helps to find the optimal evaluation scale and build a scientific and objective evaluation model. The results showed that the USS assessment based on the SSSBI index is practical. This study could provide a reference for the government’s urban planning and land-use decisions

Preparation of porous biochar from fusarium wilt-infected banana straw for remediation of cadmium pollution in water bodies

Abstract The problem of cadmium pollution and its control is becoming increasingly severe issue in the world. Banana straw is an abundant bio raw material, but its burning or discarding in field not only causes pollution but also spreads fusarium wilt. The objective of this paper is to utilize biochar derived from the wilt-infected banana straw for remediation of Cd(II) pollution while to eliminate the pathogen. The activity of wilt pathogen in biochar was determined by PDA petri dish test. The Cd(II) adsorption of the biochar was determined by batch adsorption experiments. The effects of KOH concentration (0.25, 0.5 and 0.75 M) on the physicochemical characteristics of the biochar were also observed by BET, SEM, FTIR, XRD and XPS. Results showed that pristine banana straw biochar (PBBC) did not harbor any pathogen. The specific surface area (SSA) and Cd(II) adsorption capacity of 0.75 M KOH modified banana straw biochar (MBBC0.75M) were increased by 247.2% and 46.1% compared to that of PBBC, respectively. Cd(II) adsorption by MBBC0.75M was suitable to be described by the pseudo-second-order kinetic model and Freundlich isotherm. After Cd(II) adsorption, the CdCO3 were confirmed by XRD and observed through SEM. The weakness and shift of oxygen-containing functional groups in MBBC0.75M after Cd(II) adsorption implied that those groups were complexed with Cd(II). The results showed that pyrolysis could not only eliminate banana fusarium wilt, but also prepare porous biochar with the wilt-infected banana straw. The porous biochar possessed the potential to adsorb Cd(II) pollutants

Soil Aggregate Construction: Contribution from Functional Soil Amendment Fertilizer Derived from Dolomite

Elastic and water stable macroaggregate are significant to soil structure. which is the base of the soil, to maintain sustainable agriculture. Whether and how functional amendment fertilizer is capable of construction of the macroaggregate is the main purpose of the study. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) were used to investigate the effect of dolomite-based functional soil amendment fertilizers on soil structure. The fertilizers are beneficial to elastic-stable and water-stable aggregate construction. Calcined dolomite based soil amendment functional fertilizer (CDFF) was favorable to water-stable aggregates. The elastic-stable macroaggregate increased with lime, uncalcined dolomite based soil amendment functional fertilizer (UCDFF) and CDFF, and it was 3.0 to 4.2 times the microaggregate. The water-stable one of the CDFF was increased by 20.0%. The mean weight diameter (MWD) of the CDFF and the UCDFF increased by 0.05~0.19 mm, while that of lime only increased by 0.05 mm. The percentage of aggregate dispersion (PAD) of the CDFF was the least. SEM and EDS images revealed that Fe, Al, Si, Ca, Mg, C and O existed on the aggregates. The construction of stable aggregate lies in that the functional fertilizers can gradually neutralize soil H+ and prevent soil colloid dispersion. Soil particles are bounded together to construct micro-agglomerates and then macro-agglomerates through Ca2+, Mg2+ bond bridge and CaCO3, MgCO3 salt bridge and adhesion of SiO2, Fe2O3, Al2O3 as well as the other amorphous substances from the functional fertilizers