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The emergence of big data is breaking the spatial and time limitations of urban waterlogging scenario description. The scenario data of different dimensions (e.g., administrative levels, sectors, granularities, and time) have become highly integrated. Accordingly, a structural and systematic model is needed to represent waterlogging scenarios for more efficient waterlogging response decision-making. In this article, a full-view urban waterlogging scenario is first defined and described from four dimensions. Next a structured representation of scenario element is given based on knowledge unit method. The full-view scenario model is then constructed by extracting the scenario correlation structures between different dimensions (called scenario nesting), i.e., inheritance nesting, feedback nesting, aggregation nesting, and selection nesting. Finally, a real-world case study in Wuhan East Lake High-tech Development Zone, China is evaluated to verify the reasonability of the full-view model. The results show that the proposed model effectively integrates scenario data from different dimensions, which helps generate the complete key scenario information for urban waterlogging decision-making. The full-view scenario model is expected to be applicable for other disasters under big data environment.

Summary Smoking is mainly sustained by nicotine dependence (ND), which varies across ethnic groups principally due to genetic as well as environmental factors. The Fagerström Test for Nicotine Dependence (FTND) and biomarkers of tobacco exposure are two important approaches to assess ND. However, the relationship between ND and FTND of Chinese smokers has not been studied. The aim of this study was to assess the relationship between FTND scores and nicotine, cotinine, 3′-hydroxycotinine (3HC) and nicotine metabolite ratio (NMR, the concentration ratio of 3HC to cotinine) in Chinese smokers. FTND was carried out and general characteristics were collected using a self-administered smoking questionnaire with 289 smokers. Nicotine, cotinine and 3HC in urine were simultaneously determined by liquid chromatography–mass spectrometry/mass spectrometry (LC-MS/MS). The concentrations of nicotine, cotinine and 3HC in the urine of smokers with a high FTND score were higher than in the urine of those with a low FTND score. There were significant correlations between urinary biomarker and FTND scores. Except for FTND item 2 (difficulty to refrain), the other items showed significant associations with the urinary biomarkers. No relationship was found between the nicotine metabolite ratio (NMR, 3′-hydroxycotinine/cotinine) and FTND scores or general characteristics of the participants. In conclusion, biomarkers of tobacco exposure levels are significantly associated with FTND scores. However, FTND Item 2 and NMR were not found to be associated with nicotine dependence in Chinese smokers.

With the global trend to establish smart distributed-generation grids, the level of AC-voltage harmonics is significantly increasing due to power electronic converters interfacing renewable energy sources with the grid. Such harmonics will not only degrade the power quality and increase the losses in the electricity grids but will also have a severe impact on oil impregnated paper (OIP) in power transformer bushings. While much attention has been given to investigate the deterioration of OIP under thermal and electrical stresses, the impact of voltage harmonics on the OIP structure is still uncertain and calls for more investigations. Therefore, this paper is aimed at investigating the variations of the multilevel structure of an OIP aged under high levels of AC-voltage harmonics. In this context, degree of polymerization of the aged OIP is measured to reveal the changes of OIP secondary structure. Also, X-ray diffraction, deuteration treatment and Fourier transform infra-red spectroscopy are conducted to analyze the changes of the OIP aggregate structure during the aging process. Further experiments are performed to investigate the variation of the microscopic appearance, thermal behavior and dielectric properties, including scanning electronic microscopy, thermal gravity analysis, differential scanning calorimetry and frequency domain spectroscopy. This investigation on the variation of the multilevel structure of aged OIP not only promotes further understanding to the aging behavior and mechanism of OIP under AC-voltage harmonics, but also proposes crystallinity and accessibility as two new effective aging indicators for the OIP structure.

Background: Heart failure as an important issue in global public health, has brought a heavy economic burden. Traditional Chinese medicine injections (TCMIs) have significant effects on heart failure with reduced ejection fraction (HFrEF). However, it is difficult for clinicians to identify the differences in clinical efficacy and safety of various TCMIs. The purpose of this study is to compare the efficacy and safety of various TCMIs for treating HFrEF by conducting a Bayesian network meta-analysis (NMA) and to further provide references for clinical decision-making.Methods: The clinical randomized controlled trials of TCMIs for treating HFrEF were searched in seven database from inception to August 3rd, 2021. The Cochrane collaboration’s tool was used to assess the risk of bias. NMA was performed in a Bayesian hierarchical framework. The surface under the cumulative ranking curve (SUCRA), the multi-dimensional efficacy analysis, the comparison-adjusted funnel plot, and the node-splitting analysis were conducted using R software.Results: A total of 107 eligible RCTs involving 9,073 HFrEF patients and 6 TCMIs were included. TCMIs include Huangqi injection (HQ) also called Astragalus injection, Shenfu injection (SF), Shengmai injection (SGM), Shenmai injection (SM), Xinmailong injection (XML), and Yiqifumai lyophilized injection (YQFM). The results of NMA and SUCRA showed that with conventional treatment (CT) as a common control, in terms of clinical efficacy, CT + XML was most effective in New York Heart Association cardiac functional classification efficiency, brain natriuretic peptide, and N-terminal pro-brain natriuretic peptide; the CT + SM was most effective in 6-min walking test, left ventricular end-diastolic diameter, left ventricular end-systolic diameter and cardiac output; the CT + YQFM was most effective in left ventricular ejection fraction; the CT + HQ was most effective in stroke volume; the CT + SF was most effective in Minnesota Living with Heart Failure Questionnaire. In terms of safety, there was no significant difference between CT + TCMIs and CT.Conclusion: This Bayesian network meta-analysis results show that the combination of qualified TCMIs and CT is more effective for HFrEF patients than CT alone, and CT + XML and CT + SM may be one of the potential optimal treatments. Also, the safety of these TCMIs needs to be further observed. However, due to some limitations, the conclusions need to be verified by more large-sample, double-blind, multi-center RCTs.

Coronavirus disease 2019 (COVID-19) is an emergent infectious pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is highly contagious and pathogenic. COVID-19 has rapidly swept across the world since it was first discovered in December 2019 and has drawn significant attention worldwide. During the early stages of the outbreak in China, traditional Chinese medicines (TCMs) were involved in the whole treatment process. As an indispensable part of TCM, Chinese patent medicines (CPMs) played an irreplaceable role in the prevention and treatment of this epidemic. Their use has achieved remarkable therapeutic efficacy during the period of medical observation and clinical treatment of mild, moderate, severe, and critical cases and during convalescence. In order to better propagate and make full use of the benefits of TCM in the treatment of COVID-19, this review will summarize the potential target of SARS-CoV-2 as well as the theoretical basis and clinical efficacy of recommended 22 CPMs by the National Health Commission and the Administration of TCM and local provinces or cities in the treatment of COVID-19. Additionally, the study will further analyze the drug composition, potential active ingredients, potential targets, regulated signaling pathways, and possible mechanisms for COVID-19 through anti-inflammatory and immunoregulation, antiviral, improve lung injury, antipyretic and organ protection to provide meaningful information about the clinical application of CPMs.

Coronavirus disease 2019 (COVID-19) is an emergent infectious pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is highly contagious and pathogenic. COVID-19 has rapidly swept across the world since it was first discovered in December 2019 and has drawn significant attention worldwide. During the early stages of the outbreak in China, traditional Chinese medicines (TCMs) were involved in the whole treatment process. As an indispensable part of TCM, Chinese patent medicines (CPMs) played an irreplaceable role in the prevention and treatment of this epidemic. Their use has achieved remarkable therapeutic efficacy during the period of medical observation and clinical treatment of mild, moderate, severe, and critical cases and during convalescence. In order to better propagate and make full use of the benefits of TCM in the treatment of COVID-19, this review will summarize the potential target of SARS-CoV-2 as well as the theoretical basis and clinical efficacy of recommended 22 CPMs by the National Health Commission and the Administration of TCM and local provinces or cities in the treatment of COVID-19. Additionally, the study will further analyze the drug composition, potential active ingredients, potential targets, regulated signaling pathways, and possible mechanisms for COVID-19 through anti-inflammatory and immunoregulation, antiviral, improve lung injury, antipyretic and organ protection to provide meaningful information about the clinical application of CPMs.

In community emergency management, it is crucial to generate evacuation task plans (ETPs) to help reduce risks in complex disaster situations. Case-driven and model-driven approaches have their respective advantages in generating ETPs, which can complement each other. With case-driven approach, historical experience can be fully used to establish the relationship between typhoon scenarios and historical ETPs. Through model-driven approach, the continuity of ETPs can be guaranteed when required information to operate the plans is missing. This study aims at proposing an integrated approach that can combine the benefits of both case-driven approaches and model-driven approaches. Based on the structural modeling of evacuation tasks, this paper proposes an integrated approach to generate ETPs for community typhoon emergencies. Finally, a case that is based on actual problems is provided to verify the reasonability and effectiveness of the proposed method.

Transformer insulation paper is a key indicator for transformer remaining operational life. Paper decomposition is evaluated using the degree of polymerization (DP) which calls for samples of insulation paper from operating transformers. Since collecting such paper samples is extremely difficult, other indicators have been used to indirectly reveal the DP value of insulation paper. This includes dissolved gases in transformer oil such as carbon oxides and hydrocarbon gases, furan compounds, methanol, ethanol, and moisture along with some oil characteristics such as interfacial tension. However, for the same oil sample, these individual parameters lead to different DP values. This is attributed to the lack of accuracy of the established mathematical and artificial intelligence models correlating DP with each of the above mentioned individual parameters. This paper presents a self-learning method to estimate the DP value of transformer insulation paper based on multiple transformer oil aging parameters. The proposed method comprises data processing, fuzzy c-means and linear regression. Results reveal that estimating the DP value based on multiple aging parameters is more accurate than estimating it using one single parameter as per the current practice. The proposed method not only helps to understand the correlation between multiple oil aging parameters and the DP value of paper insulation, but also promotes the establishment of more accurate life assessment models for power transformers based on these oil aging parameters.

The discrepancy between expected and actual food product features can be an important indicator of food quality and thus may inform consumer decisions. Using mixed method design, this paper explores whether and how expectation discrepancy affects food consumers' quality perception and purchase intentions under food safety risk situation. In the first phase, a qualitative study based on in-depth semi structured interviews (N = 15) was conducted. The interview results revealed that consumers in general responded negatively towards the existence of expectation discrepancies as it led to poor perceived food quality. It was further revealed that the discrepancy in expectation dimensions had a stronger effect on consumers’ perceptions as compared to the discrepancy in expectation extent. In the second phase extensive online survey (N = 2172), it was confirmed that expectation discrepancy had significantly affected quality perceptions and purchase intentions. Moreover, perceived quality played a mediating role between expectation discrepancy and purchase intentions. In addition, expectation proportion (measured with the importance of specific food feature) was shown to strengthen (moderate) the impact of expectation discrepancy. Furthermore, the effects of expectation discrepancy were stronger for high-risk food (versus low risk food). The current paper shows that the effects of expectation discrepancy play a highly significant role in forming perceptions about food safety, therefore, precise marketing strategies can be made by clustering consumers according to the expectation proportions.

A cross-region transfer learning method is proposed to identify community (e.g. car parks, public spaces and shopping centers) fire hazards based on text input provided by community members. The key component of the method, which also accounts for data imbalance, is an improved transfer component analysis (TCA) that is embedded with a local discriminant analysis (LDA) to transfer non-local rich knowledge to the fire hazard identification of local communities with an insufficient number of samples. In addition, a fire hazard knowledge map is established and applied to supplement the missing key features for fire hazard identification, and ontology modeling is applied to standardize the text features and reduce the effect of semantic ambiguity brought by cross-region knowledge transfer. The proposed method is verified based on the text data of nine fire hazard classes from Lanzhou and Beidaihe in China. Machine learning experiments show that fire hazard identification performance of all nine classes were improved with the overall accuracy, precision, recall, F1 score and AUC increased by 12%, 15%, 16%, 15% and 15%, respectively. Under data imbalance scenarios, the proposed method outperforms the state of the art methods, such as sampling-based methods, FastText and ULMFiT. The results also show that the proposed method can achieve desired performance with only half of the training samples. These findings illustrate that the proposed method can assist regions by improving fire identification results significantly through knowledge transfer. The proposed approach can be followed to build smart systems for community fire risk management with reasonable performance and high efficiency.

At present, the oil-paper insulation structure is mainly composed of traditional mineral oil and cellulose insulation paper. The hots pot of future research is to select better insulation oil to improve the overall insulation life of the system. Compared with traditional mineral insulation oil, gas to liquid(GTL) oil made from base oil refined by natural gas liquefaction technology by Fischer-Tropsch(FT) method are characterized by high oxidative stability and low sulfur content. In this paper, the space charge accumulation, distribution and attenuation characteristics of oil-impregnated paper composed of GTL oil and traditional mineral oil were studied. The space trap energy level and trap density distribution of two oil-impregnated papers were obtained according to isothermal decay current(IDC) method. The experimental results show that in the voltage-on stage, traditional mineral oil-impregnated paper is more prone to charge injection and the injection speed is faster. In the voltage-off stage, the traditional mineral oil-impregnated paper attenuates the charge faster, and the GTL oil-impregnated paper has more residual charge. Moreover, the trap level of GTL oil-impregnated paper is deeper than that of traditional mineral oil-impregnated paper.

A 3300V/62.5A low loss FS-IGBT was developed by using the carrier storage layer structure and laser annealing process on the back p+ collector, which could lower the saturation voltage. The effects of laser annealing energy and P+ collector dose on dynamic and static state were studied. Considering the trade-off of the turn-off loss and the saturation voltage, the IGBT was finally designed with the 2.35V saturation voltage, 132mJ turn-off loss.

Transformer life management has gained remarkable recognition due to its crucial role for safe operation of the power grid. Since transformer life is dominated by insulating state of its oil-paper insulation, the DP value has been traditionally regarded as the primary indicator. However, the DP sampling procedure is destructible and inconvenient. In this paper, we present an effective principal component regression method to assess the insulating state of oil-paper through indirectly estimating the degree of polymerization based on the aging characteristic parameter in oil. Thermal aging experiments were firstly conducted on palm oil and mineral oil impregnated paper, respectively. After aging experiments, aging characteristic parameters of oil were tested, including moisture, acidity, 2-furan, surface tension, dissolved gas-in-oil analysis, methanol, and ethanol. Principal component regression method was then performed to find the principal component and build a relationship between the degree of polymerization and the aging characteristic parameters of oil. After computation, the estimation of degree of polymerization value can be obtained with a high goodness of fitting, which is 0.92 and 0.78 for mineral oil and palm oil impregnated paper respectively. The estimation could be improved by data processing.

The precise classification of community service cases is the most fundamental aspect of intelligent community service systems. However, data imbalance makes it challenging to achieve the desired level of precise classification. Existing transfer learning methods use open Internet knowledge for identifying case features and mining potential feature relations. However, community service cases have the characteristics such as short text length and non-public content, which restrict the transfer learning modes. In this paper, a cross-region transfer learning method is proposed to solve the classification problem of cases with small datasets in data imbalance situation while considering the perspective of regional cooperation. First, an ontology modeling method is applied to standardize the case features, reducing the effect of semantic ambiguity on transfer results. Secondly, to improve the effectiveness of source domain classification, this paper utilizes an extended marginal fisher analysis where the distance is measured by the inner product between data. Next, the mapping matrix from target domain to source domain is learned through domain adaptation. Finally, the method is verified based on the empirical data from Lanzhou and Beidaihe in China. Experimental results on classifiers show the proposed method helps regions to improve case classification rates significantly through knowledge complementation. The proposed approach can be followed to build case-based community service systems of reasonable accuracy and limited sample sizes.

In community safety service, big data governance is the prime mode to achieve community safety big data sharing and service value increasing. Although existing researches have preliminarily established the general big data governance framework, identification and governance of specific sharing problems lack comprehensive and systematic scenario description. Applying software engineering method, this paper proposes a kind of scenario expression model of big data governance in community safety service. Considering the common features of big data governance scenarios, construct the meta-scenario model of big data governance in community safety services. Considering the scenario expression difference under different levels, scales and particle sizes, construct the full view scenario model of big data governance in community safety services by meta-models nesting to complete the scenario expression under different applying situation. Finally, a use case is proposed to verify the rationality and effectiveness of the scenario expression models.

Press Pack IGBTs modules have the characteristics of easy series connection and double side cooling widely used in industry, electric power, smart grid and other fields. Press Pack FRD (Fast Recovery Diode) chips is a component of Press Pack IGBTs module. In the actual operating conditions, the electrical parameters of FRD device will change in the electrical properties under pressure-stress, especially the Forward Voltage (VF) parameters. This change of the VF parameters will have adverse effect on the reliability of FRD chip and the overall module. In this paper, according to the stress of physical model of semiconductor material silicon were analyzed, proposed method to stress effect on the VF parameter of the FRD simulation analysis software.

A 1700V/100A NPT-IGBT was designed by process simulation, which had an internal transparent collector and a planner cell structure. The Static characteristics were studied. The simulation results show that the threshold voltage of the device can be adjusted by changing the injection dose or the drive-in time of the p-well. The saturation voltage of the device can be adjusted by changing injection dose of the p-well or the internal transparent collector. This device was fabricated using a self-aligned process, the test results show that the breakdown voltage is more than 2100V, the saturation voltage is between 2.5V and 2.7V and the threshold voltage is between 3.9V and 5.9V, which are similar with the simulation results.

IGBT chips are basic structure of power electronic devices in the power grid system. The IGBT module with high current capability needs multi chips parallel connection. Because there are different performance parameters of the IGBT chips in the Wafer, it is necessary to select the parameters of the IGBT chips for the IGBT modules to make the performance parameters of chips as consistent as possible. However, it is unknown that what the influence of performance parameter differences between IGBT chips on the overall performance of the module such as IGBT modules' switch-off and RBSOA characteristics is. In addition, switching state of each single IGBT chip is not monitored when the whole module is on working state. This paper is trying to demonstrate a method to research the multi parallel chips on the performance of different parameters effects of each single IGBT chip switch-off Process, which is based on the T-CAD software tool.

The environment in coal mines is complex, and it is very difficult to study on emergency evacuation in real environment because of its particularity and complexity. A virtual reality platform was designed and developed for emergency evacuation during disasters in coal mines based on Unity 3D. And 21 participants were selected to test and evaluate on the platform. The study has shown that the platform reproduced these scenarios during disasters in coal mines, and participants were stunning with relatively good immersion. The platform proved that it can be used to study the efficiency of emergency evacuation during disasters in coal mines.

According to the requirement of IGBT's over-current turn-off capability, this paper analyzes the factors affecting the dynamic latch-up of IGBT chip during the over-current turn-off process, including the gate width of IGBT cell, the doping concentration of back P + collector and the dVce/dt when the IGBT is turned off. The simulation results show that the anti-dynamic latch-up capability of IGBT can be effectively improved by decreasing the gate width and back P + collector doping concentration. Through multi-cell parallel simulation, it is found that current concentration exists in the process of overcurrent trun-off, which leads to the further increase of the lattice temperature, and the overcurrent turn-off capability declines.

As the first step of temporal information understanding, the results of temporal expressions recognition will directly affect further usage of temporal information. For Chinese language, there are many distinct characters both in word morphology and syntax in temporal expressions compared with the Western languages. Classifications and constructions of Chinese temporal expressions were analysed, and an approach for extracting temporal expressions from Chinese texts was presented in this paper. The model comprises of a cascade of rule-based and machine-learning pattern recognition procedures. Conditional random fields (CRFs) were applied to recognise time units rather than time expressions to avoid the boundary localisation problems in Chinese temporal expressions. Rules for the temporal expressions boundary localisation were formulated based on time triggers thesaurus and time affix words thesaurus. The F-measure of temporal expressions identification was 95.93% on the temporal 2010 Chinese corpus. The experiments result showed the validity of the proposed approach.

Rosai-Dorfman disease (RDD) is a rare idiopathic, non-neoplastic histioproliferative disease. Central nervous system (CNS) manifestations are extremely rare. In this paper, we describe a 6-year-old boy with intracranial RDD mimicking multiple meningiomas both clinically and radiologically. We reviewed the literature to understand the clinical behaviour, clinicopathological features and treatment options.A PubMed (US National Library of Medicine) search using the keywords 'Rosai-Dorfman disease' and 'central nervous system' was performed and citations were reviewed.Eighty-five cases of RDD involving the CNS have been reported until date, and only 7 cases involved children. Of the 85 cases, 16 cases mimicked multiple meningiomas. Our case is the first to involve multiple lesions in a child under 14 years old.After reviewing the literature, we concluded that RDD should be considered as a differential diagnosis for lesions mimicking multiple meningiomas, especially in children. Resection of the intracranial lesion is the most effective treatment, and a definitive diagnosis should be based on histopathologic and immunocytochemical examinations.