In construction safety, accident-causing factors typically exhibit intricate interrelationships; therefore, targeted prevention and precision control hold significant practical value for enhancing safety performance. Complex network theory has proven to be a powerful tool in this regard, revealing the interdependencies among causes. In this study, a real-world dataset is analyzed comprising 653 construction safety accidents in China. The objective is to unravel the relationships among the 39 causes and further identify the diverse influence of causes by leveraging complex network centrality metrics and visualization techniques. The results indicate that non-compliance of workers (O32), inadequate safety inspection (T23), fault of safety engineers (O22), and lacking safety awareness (S11) are high-centrality node causes that dominate the formation and propagation of accident chains. The top five high-in-degree node factors easily triggered by other unsafe behaviors are O32, T23, O22, S11, and unqualified workers (O31). The top five high-out-degree node factors are inadequate special safety meeting (T22), improper installation of temporary facilities (T14), no special safety department (O11), multi-level subcontracting (C22), and inadequate emergency rescue plan (E12). The findings provide practical guidance for targeted prevention, which focuses on prioritizing key factors and optimizing preventive strategies.

The outbreak of COVID-19 emphasized the worldwide challenges in emergency communication, especially for Hearing and speech-impaired persons (HSIPs) with communication difficulties. In China, the epicenter of the initial outbreak, HSIPs faced significant communication barriers early on. Promoting the Chinese National Sign Language (CNSL) emerges as a potential solution to this predicament. Despite its potential, CNSL’s adoption rate in China is lower than expected, underlining the need to understand the barriers to its acceptance. Through collaboration with the Disabled Persons’ Federation in Yancheng District, Luohe City, Henan Province, we distributed an online survey via official disability WeChat groups to gather data from HSIPs. The survey design, incorporating single-choice questions for demographic information and a Likert five-point scale to gage the willingness to use CNSL, was intended not only to understand their basic situation but also to identify the determinants influencing HSIPs’ behavioral intention toward CNSL adoption. By using linear regression analysis with SPSS, we found that Perceived Usefulness (PU), Ease of Use (EOU), Social Influence (SI), Facilitating Conditions (FC), and Self-efficacy (SE) all have a significant positive impact on Behavioral Intention to Use (BIU) CNSL. Additionally, one-way ANOVA and post hoc tests revealed significant group differences between annual income and behavioral intention to use CNSL. This research provides profound insights for the global community regarding the acceptance of CNSL among HSIPs in China, offering insights to enhance daily and emergency communication efficiency for HSIPs, and contributing to building a more inclusive and safe society.

Disruptive technologies fundamentally redefine competitive landscapes, forging new market and value networks that enable nascent firms to challenge and even overtake established market leaders. The ramifications of these technologies are profound, not only catalysing industry transformation but also precipitating significant societal shifts. Given these stakes, the identification of disruptive technologies emerges as a paramount concern. However, the methodologies employed in relevant literature for this purpose face various limitations. This research proposes a methodology that capitalises on market data, transcends disciplinary boundaries, and minimises reliance on subjective judgment. This is accomplished by examining stock trading volumes and academic papers to unearth disruptive technologies. Employing the density-based spatial clustering of applications with noise (DBSCAN) algorithm and the Augmented Dickey-Fuller (ADF) test, this study analyzed 159 instances from the Shanghai Stock Exchange's risk alert board. The findings reveal that disruptive technologies are predominantly spawned from the core business operations, with larger firms exhibiting a greater propensity to foster such innovations. Moreover, these technologies tend to amalgamate traditional models of technological innovation. This study's methodology and insights furnish firms with a strategic framework to identify and leverage disruptive technologies, thereby securing competitive edge and propelling industrial progress.

As risks grow increasingly complex and the field of emergency management evolves, there is an urgent need for methodological advancements. In response, this study introduces the E-M-A-M emergency management methodology, grounded in systems theory. The methodology follows a structured logical framework: (1) Effect Analysis (E) provides a phenomenological explanation of the observed phenomena; (2) Intrinsic Mechanism Analysis (M) delves into the underlying mechanisms of emergencies; (3) Comprehensive Risk Analysis (A) integrates various dimensions to construct a holistic understanding of the risk landscape; and (4) Management Mechanism Design (M), based on mechanism design theory, offers practical strategies for improving emergency management systems. To illustrate its application, the study employs the '23·7' heavy rainfall in the Beijing-Tianjin-Hebei region as a case study, detailing the operational steps of the E-M-A-M methodology. This research aims to provide a systematic and standardized approach for advancing emergency management studies and addressing real-world challenges.

Charities play a pivotal role in engaging the public in emergency management efforts. They serve to complement governmental restrictions by leveraging social resources to aid in emergency management. The involvement of charities in emergency management is likely to shape public attitudes, thereby influencing their effectiveness in this sphere. Therefore, understanding the factors that influence public attitudes toward charities in emergency management is crucial. This study sought to identify these key factors and offer recommendations for charities to enhance their participation in emergency management. The data for this study were collected from messages and comments on two prominent instant messaging platforms, WeChat Public and Sina Weibo. Content Analysis was employed to categorize the data, and the Apriori algorithm was utilized to uncover association rules and key factors. Based on the key factors, it is recommended that charities focus on collaborating with celebrities and enterprises, prioritize establishing and upholding a positive reputation, and enhance their expertise in emergency management practices.

This study aims to investigate whether the Unmanned Aerial Vehicle (UAV) safety studies match the safety issues relating to the UAV goods. The causes and the effects of the research status are combined to predict the future development of the field. To identify core categories and core concepts of the UAV safety issues, this study conducts qualitative research on the bad reviews of the UAV goods sold on e-commerce platforms. By using the Latent Dirichlet Allocation (LDA) model, the topics of the research papers on the Chinese National Knowledge Infrastructure (CNKI) and the Web of Science (WoS) are derived. The findings show that there is a gap between the UAV studies and the safety issues relating to UAV goods. Furthermore, a majority of UAV safety studies are focused on information transfer and algorithm optimization. In comparison, the studies on safety improvement from the hardware perspective are not prominent. Accordingly, this study argues that UAV development should be coordinated by regulation, supervision, and even price leverage. The paths, both data-information-network and sensing-control-optimization, could be of interest to future studies on UAV safety technologies.

In China the number of accidents and deaths in construction industry is increasing rapidly in recent years. Cause analysis is important to reveal the main factors contributing to the accidents, and therefore improve the safety mechanism for accident reduction in future. In this paper, 653 construction safety accidents in China which are fully investigated and formulated by a set of country-specific factors are used as the source data of potential causal relationships among the factors. By utilizing association rules a total of 13 main factors contributing to accident causation are explored, and 9 factors significantly impacting on the severity of accidents are identified. The discovered association rules offer scientific grounds for Chinese construction safety management and decision support. The novelty of this study is to present a different perspective during cause analysis in which the factors impacting the construction site safety and the consequences of construction accidents are revealed respectively.

Natural disaster that contributes to the economic crisis all over the world has a crucial role in emergency management. The assessment of regional risk to natural disasters is normally studied as a multi-criteria decision making (MCDM) problem in the literature. However little effort was devoted into the comparison of temporary disaster risk of regions. In this paper, a hybrid approach is proposed integrating MCDM and clustering for evaluating and comparing the regional risk to natural disasters. Our two-stage method is applied to thirty-one Chinese regions over the past two consecutive years. In the first stage MCDM is used to prioritize the regions yearly yielding a set of risk vectors over the given period. In the second stage, K-means clustering is applied to divide the regions into a number of clusters characterized by different risk variation patterns. The derived patterns reveal the variation of regions in perspective of natural disaster risk and therefore offer valuable suggestions for disaster risk reduction.

如今高速发展的中国城市正饱受"垃圾围城"之痛."人生活在城市里,城市受困于垃圾中"已是当前的真实写照."十二五"以来,地方政府虽然出台了相关政策,但是面对城市人口不断积聚的现状,我国仍面临垃圾难排、难运、难处理、难管理的窘境,"垃圾围城"的悲剧仍在上演."垃圾围城"的风险不容忽视,因此"垃圾围城"现状和对策的研究必须被重点关注.文章基于"收集信息—揭示信息—综合研判—形成方案"(Data-Information-Intelligence-Solution,DIIS)分析框架,运用"谷歌地球"(Google Earth,GE)及地理信息系统(Geographic Information System,GIS)软件识别31个直辖市和省会城市非正规垃圾填埋场的个数、面积及形成情况,以得出"垃圾存量"数据.进而,基于"驱动力-状态-响应"(Driving Force-Status-Response,DSR)风险评价模型识别31个省会城市的"垃圾围城"风险,通过分析现状、识别风险源、理清风险的发生发展机制,进而为规避垃圾围城风险进行管理机制设计,以期为政府部门降低城市"垃圾围城"风险献力.

The purpose of this paper is to further investigate engineering ethics and its gap within accident analysis models. In this paper, at first, the role of human factors in the occurrence of accidents is presented. Then engineering ethics as an element of human factors is proposed. It is suggested that engineering ethics can provide engineers with the necessary guidelines to avoid possible accidents arising from their decisions and actions. In addition, the Challenger and Columbia space shuttle case studies that demonstrate the role of engineering ethics in the prevention and occurrence of accidents are discussed. Then sequential, epidemiological, and systemic accident analysis models are briefly investigated and negligence of engineering ethics as a gap in the accident analysis models is described. At the end, we suggest that by implementing engineering ethics as a controller within the system boundary in systemic accident models we may be able to identify and prevent the ethical causes of accidents.

In the context of expensive and time-consuming acquisition of reliably labeled data, how to utilize the unlabeled instances that can potentially improve the classification accuracy becomes an attractive problem with significant importance in practice. Semi-supervised classification that fills the gap between supervised learning and unsupervised learning is designed to take advantage of the unlabeled data in regular supervised learning procedure for classification tasks. In this paper we proposed a self-learning framework, that firstly pre-learns a classification model using the labeled data, then makes the prediction of unlabeled instances in the form of soft class labels, and re-learned a model based on the enlarged training data. Two multi-label Learning Vector Quantization Neural Networks (LVQ-NNs) are proposed, namely multi-label online LVQ-NN (mLVQo) and multi-label batch LVQ-NN (mLVQb), to work with the soft labels of training instances. The experiments demonstrate that the semi-supervised models using multi-label LVQ-NN as the base classifier can produce better generalization accuracy than the supervised counterpart.

In order to cope with the emergencies with transformation effects, we study the transformation intrinsic mechanism of emergencies and propose corresponding strategies. Firstly, we analyze the characteristics and types of emergency transformation. Secondly, we explore the causes of different types of transformation, and establish a mathematical model to analyze it. Finally, we propose coping strategies for the transformation of emergencies. The results show that the coping strategies of the one-level controllable transformation include route control strategy, multi-export dredge strategy and resistance increase strategy; the coping strategies of the multi-level controllable transformation include prediction-response strategy and control the chain reaction.

Engineering as a profession has a direct effect on society and the environment. Engineering ethics is a part of the essence of engineering. One of the important branches of engineering profession is aerospace engineering. Furthermore, aerospace industry achievements play an undeniable role in our lives. Research and development in the aerospace domain have contributed to the progress of some new technologies in the last decades. The purpose of this study is to emphasize the importance of engineering ethics as an essential part of aerospace engineering. Engineering ethics examines professional responsibilities and ethical decision making of engineers. Moreover, codes of ethics help the engineers to apply ethical principles in critical conditions. The poor ethical decision-making of engineers leads to engineering failures which jeopardized human life and the environment. This paper by examining two case studies related to the field of aerospace engineering (Challenger and Columbia disasters) describes the role of the negligence of engineering ethics on the occurrence of engineering disasters.

The development of technological systems is leading to new kinds of safety issues. Systemic accident analysis (SAA) models enable safety professionals to examine the root causes of accidents. The theoretical foundation of different SAA models is considering accidents as a consequence of uncontrolled interactions within the system. Furthermore, preventing and reducing accidents requires analyzing all the technical and nontechnical aspects of control in the system. Ethical shortcomings as non-technical elements like mismanagement, conflict of interest, and inattention to safety play a crucial role in the system failures. Ethical control as a non-technical control and an aspect of human factors especially in the management area is contributing to the prevention or the occurrence of many accidents. In addition, ethical control may surround all system components in order to influence the advancement of the system purpose and avoid failure. In this study, we recommend that it is necessary to expand the concept of control in SAA models so that ethical control is considered as an essential component of the control structure in SAA models. Besides, we introduce ethical control in three levels within the system: ethical considerations of policy-makings, ethical assessment of decision makings, and ethical evaluation of performances.

The rapid development of the Chinese is under the risk of besieged by garbage, although the local government introduced the relevant policies to work out this problem ,the risk of besieged by should be taken seriously with the status of city population continue to accumulate. This research choose 31 provincial capital of China as sample, using multidimensional spatial data software identify the number and size of informal landfill space around the sample, gets the index of trash stock index. On this basis, establish the index system of the risk of besieged by to identify risks for 31 provincial capital of China, according to ranking and status give suggestion for government and improve garbage disposal issues.

The assessment of financial credit risk is an important and challenging research topic in the area of accounting and finance. Numerous efforts have been devoted into this field since the first attempt last century. Today the study of financial credit risk assessment attracts increasing attentions in the face of one of the most severe financial crisis ever observed in the world. The accurate assessment of financial credit risk and prediction of business failure play an essential role both on economics and society. For this reason, more and more methods and algorithms were proposed in the past years. From this point, it is of crucial importance to review the nowadays methods applied to financial credit risk assessment. In this paper, we summarize the traditional statistical models and state-of-the-art intelligent methods for financial distress forecasting, with the emphasis on the most recent achievements as the promising trend in this area.

It is well known that bankruptcy patterns are different across the industries, and consequently most studies focused on a given business sector or sub-sector. However, in some real world applications the bankruptcy patterns are likely constructed based on the companies of various business sectors (e.g. construction, real estate, education, retail, transportation) due to the lack of default samples. This paper presents a comparative study using different classifiers and performance metrics to examine the role of the business sector in corporate failure prediction based on the data from multiple business sectors. The experiments use a real-world French database of corporate companies diversified in different industries. The bankruptcy of companies is forecasted using 10 prediction models and evaluated by 8 performance metrics. The experimental results are analyzed by means of multidimensional scaling, which transforms the high dimensional metric data into a 2D space through a pairwise distance preserving projection. Through the interpretation of the results, we can find that the business sector used as a predictor significantly enhances the exploratory power of prediction models. Moreover, by analyzing the results there is evidence that not only tangible benefits are attained with business sector information but also the advanced ensemble approaches yield good to excellent improvements in this financial setting.

Ensemble is a recently emerged computing technique to provide promising decisions by a consensus of multiple classifiers. The benefit of classifier ensembles has been demonstrated in a vast number of studies in the scope of credit risk management. Yet the performance of different ensemble models was rarely compared when the costs of misclassification errors are asymmetric. In this paper, we concentrate on the performance of 6 ensemble techniques in the context of cost-sensitive credit scoring using 3 financial data sets. The ensemble models are built on the basis of a set of component classifiers derived from different subsets of instances or features by a single learning algorithm. The performance of classifiers is evaluated in terms of expected misclassification cost and compared by nonparametric significance test. The experimental results demonstrate that the functionality of ensembles for boosting the performance of individual classifiers is closely related to the underlying learning algorithms and the employed ensemble techniques.

可减缓性评价是衡量突发事件减缓程度的评价方法,对突发事件发生时的应急决策具有重要意义.从分析原有可减缓性评价模型的不足出发,阐述了减缓的方式、作用以及可减缓性评价的内涵,从新的角度提出了可以应用于事前和事中的可减缓性评价模型,并提出了减缓的必要性模型,用以回答突发事件救援时是否要采取减缓措施、什么时候采取减缓措施等问题.然后探讨了可减缓性评价所需信息获取方式,提出了以物联网为依托的可减缓性评价信息获取方式.最后以火灾和台风为例进行了算例分析,验证了模型的有效性.研究结果表明,新的可减缓性评价模型可以得到更为客观合理的评价结果,为不同类型突发事件的应急决策提供辅助支持.