Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification

A spatiotemporal analysis of personal casualty accidents in China's electric power industry

The electric power industry in China has experienced significant growth in recent years. Despite efforts to improve safety management in the industry, accidents still occur frequently. This study aimed to analyze the personal casualty accidents in the electric power industry from 2012 to 2021. Specific methods used include descriptive analysis, principal component analysis, and Theil index model. The results indicated that fall, electric shock, and collapse were the primary types of accidents, accounting for 59.65 % of all accidents. Accidents were higher in April and August, but lower in February. While the accident rate was relatively low on Mondays, the fatality rate was higher on Mondays, Thursdays, and Fridays. Taking into account accidents, workload, and labor, we found that Ningxia, Hainan, and Guangxi exhibited subpar levels of safety management within the electric power industry. The overall difference in the number of deaths in 31 provinces was significant in 2012 and 2016. It was significantly reduced in 2021. In terms of the proportion of intraregional and interregional differences, there were significant differences in the number of accidents and fatalities between provinces in the Central China and North China regions. This study provides valuable insights for enterprises to formulate accident prevention strategies and for the government to develop relevant policies.

Regional Sustainable Performance of Construction Industry in China from the Perspective of Input and Output: Considering Occupational Safety

Improving the poor sustainability of the construction industry requires long-term actions, especially in developing countries such as China. Regional sustainability assessment plays an indispensable role, contributing to a better understanding of the state of development in various regions. However, few studies have focused on the overall sustainability of regional construction industries, and occupational safety is generally ignored. To fill these gaps, an input-output system is established to evaluate regional sustainable performance of the construction industry (SPCI), which is made to include occupational safety by introducing the number of fatalities as an undesirable output. An evaluation model is constructed by combining window analysis with a super-slack-based measure data envelopment analysis (windows-super-SBM DEA). The SPCI in China’s 30 provinces from 2010 to 2017 is dynamically evaluated, and regional differences are further analyzed, with eight regions being defined. The results indicate that (1) the overall SPCI in China has fluctuated smoothly around a slight downward trend. By comparison, the integration of occupational safety refreshes the relative performance of most provinces; (2) dividing China into eight regions presents more detailed information because of those regions’ smaller coverage areas, and more attention should be given to the northeast, northwest, Middle Yellow River region and east coast because of the decrease in the SPCI; and (3) vigorously developing of the construction industry does not necessarily result in a large number of byproducts if the relevant policy is sufficiently strong. The findings of this study are conducive to rationally allocating resources and formulating targeted policies.

HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps

Established Internet of Things (IoT) platforms suffer from their inability to determine whether an IoT app is secure or not. A security analysis system (SAS) is a protective shield against any attack that breaks down data privacy and security. Its main task focuses on detecting malware and verifying app behavior. There are many SASs implemented in various IoT applications. Most of them build on utilizing static or dynamic analysis separately. However, the hybrid analysis is the best for obtaining accurate results. The SAS provides an effective outcome according to many criteria related to the analysis process, such as analysis type, characteristics, sensitivity, and analysis techniques. This paper proposes a new hybrid (static and dynamic) SAS based on the model-checking technique and deep learning, called an HSAS-MD analyzer, which focuses on the holistic analysis perspective of IoT apps. It aims to analyze the data of IoT apps by (1) converting the source code of the target applications to the format of a model checker that can deal with it; (2) detecting any abnormal behavior in the IoT application; (3) extracting the main static features from it to be tested and classified using a deep-learning CNN algorithm; (4) verifying app behavior by using the model-checking technique. HSAS-MD gives the best results in detecting malware from malicious smart Things applications compared to other SASs. The experimental results of HSAS-MD show that it provides 95%, 94%, 91%, and 93% for accuracy, precision, recall, and F-measure, respectively. It also gives the best results compared with other analyzers from various criteria.