基于Bow-Tie-LOPA的煤矿安全风险评估方法应用研究

为解决传统安全评估方法的局限性,有效降低煤矿安全事故、提升煤矿安全管理效能,提出基于Bow-Tie模型的LOPA煤矿安全风险评估方法,通过研究两种方法的概念并分析两种方法相结合的应用,综合应用Bow-Tie-LOPA方法进行煤矿安全风险评估,分析验证Bow-Tie-LOPA方法在煤矿安全风险评估中的可行性和有效性;利用Bow-Tie模型绘制煤矿安全风险事件因果链,找出可供LOPA方法分析的事故场景,并基于LOPA分析风险事件发生率和后果,利用风险矩阵和确定风险等级的方法,进一步评估事故场景,以确定煤矿安全风险后果以及安全保护措施。研究结果表明:LOPA方法的引入有利于Bow-Tie模型分析煤矿复杂事故和危机情境,提高煤矿安全风险管理水平。

Solution to multiple attribute group decision making problems with two decision makers

A kind of multiple attribute group decision making （MAGDM） problem is discussed from the perspective of statistic decision-making. Firstly, on the basis of the stability theory, a new idea is proposed to solve this kind of problem. Secondly, a con- crete method corresponding to this kind of problem is proposed. The main tool of our research is the technique o~ the jackknife method. The main advantage of the new method is that it can identify and determine the reliability degree of the existed decision making information. Finally, a traffic engineering example is given to show the effectiveness of the new method.

Shared decision-making in the management of patients with inflammatory bowel disease

The rapid progress of research into inflammatory bowel disease(IBD)has resulted in increasingly more treatment options.Different options have different advantages and disadvantages,and the preferences of patients may also differ.If patients can be invited to the formulation of medical decision-making,their compliance and satisfaction would be improved,thus possibly achieving better therapeutic results.The present review aims to summarize the current literature on shared decision-making(SDM)in the management of IBD,with the goal of promoting the application of SDM.

Method for triangular fuzzy multiple attribute decision making based on two-dimensional density operator method

Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.

基于Ansys/Bow-Tie燃气泄漏的双向风险分析

燃气泄漏事故伴随着爆炸、中毒等多项风险威胁,造成经济损失和伤亡损失。利用Ansys/Bow-Tie建立扩散模型与分析计算模型,将燃气泄漏定性与定量分析。研究表明:在定性变化中,燃气泄漏后泄漏区域的燃气扩散速度呈指数大模块式上升,扩散速度极快;在定量变化中,燃气泄漏的主要风险来源于巡检不足以及检维修不及时可能发生的概率最大,分别为0.039和0.046,通过加装有毒有害气体仪表报警监测群和对大风险问题加强监控可为风险管控提供帮助。

基于STPA和Bow-Tie模型的地面无人平台系统安全分析方法

随着地面无人平台(Unmanned Ground Vehicles,UGVs)在复杂作业环境中的潜在应用和战略价值日益凸显,确保其自主行为的安全性变得至关重要。提出一种结合系统理论过程分析(System-Theoretic Process Analysis,STPA)和Bow-Tie模型的地面无人平台系统安全分析方法。围绕遥控操作地面无人平台系统安全,通过STPA方法识别UGV系统中的不安全控制行为及其潜在风险,并利用Bow-Tie模型分析从损失致因场景到可能事故后果的事件链,得到风险传播路径和风险扩散路径。最终,基于Bow-Tie分析结果确定主被动安全分级控制措施,并通过自主安全控制器实现了系统安全管理。

Tjong:A transformer‐based Mahjong AI via hierarchical decision‐making and fan backward

Mahjong,a complex game with hidden information and sparse rewards,poses significant challenges.Existing Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI capabilities.The authors propose a transformer‐based Mahjong AI(Tjong)via hierarchical decision‐making.By utilising self‐attention mechanisms,Tjong effectively captures tile patterns and game dynamics,and it decouples the decision pro-cess into two distinct stages:action decision and tile decision.This design reduces de-cision complexity considerably.Additionally,a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning hands.Tjong consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 GPUs.The action decision achieved an accuracy of 94.63%,while the claim decision attained 98.55%and the discard decision reached 81.51%.In a tournament format,Tjong outperformed AIs(CNN,MLP,RNN,ResNet,VIT),achieving scores up to 230%higher than its opponents.Further-more,after 3 days of reinforcement learning training,it ranked within the top 1%on the leaderboard on the Botzone platform.

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

Bow-tie技术在CCUS项目注入系统的应用

二氧化碳捕集、利用与封存(CCUS)技术是实现化石能源低碳化利用的重要技术手段。但CCUS全流程存在二氧化碳泄漏、冻伤、窒息以及物体打击等风险,特别是注入系统涉及高压、低温及二氧化碳多相态变化等,易造成二氧化碳腐蚀、泄漏。通过Bow-tie技术对某CCUS项目注入系统开展风险识别、分析和控制,重点对设备设施及作业活动危害因素分析并制定有效管控措施,研究结果可为CCUS注入系统长期安全平稳高效运行提供技术支持。

Two-Stage IoT Computational Task Offloading Decision-Making in MEC with Request Holding and Dynamic Eviction

The rapid development of Internet of Things(IoT)technology has led to a significant increase in the computational task load of Terminal Devices(TDs).TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing(MEC).However,existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited,and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted.In addition,existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,but lack support for multiple retry in subsequent time slots.It is resulting in TD missing potential offloading opportunities in the future.To fill this gap,we propose a Two-Stage Offloading Decision-making Framework(TSODF)with request holding and dynamic eviction.Long Short-Term Memory(LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot.The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology.Simulation results show that TSODF reduces total TD’s energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Shared decision making in rural general practices:a qualitative exploration of older rural South Australians'perceived involvement in clinical consultations with doctors

Background:Shared decision-making(SDM)implementation is a priority for Australian health systems,including general practices but it remains complex for specific groups like older rural Australians.We initiated a qualitative study with older rural Australians to explore barriers to and facilitators of SDM in local general practices.Methods:We conducted a patient-oriented research,partnering with older rural Australians,families,and health service providers in research design.Participants who visited general practices were purposively sampled from five small rural towns in South Australia.A semi-structured interview guide was used for interviews and reflexive thematic coding was conducted.Results:Telephone interviews were held with 27 participants.Four themes were identified around older rural adults’involvement in SDM:(1)Understanding of"patient involvement";(2)Positive and negative outcomes;(3)Barriers to SDM;and(4)Facilitators to SDM.Understanding of patient involvement in SDM considerably varied among participants,with some reporting their involvement was contingent on the“opportunity to ask questions”and the“treatment choices”offered to them.Alongside the opportunity for involvement,barriers such as avoidance of cultural care and a lack of continuity of care are new findings.Challenges encountered in SDM implementation also included resource constraints and time limitations in general practices.Rural knowledge of general practitioners and technology integration in consultations were viewed as potential enablers..Conclusion:Adequate resources and well-defined guidelines about the process should accompany the implementation of SDM in rural general practices of South Australia.Innovative strategies by general practitioners promoting health literacy and culturally-tailored communication approaches could increase older rural Australians'involvement in general.

Fuzzy multi-criteria decision-making method-based operational assessment of Chinese electricity markets

The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and contributes to stable and healthy market growth.This study investigated the characteristics of electricity markets in different provinces and synthesized a comprehensive set of evaluation indicators to assess market effectiveness.The evaluation framework,comprising nine indicators organized into two tiers,was constructed based on three aspects:market design,market efficiency,and developmental coordination.Furthermore,a novel fuzzy multi-criteria decision-making evaluation model for electricity market performance was developed based on the Fuzzy-BWM and fuzzy COPRAS methodologies.This model aimed to ensure both accuracy and comprehensiveness in market operation assessment.Subsequently,empirical analyses were conducted on four typical provincial-level electricity markets in China.The results indicate that Guangdong’s electricity market performed best because of its effective balance of stakeholder interests and adherence to contractual integrity principles.Zhejiang and Shandong ranked second and third,respectively,whereas Sichuan exhibited the poorest market performance.Sichuan’s electricity market must be improved in terms of market design,such that market players can obtain a fairly competitive environment.The sensitivity analysis of the constructed indicators verified the effectiveness of the evaluation model proposed in this study.Finally,policy recommendations were proposed to facilitate the sustainable development of China’s electricity markets with the objective of transforming them into efficient and secure markets adaptable to the evolution of novel power systems.

Performance of the walking trail making test in older adults with white matter hyperintensities

BACKGROUND Several studies have reported that the walking trail making test(WTMT)completion time is significantly higher in patients with developmental coordination disorders and mild cognitive impairments.We hypothesized that WTMT performance would be altered in older adults with white matter hyperintensities(WMH).AIM To explore the performance in the WTMT in older people with WMH.METHODS In this single-center,observational study,25 elderly WMH patients admitted to our hospital from June 2019 to June 2020 served as the WMH group and 20 participants matched for age,gender,and educational level who were undergoing physical examination in our hospital during the same period served as the control group.The participants completed the WTMT-A and WTMT-B to obtain their gait parameters,including WTMT-A completion time,WTMT-B completion time,speed,step length,cadence,and stance phase percent.White matter lesions were scored according to the Fazekas scale.Multiple neuropsychological assessments were carried out to assess cognitive function.The relationships between WTMT performance and cognition and motion in elderly patients with WMH were analyzed by partial Pearson correlation analysis.RESULTS Patients with WMH performed significantly worse on the choice reaction test(CRT)(0.51±0.09 s vs 0.44±0.06 s,P=0.007),verbal fluency test(VFT,14.2±2.75 vs 16.65±3.54,P=0.012),and digit symbol substitution test(16.00±2.75 vs 18.40±3.27,P=0.010)than participants in the control group.The WMH group also required significantly more time to complete the WTMT-A(93.00±10.76 s vs 70.55±11.28 s,P<0.001)and WTMT-B(109.72±12.26 s vs 82.85±7.90 s,P<0.001).WTMT-A completion time was positively correlated with CRT time(r=0.460,P=0.001),while WTMT-B completion time was negatively correlated with VFT(r=-0.391,P=0.008).On the WTMT-A,only speed was found to statistically differ between the WMH and control groups(0.803±0.096 vs 0.975±0.050 m/s,P<0.001),whereas on the WTMT-B,the WMH group exhibited a significantly lower speed(0.778±0.111 vs 0.970±0.053 m/s,P<0.001)and cadence(82.600±4.140 vs 85.500±5.020 steps/m,P=0.039),as well as a higher stance phase percentage(65.061±1.813%vs 63.513±2.465%,P=0.019)relative to controls.CONCLUSION Older adults with WMH showed obviously poorer WTMT performance.WTMT could be a potential indicator for cognitive and motor deficits in patients with WMH.

Clustering Analysis of Black-start Decision-making with a Large Group of Decision-makers

The optimization of black-start decision-making plays an important role in the rapid restoration of a power system after a major failure/outage.With the introduction of the concept of smart grids and the development of real-time communication networks,the black-start decision-makers are no longer limited to only one or a few power system experts such as dispatchers,but rather a large group of professional people in practice.The overall behaviors of a large decision-making group of decision-makers/experts are more complicated and unpredictable.However,the existing methods for black-start decision-making cannot handle the situations with a large group of decision-makers.Given this background,a clustering algorithm is presented to optimize the black-start decision-making problem with a large group of decision-makers.Group decision-making preferences are obtained by clustering analysis,and the final black-start decision-making results are achieved by combining the weights of black-start indexes and the preferences of the decision-making group.The effectiveness of the proposed method is validated by a practical case.This work extends the black-start decision-making problem to situations with a large group of decision-makers.

An Effective Prediction Method for Supporting Decision Making in Real Estate Area Selection

Real estate has been a dominant industry in many countries. One problem for real estate companies is determining the most valuable area before starting a new project. Previous studies on this issue mainly focused on market needs and economic prospects, ignoring the impact of natural disasters. We observe that natural disasters are important for real estate area selection because they will introduce considerable losses to real estate enterprises. Following this observation, we first develop a self-defined new indicator named Average Loss Ratio to predict the losses caused by natural disasters in an area. Then, we adopt the existing ARIMA model to predict the Average Loss Ratio of an area. After that, we propose to integrate the TOPSIS model and the Grey Prediction Model to rank the recommendation levels for candidate areas, thereby assisting real estate companies in their decision-making process. We conduct experiments on real datasets to validate our proposal, and the results suggest the effectiveness of the proposed method.

A Comparative Study of 3D Virtual Pattern and Traditional Pattern Making

Pattern making plays a key role in the aspect of fashion design and garment production, as it serves as the transformative process that turns a simple drawing into a consistent accumulation of garments. The process of creating conventional or manual patterns requires a significant amount of time and a specialized skill set in various areas such as grading, marker planning, and fabric utilization. This study examines the potential of 3D technology and virtual fashion designing software in optimizing the efficiency and cost-effectiveness of pattern production processes. The proposed methodology is characterized by a higher level of comprehensiveness and reliability, resulting in time efficiency and providing a diverse range of design options. The user is not expected to possess comprehensive knowledge of traditional pattern creation procedures prior to engaging in the task. The software offers a range of capabilities including draping, 3D-to-2D and 2D-to-3D unfolding, fabric drivability analysis, ease allowance calculation, add-fullness manipulation, style development, grading, and virtual garment try-on. The strategy will cause a shift in the viewpoints and methodologies of business professionals when it comes to the use of 3D fashion design software. Upon recognizing the potential time, financial, and resource-saving benefits associated with the integration of 3D technology into their design development process, individuals will be motivated to select for its utilization over conventional pattern making methods. Individuals will possess the capacity to transfer their cognitive processes and engage in introspection regarding their professional endeavors and current activities through the utilization of 3D virtual pattern-making and fashion design technologies. To enhance the efficacy and ecological sustainability of designs, designers have the potential to integrate 3D technology with virtual fashion software, thereby compliant advantages for both commercial enterprises and the environment.

飞机风挡裂纹/破裂类事件统计及Bow-tie模型分析

为分析航空器风挡裂纹/破裂类事件的安全风险,收集1 629份风挡裂纹/破裂类事件,对样本事件涉及故障状况、飞行阶段、故障原因等特征进行统计分析,确定风挡裂纹/破裂类事件的可能原因、潜在后果及其预防、减缓控制措施并绘制了风挡裂纹/破裂类事件Bow-tie模型图。结果表明:风挡损伤的主要情形包括裂开、裂缝、结冰、烟雾、划伤等;涉及风挡及相关部件故障以加温故障、雨刷故障为主;飞行阶段以巡航、爬升为主;风挡裂纹/破裂事件的主要原因为鸟击、零部件缺失/故障、系统失效/故障、冰击,最终有针对性地提出预防措施:机务、机组等相关人员应做好航前、航后检查;对零部件缺失/损坏/磨损、系统失效、鸟击等高发事件类型进行重点关注,提高机组人员对此类突发事件的应对能力等。

Use of Research Evidence in Policy and Decision-Making: Views, Attitudes and Practices of Health Policy Makers in Lusaka Province of Zambia

Background: The integration of relevant high-quality research evidence into the health decision and policy formulation process is a key strategy for improving health systems especially in developing countries such as Zambia. However, the lack of capacity to understand and value research evidence by policy and decision makers makes it difficult for them to find and use research evidence in a timely manner even when motivated to do so. This study aimed to establish the views, attitudes and practices of policy makers on the use of research evidence in policy and decision-making process in Zambia. Methodology: This descriptive cross-sectional study was conducted in Lusaka, Zambia among selected public health decision and policy making institutions. A purposive sample of 21 consenting policy makers who were working in different positions in the Ministry of Health Headquarters, Provincial and District Health Offices, Health Professions Regulatory Bodies, United Nations Agencies, International Non-Governmental Organizations and University Deans from the University of Zambia participated in the study. A self-administered questionnaire was used to collect data. The IBM? SPSS? Statistics for Windows Version 20.0 was used for data analysis. Results: The concept of Evidence Informed Health Policy was not well understood such that only less than half (47.5%) of the participants reported having heard specifically about Evidence Informed Health Policy meanwhile almost two thirds (61.9%) reported that they used research evidence in decision making and policy formulation. Similar discrepancy was expressed in the understanding of and use of rapid response mechanisms such that although (47.6%) of the participants reported having heard about it, (57%) had never used rapid response mechanisms for deci-sion-making. With regard to the sources of information, about half (52.3) of the participants reported scholarly articles as their main source of evidence. Con-clusion and Recommendations: There is need for more sensitization and ca-pacity building among the decision and policy makers on the importance of using research evidence in decision and policy making process as incorporation of relevant high-quality research evidence into the health policy making pro-cess is a key strategy for improving health systems.