DEFORMATION MONITORING OF LARGE STRUCTURES BY GROUND-BASED SAR INTERFEROMETRY

In this paper, a ground-based SAR interferometry technology was used to monitor major engineering. This technology has been recognized as a powerful tool for terrain monitoring and structural change detecting. Deformation monitoring for large project has been a hot issue among them. According to GBSAR interferometry principle and characteristics of IBIS system, the authors analysis the error sources of deformation monitoring, and experimentally extract atmospheric phase which should removed based on permanent scattered analysis. Atmospheric disturbance effect analysis is discussed in this paper, and an atmospheric correction method is proposed to remove atmospheric effect, then the effective displacement can be retrieved. Results from this approach have been compared with that from traditional method in this campaign, GBInSAR technology can be exploited successfully in deformation monitoring for major projects with high accuracy

An innovative extraction methodology of active deformation areas based on sentinel-1 SAR dataset: the catalonia case study

Persistent scatterer interferometry (PSI) has been proved to be an advanced Interferometric Synthetic Aperture Radar (InSAR) technique used to measure and monitor terrain deformation. Two of the critical problems with InSAR have been the effect of the refractive atmosphere and decorrelation on the interferometric phases due to long spatial-temporal baseline. The low density of persistent scatterers (PS) in non-urban areas affected by spatial-temporal decoherence more seriously has inspired the development of alternative approaches. Sentinel-1 (S1) has improved the data acquisition throughout, and compared to previous sensors, increased considerably the differential interferometric SAR (DInSAR) and PSI deformation monitoring potential. This paper describes an innovative methodology to process S1 SAR data. Different with PSI, its most original part includes two key processing stages: high and low frequency splitting from wrapped phases, prior to atmospheric filtering, and final direct integration to generate the complete deformation with time series containing linear and nonlinear components. The proposed method has two fundamental advantages compared with traditional PSI approach: the final monitoring results with excellent coverage of coherent points and the generation of active maps even for the areas with serious deformation in short term to break through the inherent limitation of PSI. The effectiveness of the proposed tools is illustrated using a case study located in Catalonia (Spain). This methodology has supposed a definitive step towards the implementation of DInSAR based techniques to support decision makers against geohazards. In this work, the deformation procedures happened in three different areas of the Catalonia (Spain) are presented and analysed. The maximum accumulated subsidence of over – 60 cm induced by mining activity can be detected by proposed methodology with nice coverage from January 2017 to January 2019. These reported cases illustrate how DInSAR based techniques can provide detailed terrain deformation for geohazard activity with complex topographical conditions. The active deformation areas map can be generated in fast aimed at geohazard risk early warning and management.Peer ReviewedPostprint (author's final draft

Differential effects of source-specific particulate matter on emergency hospitalizations for ischemic heart disease in Hong Kong

Background: Ischemic heart disease (IHD) is a major public health concern. Although many epidemiologic studies have reported evidence of adverse effects of particulate matter (PM) mass on IHD, significant knowledge gaps remain regarding the potential impacts of different PM sources. Much the same as PM size, PM sources may influence toxicological characteristics. Objectives: We identified contributing sources to PM10 mass and estimated the acute effects of PM10 sources on daily emergency IHD hospitalizations in Hong Kong. Methods: We analyzed the concentration data of 19 PM10 chemical components measured between 2001 and 2007 by positive matrix factorization to apportion PM10 mass, and used generalized additive models to estimate associations of interquartile range (IQR) increases in PM10 exposures with IHD hospitalization for different lag periods (up to 5 days), adjusted for potential confounders. Results: We identified 8 PM10 sources: vehicle exhaust, soil/road dust, regional combustion, residual oil, fresh sea salt, aged sea salt, secondary nitrate, and secondary sulfate. Vehicle exhaust, secondary nitrate, and secondary sulfate contributed more than half of the PM10 mass. Although associations with IQR increases in 2-day moving averages (lag01) were statistically significant for most sources based on single-source models, only PM10 from vehicle exhaust [1.87% (95% CI: 0.66, 3.10); IQR = 4.9 μg/m3], secondary nitrate [2.28% (95% CI: 1.15, 3.42); IQR = 8.6 μg/m3], and aged sea salt [1.19% (95% CI: 0.04, 2.36); IQR = 5.9 μg/m3] were significantly associated with IHD hospitalizations in the multisource model. Analysis using chemical components provided similar findings. Conclusion: Emergency IHD hospitalization was significantly linked with PM10 from vehicle exhaust, nitrate-rich secondary PM, and sea salt-related PM. Findings may help prioritize toxicological research and guide future monitoring and emission-control polices.published_or_final_versio

Observed 3D Structure, Generation, and Dissipation of Oceanic Mesoscale Eddies in the South China Sea

Oceanic mesoscale eddies with horizontal scales of 50–300 km are the most energetic form of flows in the ocean. They are the oceanic analogues of atmospheric storms and are effective transporters of heat, nutrients, dissolved carbon, and other biochemical materials in the ocean. Although oceanic eddies have been ubiquitously observed in the world oceans since 1960s, our understanding of their three-dimensional (3D) structure, generation, and dissipation remains fragmentary due to lack of systematic full water-depth measurements. To bridge this knowledge gap, we designed and conducted a multi-months field campaign, called the South China Sea Mesoscale Eddy Experiment (S-MEE), in the northern South China Sea in 2013/2014. The S-MEE for the first time captured full-depth 3D structures of an anticyclonic and cyclonic eddy pair, which are characterized by a distinct vertical tilt of their axes. By observing the eddy evolution at an upstream versus downstream location and conducting an eddy energy budget analysis, the authors further proposed that generation of submesoscale motions most likely constitutes the dominant dissipation mechanism for the observed eddies

Coordinated Electric Vehicle Active and Reactive Power Control for Active Distribution Networks

The deployment of renewable energy in power systems may raise serious voltage instabilities. Electric vehicles (EVs), owing to their mobility and flexibility characteristics, can provide various ancillary services including active and reactive power. However, the distributed control of EVs under such scenarios is a complex decision-making problem with enormous dynamics and uncertainties. Most existing literature employs model-based approaches to formulate the active and reactive power control problems, which require full models and are time-consuming. This paper proposes a multi-agent reinforcement learning method featuring actor-critic networks and a parameter sharing framework to solve the EVs coordinated active and reactive power control problem towards both demand-side response and voltage regulations. The proposed method can further enhance the learning stability and scalability with privacy perseverance via the location marginal prices. Simulation results based on a modified IEEE 15-bus network are developed to validate its effectiveness in providing system charging and voltage regulation services

Altered Gut Microbiota in Myasthenia Gravis

Myasthenia gravis (MG) is an autoimmune-mediated disorder, the etiology of which involves both environmental factors and genetics. While the exact factors responsible for predisposition to MG remain elusive, it is hypothesized that gut microbiota play a critical role in the pathogenesis of MG. This study investigated whether gut microbiota are altered in MG patients by comparing the fecal microbiota profiles of MG patients to those of age- and sex-matched healthy controls. Phylotype profiles of gut microbial populations were generated using hypervariable tag sequencing of the V4 region of the 16S ribosomal RNA gene. Fecal short-chain fatty acids (SCFAs) were assessed by gas chromatographic analyses. The results demonstrated that, compared to the healthy cohort, the gut microbiota of the MG group was changed in terms of the relative abundances of bacterial taxa, with sharply reduced microbial richness, particularly in the genus Clostridium. The fecal SCFA content was significantly lower in the MG group. Furthermore, microbial dysbiosis was closely related to the levels of inflammatory biomarkers in the sera of MG patients

CE-BLAST makes it possible to compute antigenic similarity for newly emerging pathogens

Major challenges in vaccine development include rapidly selecting or designing immunogens for raising cross-protective immunity against different intra-or inter-subtypic pathogens, especially for the newly emerging varieties. Here we propose a computational method, Conformational Epitope (CE)-BLAST, for calculating the antigenic similarity among different pathogens with stable and high performance, which is independent of the prior binding-assay information, unlike the currently available models that heavily rely on the historical experimental data. Tool validation incorporates influenza-related experimental data sufficient for stability and reliability determination. Application to dengue-related data demonstrates high harmonization between the computed clusters and the experimental serological data, undetectable by classical grouping. CE-BLAST identifies the potential cross-reactive epitope between the recent zika pathogen and the dengue virus, precisely corroborated by experimental data. The high performance of the pathogens without the experimental binding data suggests the potential utility of CE-BLAST to rapidly design cross-protective vaccines or promptly determine the efficacy of the currently marketed vaccine against emerging pathogens, which are the critical factors for containing emerging disease outbreaks.Peer reviewe

Long Noncoding RNA SNHG16 Promotes Cell Proliferation by Sponging MicroRNA-205 and Upregulating ZEB1 Expression in Osteosarcoma

Background/Aims: Long noncoding RNAs (lncRNAs) have been a research hotspot, as they play important roles in tumor development. However, their expression pattern and biological function in osteosarcoma have not yet been clarified. Methods: Differentially expressed lncRNAs in osteosarcoma and paracarcinoma tissues were identified by screening an lncRNA microarray, and candidate lncRNAs were verified by quantitative real-time PCR (qRT-PCR). A series of bioinformatics and molecular biological methods were adopted to investigate the interaction among lncRNA, microRNA (miRNA), and miRNA target genes during the development and occurrence of osteosarcoma. Cell viability was measured using a Cell Counting Kit-8 assay. Results: Chip microarray screening combined with the validation of differentially expressed candidate lncRNAs showed that the lncRNA small nucleolar RNA host gene 16 (SNHG16) had the largest fold change. SNHG16 was highly expressed in osteosarcoma tissues and cell lines, and its downregulation led to the suppressed proliferation of osteosarcoma cells. Further investigations revealed that SNHG16 could upregulate zinc finger E-box-binding homeobox 1 (ZEB1) expression by acting as an endogenous sponge of miR-205. Moreover, rescue assays proved that the effects of SNHG16 on the proliferation of osteosarcoma cells were dependent on miR-205. Conclusion: SNHG16 can significantly enhance the proliferation of osteosarcoma cells. In addition, SNHG16, miR-205, and ZEB1 interact in a common pathway during the development and occurrence of osteosarcoma, providing novel targets for intervention in the treatment of osteosarcoma

Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach

The large-scale integration of distributed energy resources into the energy industry enables the fast transition to a decarbonized future but raises some potential challenges of insecure and unreliable operations. Multi-energy Microgrids (MEMGs), as localized small multi-energy systems, can effectively integrate a variety of energy components with multiple energy sectors, which have been recently recognized as a valid solution to improve the operational security and reliability. As a result, a massive amount of research has been conducted to investigate MEMG energy management problems, including both model-based optimization and model-free learning approaches. Compared to optimization approaches, reinforcement learning is being widely deployed in MEMG energy management problems owing to its ability to handle highly dynamic and stochastic processes without knowing any system knowledge. However, it is still difficult for conventional model-free reinforcement learning methods to capture the physical constraints of the MEMG model, which may therefore destroy its secure operation. To address this research challenge, this paper proposes a novel safe reinforcement learning method by learning a dynamic security assessment rule to abstract a physical-informed safety layer on top of the conventional model-free reinforcement learning energy management policy, which can respect all the physical constraints through mathematically solving an action correction formulation. In this setting, the secure energy management of the MEMG can be guaranteed for both training and test procedures. Extensive case studies based on two integrated systems (i.e., a small 6-bus power and 7-node gas network, and a large 33-bus power and 20-node gas network) are carried out to verify the superior performance of the proposed physical-informed reinforcement learning method in achieving a cost-effective MEMG energy management performance while respecting all the physical constraints, compared to conventional reinforcement learning and optimization approaches