Safety Risk Assessment Analysis of Bridge Construction Using Backpropagation Neural Network

The evaluation of construction safety risks has become a crucial task with the increasing development of bridge construction.This paper aims to provide an overview of the application of backpropagation neural networks in assessing safety risks during bridge construction.It introduces the situation,principles,methods,and advantages,as well as the current status and future development directions of backpropagation-related research.

Patient Safety Risks and Measures for Pediatric Neurosurgery Nursing

The aim of this study was to determine the patient safety risks and measures for pediatric neurosurgery nursing.A total of 564 pediatric patients admitted to the hospital from June 2020 to June 2023 under the neurosurgery department were included in this study.We analyzed the safety incidents in pediatric neurosurgery nursing and their causes and proposed corresponding measures for pediatric neurosurgery nursing in hope to reduce the occurrence of patient safety incidents in pediatric neurosurgery nursing and establish harmonious nurse-patient relationships.

Safety risk management of underground engineering in China：Progress, challenges and strategies

Underground construction in China is featured by large scale, high speed, long construction period,complex operation and frustrating situations regarding project safety. Various accidents have been reported from time to time, resulting in serious social impact and huge economic loss. This paper presents the main progress in the safety risk management of underground engineering in China over the last decade, i.e.（1） establishment of laws and regulations for safety risk management of underground engineering,（2） implementation of the safety risk management plan,（3） establishment of decision support system for risk management and early-warning based on information technology, and（4） strengthening the study on safety risk management, prediction and prevention. Based on the analysis of the typical accidents in China in the last decade, the new challenges in the safety risk management for underground engineering are identified as follows：（1） control of unsafe human behaviors;（2） technological innovation in safety risk management; and（3） design of safety risk management regulations. Finally, the strategies for safety risk management of underground engineering in China are proposed in six aspects, i.e. the safety risk management system and policy, law, administration, economy, education and technology.

An intelligent identification method of safety risk while drilling in gas drilling

In view of the shortcomings of current intelligent drilling technology in drilling condition representation, sample collection, data processing and feature extraction, an intelligent identification method of safety risk while drilling was established. The correlation analysis method was used to determine correlation parameters indicating gas drilling safety risk. By collecting monitoring data in the safety risk period of more than 20 wells, a sample database of a variety of safety risks in gas drilling was established, and the number of samples was expanded by using the method of few-shot learning. According to the forms of gas drilling monitoring data samples, a two-layer convolution neural network architecture was designed, and multiple convolution cores of different sizes and weights were set to realize the vertical and horizontal convolution computations of samples to extract and learn the variation law and correlation characteristics of multiple monitoring parameters. Finally, based on the training results of neural network, samples of different kinds of safety risks were selected to enhance the recognition accuracy. Compared with the traditional BP(error back propagation) full-connected neural network architecture, this method can more deeply and effectively identify safety risk characteristics in gas drilling, and thus identify and predict risks in advance, which is conducive to avoid and quickly solve safety risks while drilling. Field application has proved that this method has an identification accuracy of various safety risks while drilling in the process of gas drilling of about 90% and is practical.

Research on Safety Risk Assessment Method for Large Scale Field Operation Project

By applying man-machine-environment system engineering theory, safety risks on large scale field operation project have been evaluated in this article. The factors concerning with the man, machine and environment in system were proposed separately. The value for lowest indexs was determined by decision-making of expert group. The weights were calculated based on AHP, and then safety risk assessment in different layers was made. The results show that the assessment method is reasonable, and it is significant for large scale field operation project safety managerment.

Data-driven short circuit resistance estimation in battery safety issues

Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.

Fatigue Analysis of Liquefied Petroleum Gas Cylinders for Safety Risk Assessment

With the widespread application of liquefied petroleum gas (LPG),the safety of LPG cylinder has received more and more attention.For the safety of LPG cylinder,we conduct a safety risk assessment of cylinder using the failure mode and effect analysis (FMEA) method.Taking the most influential inflatable fatigue under normal conditions as the research object,we use FE-safe software to analyze the fatigue failure.The risk compliance coefficients of various failure modes are calculated and classified according to the risk level.In this way,the service life of the LPG cylinder weld is determined.The presented method improves the safety risk assessment process of LPG cylinder and provides a good theoretical and practical basis for similar pressure vessel risk assessment.

Protective performance test and safety risk evaluation of a powered air-purifying suit

The positive pressure biological protective suit is the highest degree of personal protective equipment, and its performance directly impacts the health and safety of the wearer. Protection factor is the most critical measure of the performance of a positive pressure bio-protective suit. This study establishes a dynamic detection system for the positive pressure bio-protective suit, to evaluate its safety risks, and explores methods to reduce these risks. A manikin was used to perform human actions, such as raising the arms, sitting, and walking, that were simulated at different levels. Integrity breaches of different shapes and sizes were made to different parts of the suit for evaluation. The aerosol concentration and the pressure of the suit were detected. Positive-pressure suits provide exceptionally good protection even in accident scenarios and provide a greater flexibility and more ergonomic benefits. However, sufficiently large (>3 cm) breaches caused a negative-pressure and an inward airflow during vigorous activities. The location of the breach site on the suit also had a significant effect on bio-aerosol leakage. Studies have shown that effective methods to avoid the risk of damage include increasing the air supply flow and performing gentle movements while wearing the suit. The dynamic detection method and the results obtained in this study are a significant advance to predict and avoid the risks associated with powered air-purifying suits.

Environmental risk for application of ammonia-soda white mud in soils in China

In recent years,some reports,mainly from Chinese research,show that there has been an increasing trend in the use of ammonia-soda residue(ASR)(or called ammonia-soda white mud) as a soil conditioner in farmlands.Up to now,the studies on ASR have focused on its utilization for acid soil amendment in agriculture,but few studies have assessed its environmental risk.ASR contains pollutant elements such as mercury(Hg),cadmium(Cd),copper(Cu) and fluorine(F) and the purpose of this study was to review research on the environmental impacts of ASR application in agriculture.Observations obtained from 23 research reports indicate that the concentrations of Hg,Cd,Cu,F and Cl(0-170,0.01-2.8,4.5-200,2000-24700 and 1 600-188 000 mg kg^-1,respectively) in ASR may exceed the limits(≤0.5,≤0.3 and ≤50 mg kg^-1 for Hg,Cd and Cu,respectively) of Chinese Risk Screening Values for Soil Contamination of Agricultural Land(GB 15618-2018 2018) or the refereed critical value(≤800 and ≤200 mg kg^-1 for F and Cl,respectively) based on Chinese research.The concentrations of the elements Hg,Cd,Cu,F and Cl in the leachate of ASR detected by the extraction tests also exceed the limits(Class IV-V) of the Chinese Standard for Groundwater Quality(GB/T 14848-2017 2017).Based on the above results,it is suggested that ASR without any pretreatment for reducing harmful pollutants should not be used for soil remediation or conditioning of farmlands,to ensure soil health,food safety and environmental quality.

Risk Analysis of Big Data Based on Cloud Computing for the Inspection and Testing of Toxic and Hazardous Substances in Meat Products

Large-scale data emerge in food safety inspection and testing industry with the development of Internet technology in China.This paper was aimed at designing toxic and hazardous substance big data risk analysis algorithm in food safety inspection and testing based on cloud computing^([1]).Cloud computing platform was set up to store the massive extensive data with geographical distribution,dynamic and high complexity from the Internet,and MapReduce^([2]) computational framework in cloud computing was applied to process and compute parallel data.The risk analysis results were obtained by analyzing 1000000 meat products testing data collected from the laboratory management information system based on web.The results show that food safety index IFS < 1,which proves that the food safety state is in good condition.

Effect of Food Safety Training on Achieving Good Hygiene Practices in Restaurants in the Emirates of Dubai

Food safety,specifically in restaurants,is becoming a key public health priority because of the increased number of meals eaten outside the home.Foodborne illness prevention thus is a significant concern and a public health priority in the United Arab Emirates,particularly Dubai,because of the extensive tourism industry.The purpose of the study was to evaluate the effectiveness of using demonstrations in training sessions to improve food safety knowledge and practices amongst food handlers.A descriptive and quantitative approach has been applied to collect the quantifiable information related to the research study.This has been further analyzed using the correlation tests to gather the required data.On comparison of the pre-test scores between the intervention and the control group,the t-test analysis showed significant difference in the level of food safety knowledge between the two groups.Pre-test score for the control group was 78.33 and post-test score was 104.66.In the case of the intervention group,pre-test score was 91.37 and post-test score was 130.75.The scores of food handlers’food safety practice for control group:pre-treatment score was 470 and post-treatment score was 646.For intervention group:pre-test score was 723 and post-test score was 1,056.The study concluded that training with demonstration techniques is an effective way of improving compliance with food safety guidelines.It has been understood that training helps in improving the performance of the employees while reducing the foodborne diseases and maintaining hygiene in the food.The study recommends every restaurant needs to provide regular trainings to the employees so that the restaurants can maintain hygiene and food safety practices.

Skeleton-based Violation Action Recognition Method for Safety Supervision in Operation Field of Distribution Network Based on Graph Convolutional Network

Safety accidents in the operation field of the distribution network often occur,which seriously endanger the safety and lives of operators.Existing identification methods for safety risk can identify static safety risks,such as no-helmet,no-safety gloves,etc.,but fail to identify risks in the dynamic actions of operators.Therefore,this paper proposes a skeletonbased violation action-recognition method for supervision of safety during operations in a distribution network,i.e.,based on spatial temporal graph convolutional network(STGCN)and key joint attention module(KJAM),which can implement dynamic violation behavior recognition of operators.In this method,the human posture estimation method,i.e.Multi-Person Pose Estimation,is utilized to extract the skeleton information of operators during operations,and to construct an undirected graph,which reflects the movement and posture of the human body.Then,the STGCN is utilized to identify actions of operators that can lead to dynamic violations.In addition,the KJAM captures important joint information of operators.The effectiveness and superiority of the proposed method are verified in comparison to other action recognition methods.The experimental results show that the proposed method has higher recognition accuracy for common violations collected at the actual operation site of the distribution network and shows a strong generalization ability,which can be applied to the video monitoring system of field operations to reduce the occurrence of safety accidents.

Environmental risks for application of iron and steel slags in soils in China: A review

Iron and steel slags are smelting wastes, mainly including blast furnace slag(BFS) and steel slag(SS) produced in the iron and steel industry. Utilization of iron and steel slags as resources for solving the problem of slag disposals has attracted much attention with increasing iron and steel smelting slags in China. Because the iron and steel slags contain calcium(Ca), magnesium(Mg), phosphorus(P), and silicon(Si), some have tried to use them as Si-and P-fertilizers, for producing Ca-Mg-P fertilizers, or as soil amendments in agriculture. However, in the iron metallurgical process, several pollutants in iron ores can inevitably transfer into iron and steel slags, resulting in the enrichment of pollutants both in BFS(mainly nickel(Ni), copper(Cu), mercury, zinc(Zn),cadmium(Cd), chromium(Cr), arsenic, lead, selenium, fluorine(F), and chlorine(Cl)) and in SS(mainly Ni, Cr, Cd, Zn, Cu, F, and Cl), in which some of pollutants(especially Cr, Ni, F, and Cl) exceed the limits of environmental quality standards for soils and groundwater. The elements of manganese, barium,and vanadium in iron and steel slags are higher than the background values of soil environment. In order to ensure soil health, food safety, and environmental quality, it is suggested that those industrial solid wastes, such as iron and steel slags, without any pretreatment for reducing harmful pollutants and with environmental safety risk, should not be allowed to use for soil remediation or conditioning directly in farmlands by solid waste disposal methods, to prevent pollutants from entering food chain and harming human health.

Safety Analysis of Flow Parameters in a Rotor-stator Cavity

In order to ensure the safety of engine life limited parts （ELLP） according to airworthiness regulations, a numerical approach integrating one-way fluid structure interaction （FSI） and probabilistic risk assessment （PRA） is developed, by which the variation of flow parameters in a rotor-stator cavity on the safety of gas turbine disks is investigated. The results indicate that the flow parameters affect the probability of fracture of a gas turbine disk since they can change the distribution of stress and temperature of the disk. The failure probability of the disk rises with increasing rotation Reynolds number and Chebyshev number, but descends with increasing inlet Reynolds number. In addition, a sampling based sensitivity analysis with finite difference method is conducted to determine the sensitivities of the safety with respect to the flow parameters. The sensitivity estimates show that the rotation Reynolds number is the dominant variable in safety analysis of a rotor-stator cavity among the flow parameters.

Pedestrian risk analysis at uncontrolled midblock and unsignalised intersections

Transportation is defined as port to port transfer of person or goods by a medium which can be a vehicle or a person. Pedestrians being the most neglected mode of transportation in terms of safety and facility, face difficult situations while crossing near intersections and midblock crossings. It becomes more of a risk when the place of crossing is uncontrolled.But if behaviour of pedestrians while crossing is analysed in such conditions, it might be possible to create suitable solution to lessen the risk and ensure safety. In most of the cities, accepting suitable gaps between vehicles in uncontrolled midblock and intersection crossings pose threat to pedestrians＇ safety. The present study examines the safety of pedestrian crossing behaviour at midblock and unsignalised intersection crossings.Crossing time, speed, stages of crossing, number of interruptions while crossing, and the type of vehicles for which pedestrians accept the gap were extracted from the video. The tendency to show rolling gap behavior was observed and examined for different age and gender groups to analyse the risk involved in such type of crossings. The risks analysed from the study in correlation with the pedestrian demand in such uncontrolled crossings will help in design of safer pedestrian facilities. It was observed that the size of the vehicle has a significant influence on gap acceptance and crossing behaviour of pedestrians. Male pedestrians take more risks than female pedestrians in crossing unsignalized intersections. Middle aged pedestrian category poses 60.1% more chances of interrupted crossing than the other elder and young age categories of pedestrians. Male pedestrian category and the middle aged pedestrian category are more tended to accept the smallest gap between the vehicles showing a risky nature of crossing.

Estimating likelihood of future crashes for crash-prone drivers

At-fault crash-prone drivers are usually future incidents or crashes. In Louisiana, considered as the high risk group for possible 34% of crashes are repeatedly committed by the at-fault crash-prone drivers who represent only 5% of the total licensed drivers in the state. This research has conducted an exploratory data analysis based on the driver faultiness and proneness. The objective of this study is to develop a crash prediction model to esti- mate the likelihood of future crashes for the at-fault drivers. The logistic regression method is used by employing eight years＇ traffic crash data （2004-2011） in Louisiana. Crash predictors such as the driver＇s crash involvement, crash and road characteristics, human factors, collision type, and environmental factors are considered in the model. The at-fault and not-at-fault status of the crashes are used as the response variable. The developed model has identified a few important variables, and is used to correctly classify at-fault crashes up to 62.40% with a specificity of 77.25%. This model can identify as many as 62.40% of the crash incidence of at-fault drivers in the upcoming year. Traffic agencies can use the model for monitoring the performance of an at-fault crash-prone drivers and making roadway improvements meant to reduce crash proneness. From the findings, it is recommended that crash-prone drivers should be targeted for special safety programs regularly through education and regulations.