Risk Assessment of Deep-Water Horizontal X-Tree Installation

Due to the high potential risk and many influencing factors of subsea horizontal X-tree installation,to guarantee the successful completion of sea trials of domestic subsea horizontal X-trees,this paper established a modular risk evaluation model based on a fuzzy fault tree.First,through the analysis of the main process oftree down and combining the Offshore&Onshore Reliability Data(OREDA)failure statistics and the operation procedure and the data provided by the job,the fault tree model of risk analysis of the tree down installation was established.Then,by introducing the natural language of expert comprehensive evaluation and combining fuzzy principles,quantitative analysis was carried out,and the fuzzy number was used to calculate the failure probability of a basic event and the occurrence probability of a top event.Finally,through a sensitivity analysis of basic events,the basic events of top events significantly affected were determined,and risk control and prevention measures for the corresponding high-risk factors were proposed for subsea horizontal X-tree down installation.

A risk assessment method considering risk attributes and work safety informational needs and its application

The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility.

Development of Localized Assessment of Municipal Wastewater Disposal Risks

A means to develop a comparative assessment of the risks of available wastewater effluent disposal options on a local scale needs to be developed to help local decision-makers make decisions on options such as direct or indirect potable reuse options. These options have garnered more interest as a result of water supply limitations in many urban areas. This risk assessment was developed from a risk assessment developed at the University of Miami in 2001 and Florida Atlantic University (FAU) in 2023. Direct potable reuse and injection wells were deemed to have the lowest risk in the most recent study by FAU. However, the injection well option may not be available everywhere. As a result, a more local means to assess exposure risk is needed. This paper outlines the process to evaluate the public health risks associated with available disposal alternatives which may be very limited in some areas. The development of exposure pathways can help local decision-makers define the challenges, and support later expert level analysis upon which public health decisions are based.

Distribution, health and ecological risk assessments of trace elements in Nigerian oil sands

The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. In the present study, we investigated the distribution pattern of 18trace elements(including biophile and chalcophile elements) as well as the estimated risks associated with exposure to these elements. The results of the study indicated that Fe was the most abundant element, with a mean concentration of 22,131 mg/kg while Br had the lowest mean concentration of 48 mg/kg. The high occurrence of Fe and Ti suggested a possible occurrence of ilmenite(Fe TiO_(3)) in the oil sands. Source apportionment using positive matrix factorization showed that the possible sources of detected elements in the oil sands were geogenic, metal production, and crustal. The contamination factor, geo-accumulation index, modified degree of contamination, pollution load index, and Nemerow pollution index indicated that the oil sands are heavily polluted by the elements. Health risk assessment showed that children were relatively more susceptible to the potentially toxic elements in the oil sands principally via ingestion exposure route(HQ > 1E-04). Cancer risks from inhalation are unlikely due to CR < 1E-06 but ingestion and dermal contact pose severe risks(CR > 1E-04). The high concentrations of the elements pose serious threats due to the potential for atmospheric transport, bioaccessibility, and bioavailability.

An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Risk assessment of oil and gas investment environment in countries along the Belt and Road Initiative

With the implementation of the Belt and Road Initiative, China is deepening its cooperation in oil and gas resources with countries along the Initiative. In order to better mitigate risks and enhance the safety of investments, it is of significant importance to research the oil and gas investment environment in these countries for China's overseas investment macro-layout. This paper proposes an indicator system including 27 indicators from 6 dimensions. On this basis, game theory models combined with global entropy method and analytic hierarchy process are applied to determine the combined weights, and the TOPSIS-GRA model is utilized to assess the risks of oil and gas investment in 76 countries along the Initiative from 2014 to 2021. Finally, the GM(1,1) model is employed to predict risk values for 2022-2025. In conclusion, oil and gas resources and political factors have the greatest impact on investment environment risk, and 12 countries with greater investment potential are selected through cluster analysis in conjunction with the predicted results. The research findings may provide scientific decisionmaking recommendations for the Chinese government and oil enterprises to strengthen oil and gas investment cooperation with countries along the Belt and Road Initiative.

Fuzzy Risk Assessment Method for Airborne Network Security Based on AHP-TOPSIS

With the exponential increase in information security risks,ensuring the safety of aircraft heavily relies on the accurate performance of risk assessment.However,experts possess a limited understanding of fundamental security elements,such as assets,threats,and vulnerabilities,due to the confidentiality of airborne networks,resulting in cognitive uncertainty.Therefore,the Pythagorean fuzzy Analytic Hierarchy Process(AHP)Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)is proposed to address the expert cognitive uncertainty during information security risk assessment for airborne networks.First,Pythagorean fuzzy AHP is employed to construct an index system and quantify the pairwise comparison matrix for determining the index weights,which is used to solve the expert cognitive uncertainty in the process of evaluating the index system weight of airborne networks.Second,Pythagorean fuzzy the TOPSIS to an Ideal Solution is utilized to assess the risk prioritization of airborne networks using the Pythagorean fuzzy weighted distance measure,which is used to address the cognitive uncertainty in the evaluation process of various indicators in airborne network threat scenarios.Finally,a comparative analysis was conducted.The proposed method demonstrated the highest Kendall coordination coefficient of 0.952.This finding indicates superior consistency and confirms the efficacy of the method in addressing expert cognition during information security risk assessment for airborne networks.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

BACKGROUND Gastrointestinal neoplasm(GN)significantly impact the global cancer burden and mortality,necessitating early detection and treatment.Understanding the evolution and current state of research in this field is vital.AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research,focusing on key contributors,institutions,and thematic evolution.METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the"bibliometrix"R package,VOSviewer,and CiteSpace.The analysis focused on the distribution of publications,contributions by institutions and countries,and trends in keywords.The methods included data synthesis,network analysis,and visualization of international collaboration networks.RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration.It highlights the United States'critical role in advancing this field,with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute.The last five years,substantial advancements have been made,representing nearly 45%of the examined literature.Publication rates have dramatically increased,from 20 articles in 2002 to 112 in 2022,reflecting intensified research efforts.This study underscores a growing trend toward interdisciplinary and international collaboration,with the Journal of Clinical Oncology standing out as a key publication outlet.This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks.CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis.This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy,ultimately enhancing worldwide patient care.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

Importance of risk assessment,endoscopic hemostasis,and recent advancements in the management of acute non-variceal upper gastrointestinal bleeding

Acute non-variceal upper gastrointestinal bleeding(ANVUGIB)is a common medical emergency in clinical practice.While the incidence has significantly reduced,the mortality rates have not undergone a similar reduction in the last few decades,thus presenting a significant challenge.This editorial outlines the key causes and risk factors of ANVUGIB and explores the current standards and recent updates in risk assessment scoring systems for predicting mortality and endoscopic treatments for achieving hemostasis.Since ANUVGIB predominantly affects the elderly population,the impact of comorbidities may be responsible for the poor outcomes.A thorough drug history is important due to the increasing use of antiplatelet agents and anticoagulants in the elderly.Early risk stratification plays a crucial role in deciding the line of management and predicting mortality.Emerging scoring systems such as the ABC(age,blood tests,co-morbidities)score show promise in predicting mortality and guiding clinical decisions.While conventional endoscopic therapies remain cornerstone approaches,novel techniques like hemostatic powders and over-the-scope clips offer promising alternatives,particularly in cases refractory to traditional modalities.By integrating validated scoring systems and leveraging novel therapeutic modalities,clinicians can enhance patient care and mitigate the substantial morbidity and mortality associated with ANVUGIB.

Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China

This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.

Comprehensive Risk Assessment of Sea Level Rise and Tropical Cyclones in Dongzhaigang Mangroves,China

Mangroves play a pivotal role in tropical and subtropical coastal ecosystem,yet they are highly vulnerable to the effects of climate change,particularly the accelerated global sea level rise(SLR)and stronger tropical cyclones(TCs).However,there is a lack of research addressing future simultaneous combined impacts of the slow-onset of SLR and rapid-onset of TCs on China's mangroves.In order to develop a comprehensive risk assessment method considering the superimposed effects of these two factors and analyze risk for mangroves in Dongzhaigang,Hainan Island,China,we used observational and climate model data to assess the risks to mangroves under low,intermediate,and very high greenhouse gas(GHG)emission scenarios(such as SSP1-2.6,SSP2-4.5,and SSP5-8.5)in 2030,2050,and 2100,and compiled a risk assessment scheme for mangroves in Dongzhaigang,China.The results showed that the combined risks from SLR and TCs will continue to rise;however,SLRs will increase in intensity,and TCs will decrease.The comprehensive risk of the Dongzhaigang mangroves posed by climate change will remain low under SSP1-2.6 and SSP2-4.5 scenarios by 2030,but it will increase substantially by 2100.While under SSP5-8.5 scenario,the risks to mangroves in Dongzhaigang are projected to increase considerably by 2050,and approximately 68.8%of mangroves will be at very high risk by 2100.The risk to the Dongzhaigang mangroves is not only influenced by the hazards but also closely linked to their exposure and vulnerability.We therefore propose climate resilience developmental responses for mangroves to address the effects of climate change.This study for the combined impact of TCs and SLR on mangroves in Dongzhaigang,China can enrich the method system of mangrove risk assessment and provide references for scientific management.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.

Application Effect of Stratified Nursing Intervention for ICU Mechanically Ventilated Patients in the Context of Aspiration Risk Assessment

Objective:To explore the application effect of stratified nursing intervention based on the background of misinspiration risk assessment in mechanically ventilated patients in intensive care unit(ICU).Methods:100 cases of mechanically ventilated patients who were admitted to the ICU of our hospital from March 2022 to March 2023 were selected and divided into an observation group and a control group according to the random number table method,with 50 cases in each of the two groups.The control group was given routine care in ICU,and the observation group was given stratified nursing interventions based on the background of the risk of aspiration assessment on the basis of the control group,and both groups were cared for until they were transferred out of the ICU,and the mechanical ventilation time,ICU stay time,muscle strength score,complication rate,adherence,and satisfaction were observed and compared between the two groups.Results:The mechanical ventilation time and ICU stay time of the observation group were shorter than that of the control group after the intervention;the muscle strength score,compliance and satisfaction of the observation group were higher than that of the control group after the intervention;and the complication rate of the observation group was lower than that of the control group after the intervention,all of which were P<0.05.Conclusion:The application of stratified nursing intervention based on the background of misaspiration risk assessment in ICU mechanically ventilated patients can improve the patient's muscle strength,shorten the time of mechanical ventilation,promote the patient's recovery,reduce the occurrence of complications,and improve the patient's compliance and satisfaction.

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.

Risk Assessment and Control in Medical Investment,Merger,and Acquisition

The risk assessment and control of medical investment,merger,and acquisition are crucial topics within the medical industry,encompassing various aspects of investment,merger,and acquisition within this sector.The process primarily targets the unique nature and associated risks of the medical industry,focusing on effective risk management and control strategies to facilitate the smooth progression of investment,merger,and acquisition activities.

Health risk assessment of heavy metals in soils and crops in a mining area(Au-Ag-Cutrona-oil et al.) of the Nanyang Basin, Henan Province, China

Heavy metal distribution in mining areas has always been a hot research topic due to the special environment of these areas. This study aims to explore the impact of heavy metal pollution on soils and crops in the study area, ensure the safety of local crops and the health of local residents, and provide a basis for the subsequent environmental restoration and the prevention and control of environmental pollution. Based on the analysis of the heavy metal concentrations in local soils and crops, the study investigated the spatial distribution, pollution degrees, and potential ecological risks of heavy metals in the farmland of a mining area in the southeastern Nanyang Basin, Henan province, China explored the sources of heavy metals and assessed the health risks caused by crop intake. The results of this study are as follows. The root soils of crops in the study area suffered heavy metal pollution to varying degrees. The degree of heavy metal pollution in maize fields is higher than that in wheat fields, and both types of fields suffer the most severe Cd pollution. Moreover, the root soils of different crops suffer compound pollution.The root soils in the maize fields suffer severe compound pollution at some sampling positions, whose distribution is similar to that of the mining area. Cd poses the highest potential ecological risks among all heavy metals, and the study area mainly suffers low and moderate comprehensive potential ecological risks. The principal component analysis(PCA) shows that the distribution of Zn, Cd, Pb, and As in soils of the study area is mainly affected by anthropogenic factors such as local mining activities;the distribution of Cr and Ni is primarily controlled by the local geological background;the distribution of Hg is mainly affected by local vehicle exhaust emissions, and the distribution of Cu is influenced by both human activities and the geological background. Different cereal crops in the study area are polluted with heavy metals dominated by Cd and Ni to varying degrees, especially wheat. As indicated by the health risk assessment results, the intake of maize in the study area does not pose significant human health risks;however, Cu has high risks to human health, and the compound heavy metal pollution caused by the intake of wheat in the study area poses risks to the health of both adults and children. Overall, the soils and crops in the study area suffer a high degree of heavy metal pollution, for which mining activities may be the main reason.

Assessment of Heavy Metals Contamination of Topsoil and Street Dust around Cement Factory in Southern Jordan

Evaluation of assessment of the metal processes governing the metals distribution in soil and dust samples is very significant and protects the health of human and ecological system. Recently, special attention has given to the assessment of metals pollution impact on soil and dust within industrial areas. This study aims to assess the metal contamination levels in the topsoil and street dust around the cement factory in Qadissiya area, southern Jordan. The levels of seven metals (namely Fe, Zn, Cu, Pb, Cr, Cd, and Mn) were analyzed using Flame Atomic Absorption Spec-trophotometer (FAAS) to monitor, evaluate, and to compare topsoil and road dust pollution values of metals of the different types of urban area. The physicochemical parameters which believed to affect the mobility of metals in the soil of the study area were determined such as pH, EC, TOM, CaCO3 and CEC. The levels of metal in soil samples are greater on the surface but decrease in the lower part as a result of the basic nature of soil. The mean values of the metals in soil can be arranged in the following order: Zn > Pb > Mn > Fe > Cu > Cr > Cd. The relatively high concentration of metals in the soil sample was attributed to anthropogenic activities such as traffic emissions, cement factory and agricultural activities. Correlation coefficient analysis and the spatial distribution of indices and the results of statistical analysis indicate three groups of metals: Fe and Mn result by natural origin, Zn, Pb, Cu and Zn result by anthropogenic origin (mainly motor vehicle traffic and abrasion of tires) while Cd is mixed origin. The higher content level values of metals of anthropogenic source in soil samples indicate that it is a source of contamination of air in the studied area. .