Study on Ignition Probability of Flammable Materials after Leakage Accidents

AbstractIt is a key step of quantitative risk analysis (QRA) to estimate ignition probability of flammable materials after leakage accidents. This paper reviews the available literature and expert opinion on how to evaluate and determine ignition probability value, and it was detailedly discussed on the main influencing factors of ignition probability, including flammable material properties, mass flow rate of flammable materials spillage, ignition resources and ignition controls. Moreover, the operational and practical ignition probability value could be estimated from the all way of classifications of flammable material, mass flow rate, ignition resources, hazardous areas and ignition prevention and control measure. Furthermore, the more practical ignition probability model was put forward that the ignition probability was the maximum value of the probability decided by material properties (PMP), mass flow rate(PQ) and ignition resources(PIS) with the factor of preventing and controlling ignition (KIC). Finally, the further research was proposed to assign some feasible weigh factors of the ignition probability for flammable materials after leakage accidents

Research progress on material properties of clad steel

[EN] Clad steel possesses benefits of the both component metals in terms of mechanical performance, corrosion resistance, sustainability and lower full lifecycle cost, etc. As a result, it has been more and more widely used in the petroleum, chemical, marine, shipbuilding and metallurgical industries, including stainless-clad steel and titanium clad steel. Such clad steel has also great potential for application in building and bridge structures. For better understanding material properties of such clad steel, a review of research progress available in the literature is conducted herein, as well as recent research undertaken by the authors’ group at Tsinghua University. It can be found that very limited research reported in the literature mainly concerns static material properties of the clad steel, and primary relations between clad ratio and strength are suggested. The authors carried out material tests on both titanium and stainless-clad steel plates, with different clad ratios being incorporated. For the stainless-clad steel tests, both material and butt welded connections are tested, and various elevated temperatures are considered. In addition, tension coupon tests under cyclic loadings are also briefly introduced herein. Primary constitutive relations developed by the authors are reviewed in this paper. All the research findings and proposed formulae may provide an essential basis for future structural analysis, and may promote its application in structural engineering.This work was financially supported by the National Natural Science Foundation of China (Nos. 51778329, 51608300), which are gratefully acknowledged.Ban, H.; Bai, R.; Chung, K.; Zhu, J.; Bai, Y. (2018). Research progress on material properties of clad steel. En Proceedings of the 12th International Conference on Advances in Steel-Concrete Composite Structures. ASCCS 2018. Editorial Universitat Politècnica de València. 653-658. https://doi.org/10.4995/ASCCS2018.2018.7010OCS65365

Social exclusion and suicide intention in Chinese college students: a moderated mediation model

Given the growing incidence rates of suicide among college students and the potential lifelong consequences of suicide, it is imperative to better understand the factors that reduce the rates at which college students in a clinical sample engage in suicide. This study examines the relationship between social exclusion and suicide intention, the mediating effect of depression, and the moderating effect of meaning in life. Two hundred and ninety-nine Chinese college students, aged from 18 to 22 years (56.86% female, M age = 20.14, SD = 1.27) completed questionnaires assessing their social exclusion, suicide intention, depression, and meaning in life. The result revealed that social exclusion was positively associated with suicide intention, and depression mediated this relationship. In addition, this mediating effect of depression was moderated by meaning in life. That is, the mediation effect was stronger for students with a higher level of meaning in life. These findings provide educational suggestions for preventing and intervening in suicide intention among college students

Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding

Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. This task has achieved significant momentum in the computer vision community as it enables activity grounding beyond pre-defined activity classes by utilizing the semantic diversity of natural language descriptions. The semantic diversity is rooted in the principle of compositionality in linguistics, where novel semantics can be systematically described by combining known words in novel ways (compositional generalization). However, existing temporal grounding datasets are not carefully designed to evaluate the compositional generalizability. To systematically benchmark the compositional generalizability of temporal grounding models, we introduce a new Compositional Temporal Grounding task and construct two new dataset splits, i.e., Charades-CG and ActivityNet-CG. When evaluating the state-of-the-art methods on our new dataset splits, we empirically find that they fail to generalize to queries with novel combinations of seen words. We argue that the inherent structured semantics inside the videos and language is the crucial factor to achieve compositional generalization. Based on this insight, we propose a variational cross-graph reasoning framework that explicitly decomposes video and language into hierarchical semantic graphs, respectively, and learns fine-grained semantic correspondence between the two graphs. Furthermore, we introduce a novel adaptive structured semantics learning approach to derive the structure-informed and domain-generalizable graph representations, which facilitate the fine-grained semantic correspondence reasoning between the two graphs. Extensive experiments validate the superior compositional generalizability of our approach.Comment: arXiv admin note: substantial text overlap with arXiv:2203.1304

Evaluation of Chinese Quad-polarization Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval

Our work describes the accuracy of Chinese quad-polarization Gaofen-3 (GF-3) synthetic aperture radar (SAR) wave mode data for wave retrieval and provides guidance for the operational applications of GF-3 SAR. In this study, we evaluated the accuracy of the SAR-derived significant wave height (SWH) from 10,514 GF-3 SAR images with visible wave streaks acquired in wave mode by using the existing wave retrieval algorithms, e.g., the theoretical-based algorithm parameterized first-guess spectrum method (PFSM), the empirical algorithm CSAR_WAVE2 for VV-polarization, and the algorithm for quad-polarization (Q-P). The retrieved SWHs were compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field with 0.125° grids. The root mean square error (RMSE) of the SWH is 0.57 m, found using CSAR_WAVE2, and this RMSE value was less than the RMSE values for the analysis results achieved with the PFSM and Q-P algorithms. The statistical analysis also indicated that wind speed had little impact on the bias with increasing wind speed. However, the retrieval tended to overestimate when the SWH was smaller than 2.5 m and underestimate with an increasing SWH. This behavior provides a perspective of the improvement needed for the SWH retrieval algorithm using the GF-3 SAR acquired in wave mode

Clinical, dermoscopic, and ultrasonic monitoring of the response to biologic treatment in patients with moderate-to-severe plaque psoriasis

BackgroundAssessment of therapeutic response of psoriasis has relied traditionally on clinical observation, and effective non-invasive tools are desirable.ObjectivesTo investigate the value of dermoscopy and high-frequency ultrasound (HFUS) in the monitoring of psoriatic lesions treated with biologics.MethodsPatients with moderate-to-severe plaque psoriasis treated with biologics were evaluated by clinical, dermoscopic, and ultrasonic scores at weeks 0, 4, 8, and 12. Clinical scores, including Psoriasis Area Severity Index (PASI) and target lesion score (TLS), were evaluated at representative lesions. Dermoscopy was performed to assess the red background, vessels, and scales on a 4-point scale as well as the presence of hyperpigmentation, hemorrhagic spots, and linear vessels. HFUS was performed to measure the thicknesses of the superficial hyperechoic band and subepidermal hypoechoic band (SLEB). The correlation between clinical, dermoscopic, and ultrasonic evaluation was also analyzed.ResultsIn total, 24 patients were analyzed and achieved 85.3 and 87.5% reduction of PASI and TLS, respectively, after 12 weeks of treatment. The red background, vessels, and scales scores under dermoscopy were reduced by 78.5, 84.1, and 86.5%, respectively. Some patients developed hyperpigmentation and linear vessels after treatment. Hemorrhagic dots slowly subside over the therapeutic course. Ultrasonic scores were significantly improved with an average reduction of 53.9% in superficial hyperechoic band thickness and 89.9% in SLEB thickness. TLS in the clinical variables, scales in dermoscopic variables, and SLEB in ultrasonic variables decreased the most significantly in the early stage of treatment (week 4) with 55.4, 57.7, and 59.1% (P > 0.05), respectively. Most of the variables, including the red background, vessels, scales, and SLEB thickness, were strongly correlated with TLS. High correlations were also found between the SLEB thickness and the red background or vessels scores, and between the superficial hyperechoic band thickness and the scales scores.ConclusionBoth dermoscopy and HFUS were useful in the therapeutic monitoring of moderate-to-severe plaque psoriasis

Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting. Document type: Articl

Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos

Understanding human emotions is a crucial ability for intelligent robots to provide better human-robot interactions. The existing works are limited to trimmed video-level emotion classification, failing to locate the temporal window corresponding to the emotion. In this paper, we introduce a new task, named Temporal Emotion Localization in videos~(TEL), which aims to detect human emotions and localize their corresponding temporal boundaries in untrimmed videos with aligned subtitles. TEL presents three unique challenges compared to temporal action localization: 1) The emotions have extremely varied temporal dynamics; 2) The emotion cues are embedded in both appearances and complex plots; 3) The fine-grained temporal annotations are complicated and labor-intensive. To address the first two challenges, we propose a novel dilated context integrated network with a coarse-fine two-stream architecture. The coarse stream captures varied temporal dynamics by modeling multi-granularity temporal contexts. The fine stream achieves complex plots understanding by reasoning the dependency between the multi-granularity temporal contexts from the coarse stream and adaptively integrates them into fine-grained video segment features. To address the third challenge, we introduce a cross-modal consensus learning paradigm, which leverages the inherent semantic consensus between the aligned video and subtitle to achieve weakly-supervised learning. We contribute a new testing set with 3,000 manually-annotated temporal boundaries so that future research on the TEL problem can be quantitatively evaluated. Extensive experiments show the effectiveness of our approach on temporal emotion localization. The repository of this work is at https://github.com/YYJMJC/Temporal-Emotion-Localization-in-Videos.Comment: Accepted by ACM Multimedia 202

Integrated gene-based and pathway analyses using UK Biobank data identify novel genes for chronic respiratory diseases

BackgroundChronic respiratory diseases have become a non-negligible cause of death globally. Although smoking and environmental exposures are primary risk factors for chronic respiratory diseases, genetic factors also play an important role in determining individual’s susceptibility to diseases. Here we performed integrated gene-based and pathway analyses to systematically illuminate the heritable characteristics of chronic respiratory diseases.MethodsUK (United Kingdom) Biobank is a very large, population-based prospective study with over 500,000 participants, established to allow detailed investigations of the genetic and nongenetic determinants of the diseases. Utilizing the GWAS-summarized data downloaded from UK Biobank, we conducted gene-based analysis to obtain associations of susceptibility genes with asthma, chronic obstructive pulmonary disease (COPD) and pneumonia using FUSION and MAGMA software. Across the identified susceptibility regions, functional annotation integrating multiple functional data sources was performed to explore potential regulatory mechanisms with INQUISIT algorithm. To further detect the biological process involved in the development of chronic respiratory diseases, we undertook pathway enrichment analysis with the R package (clusterProfiler).ResultsA total of 195 susceptibility genes were identified significantly associated with chronic respiratory diseases (Pbonferroni < 0.05), and 24/195 located out of known susceptibility regions (e.g. WDPCP in 2p15). Within the identified susceptibility regions, functional annotation revealed an aggregation of credible variants in promoter-like and enhancer-like histone modification regions and such regulatory mechanisms were specific to lung tissues. Furthermore, 110 genes with INQUISIT score ≥1 may influence diseases susceptibility through exerting effects on coding sequences, proximal promoter and distal enhancer regulations. Pathway enrichment results showed that these genes were enriched in immune-related processes and nicotinic acetylcholine receptors pathways.ConclusionsThis study implemented an integrated gene-based and pathway strategy to explore the underlying biological mechanisms and our findings may serve as promising targets for future clinical treatments of chronic respiratory diseases

Electric vehicle charging load forecasting: A comparative study of deep learning approaches

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model