基于社会技术系统的复工企业疫情防控风险路径研究*

为对复工企业疫情防控中的风险致因因素进行分析，探寻复工企业疫情防控的风险路径，基于社会技术系统理论，建立宏观工效学模型，通过查阅并分析国家、地方政府、企业文件以及相关文献，从人员、技术、组织管理、内部环境及外部环境5个子系统出发，识别出16个复工企业疫情失控致因因素；运用社会网络分析（SNA）构建复工企业疫情防控关系网络，对各因素节点中心度进行计算，分析各节点在网络中的位置及影响程度；采用贝叶斯网络（BN）进行参数学习和推理学习，找出复工企业疫情失控的最大致因链。结果表明:复工企业疫情失控关系网络中，存在政府监督管理不力→防疫物资筹备不足→日常监管不到位，政府监督管理不力→ 防疫宣传、培训缺失→员工防疫知识欠缺，政府监督管理不力→防疫宣传、培训缺失→日常监管不到位3条最长风险路径。研究结果可帮助复工企业针对最长风险路径中的因素进行管理，从而有效地为复工企业防疫提供理论支持。     

基于贝叶斯网络的航空器看错落错跑道事件研究

目前,航空器看错、落错跑道事件在国际上是一个相当突出的安全问题,这个问题在我国近年也突显出来。导致航空器看错落错跑道的原因十分复杂,且此类事件及易引发严重的后果,造成人、财、物等多方面的损失,本文特对此类问题开展了一系列的研究。为了预防航空器看错、落错跑道事故的发生,查找诱发该类事故的因素,针对航空器看错、落错跑道事故的形成特点,笔者运用贝叶斯网络探讨其各影响因素间的关系和相互作用,该网络强大的逻辑推理能力,克服了不完备样本空间带来的不足,揭示了人、机、环境与管理因素相互作用的内在规律。为改善机场安全管理的科学性、可靠性,进一步降低事故率,提供了可靠的技术支持。     

一个公平的多方的不可否认协议

公平的不可否认协议解决了电子商务中信息传输的互相否认问题，使通信各方处于平等的位置。协议对各方都是公平的。该协议是基于组加密方法的，由可靠的第三方产生不可否认证据，数据交互次数少，高效实用。     

学习率有限监督调整BP网络在水质评价中的应用

以地表水环境质量标准基本项目标准限值为依据,随机生成标准样本和检测样本,采用学习率有限监督调整BP网络建立水质评价模型。完成网络训练的模型对检测样本进行检验评价,检验结果表明,该人工神经网络水质评价模型具有较高的精度,同时,可以避免人为主观因素对水质评价的影响,保证了水质鉴定的科学性和公正性。     

Carbon footprint in corporate logistics operations in the food sector

Global warming is negatively affecting the environment of the planet. This situation has led to the development of international standards, such as the International Organisation for Standardization's DIS 14064‐1 and the Greenhouse Gas Protocol Corporate Standard, both of which measure corporate carbon footprints. These standards provide guidelines that can be applied to different organizational sectors. However, these are not sufficient for controlling the reduction of carbon emissions, because although they propose the use of indicators, they do not explicitly define them. In addition, in the case of emissions from wastewater treatment, they only suggest that the emissions associated with this process be considered. In the present study, an eight‐step, unified methodology based on these two international standards is proposed, focusing on direct emissions. Moreover, the step‐by‐step to data collection, calculations, and the required indicators to control the emissions are defined. The first scope considers direct emissions from sources that are owned or controlled by the company. Methane generation measurement from wastewater treatment has been included in the methodology within Scope 1, as it is the second most polluting gas after carbon dioxide, both of which give rise to global warming. The proposed methodology was tested as a case study in one of the most important companies in the food sector in Colombia.     

基于FY-4A卫星数据反演气溶胶光学厚度及分析应用

风云四号A星（FY-4A）是我国新一代静止气象卫星,多通道扫描成像辐射计（AGRI）是风云四号静止气象卫星的主要载荷之一.为探究FY4A数据用于气溶胶光学厚度（AOD）反演的可行性,基于FY-4A/AGRI数据,利用暗像元算法对2019年2月23~26日及10月27~30日京津冀地区AOD进行反演研究,并与AERONET地基观测AOD数据进行了对比分析.结果表明,基于FY-4A数据及构建响应函数库,通过暗像元方法能较好的反演出京津冀地区气溶胶空间分布;AOD主要的高值区体现在京津冀中南部地区并向周边郊区逐渐降低,在AOD值较大时此特征较为明显;将反演值与同期AERONET地基观测数据对比验证,相关系数达到0.869,均方根误差为0.221,表明AOD反演值与观测值吻合较好,FY-4A卫星数据反演AOD具有一定可行性.     

Performance evaluation of process industries resilience: Risk-based with a network approach

Risk management can be defined as coordinated activities to conduct and control an organization with consideration of risk. Recently, risk management strategies have been developed to change the approach to hazards and risks. Resilience as a safety management theory considers the technical and social aspects of systems simultaneously. Resilience in process industries, as a socio-technical system, has four aspects of early detection, error-tolerant design, flexibility, and recoverability. Meanwhile, process industries' resilience has three phases: avoidance, survival, and recovery, determining the transition between normal state, process upset event, and catastrophic event. There may be various technical and social failures such as regulatory and human or organizational items that can lead to upset or catastrophic events. In the avoidance phase, the upset event is predicted, and thus, the system remains in a normal state. For the survival phase, the system state is assumed to be an upset process event, and the system tries to survive through the unhealthy process conditions or remains in the same state, probably with low performance. In the recovery phase, the system is supposed to be catastrophic, and the emergency barriers are prioritized to show the severity of the consequences and response time, leading to a resumption of a normal state. Therefore, a resilience-based network can be designed for process industries to show its inherent dynamic transition in nature. In this study, network data envelopment analysis (DEA), as a mathematical model, is used to evaluate the relative efficiency of the process industries regarding a network transition approach based on the system's internal structure. First, a resilience-based network is designed to consist of three states of normal, upset, and catastrophic events. Then, the efficiency of each industrial department, which is defined as decision-making units (DMUs), is evaluated using network DEA. As a case study, a refinery that is considered a critical process industry is assessed. Using the proposed model shows the efficient and inefficient DMUs in each of three states of normal, upset, and catastrophic events of the process and the projection onto efficient frontiers. Besides calculating the network efficiency, the performance of each state is extracted to precisely differentiate between DMUs. The results of this study, which is one of the fewest cases in the area of performance evaluation of process industries with a network approach, indicated a robust viewpoint for monitoring and assessment of risks.