A Unified Decision-Making Technique for Analysing Treatments inPandemicContext

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before. Medical experts, on the other hand, are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection. Further, this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable. The research analysis is based on vast data gathered from professionals and research journals, making this study a comprehensive reference. To solve this challenging task, the researchers used the HF AHP-TOPSIS Methodology, which is a well-known and highly effective Multi-Criteria Decision Making (MCDM) technique. The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries, based on the recommendations of medical practitioners and professionals. The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives. We also perform robustness analysis to validate the conclusions of our analysis. As a result, we obtained highly corroborative results that can be used as a reference. The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand, implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion. Peepli also has the lowest priority in the estimation.     

Subspace Clustering for Situation Assessment in Aquatic Drones: A Sensitivity Analysis for State-Model Improvement

Abstract

In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches.     

水文水资源学家陈守煜先生学术研究的知识图谱分析

为探索水文水资源学家陈守煜先生学术研究历程及其演变规律,以其335篇文献作为数据基础,采用共现分析等文献计量可视化分析方法,通过文献发表的时间分布、学科和期刊分布、高被引文献分布和研究主题等方面的知识图谱分析,系统分析了陈先生的学术研究历程。结果显示:陈守煜先生学术研究生命周期历时达62年,在其60岁后出现了2个学术创新高峰期;其学术研究特色荟萃于其河渠非恒定流计算、模糊集理论的水文水资源应用、模糊水文水资源学基本理论、模糊聚类-识别-优选统一理论、可变模糊集理论和可变模糊集理论的水文水资源应用6个研究主题,其中主题1为后续的5个模糊水文水资源学创新主题奠定了数值计算的研究方法论,主题2为模糊集现有理论的应用探索,主题3至主题5为面向工程应用的模糊集理论不断深入创新,主题6为可变模糊集理论的应用探索,形成了"紧密结合应用-持续理论创新-创新理论再应用"这种理论与实践互馈的研究特色。他每一阶段的研究目标比较集中、明确,与其研究主题密切对应,显著提高了研究效率;他的学术研究影响广泛,合计被引8554次,缘于他的源头理论创新。陈先生的研究,理论与应用不断互馈,贵在厚积薄发、深入钻研、持续创新,其研究理念和方法论,对水利科学的学术创新研究具有重要的启示作用和深远影响。     

Magnetic induction based cluster optimization in non-conventional WSNs: A cross layer approach

Conventional Radio Frequency (RF) communication technique is unsuitable for communication in non-conventional media (water, soil, rocks, etc.) because of heavy losses incurred due to dynamic channel characteristics. Magnetic Induction (MI) communication overcomes these losses as it is least affected by such varying channel characteristics. In non-conventional media based Wireless Sensor Networks (WSNs) the deployed sensor nodes cannot replace or replenish their batteries. Thus, energy consumption should be minimized and that can be achieved by clustering process. This process involves data sensing, aggregation and routing to the base station. These sub-tasks are performed under Physical (PHY), Medium Access Control (MAC) and Network (NET) layers of OSI Network model. Having lesser or larger number of clusters has different impact on energy consumption in different layers’ perspective. A large number of clusters decreases energy consumption as per PHY layer whereas it results in increased energy consumption as per MAC and NET layers. Thus, a trade-off is required to minimize the overall energy consumption. To this end, we found an optimal number of clusters considering the simultaneous influence of all three layers. The above analysis is performed for three media viz. sea water, fresh water and dry soil.     

基于模糊故障树的数控龙门铣床加工误差分析与试验

针对数控龙门铣床加工中出现的平面度误差问题进行研究与改进,分析工艺系统因素产生的误差,建立加工平面误差的模糊故障树,通过下行法对其进行简化分析,定性分析影响加工平面度误差的最小割集,运用模糊隶属函数定量分析顶事件的发生概率及底事件的发生概率重要度,确定主要因素为工作台形位误差、定位元件误差、热变形等。使用激光测量技术对安装的工作台平面进行矩形布点测量,根据采集数据建立基于最小二乘法的数学模型,运用Matlab软件定性分析平面度的评定,并对超差区域进行改进,最终实现了机床装配与使用要求。     

基于模糊数和AHP的数控机床可靠性分配方法

针对层次分析法(AHP)处理可靠性分配问题时存在的局限性,将直觉梯形模糊数与层次分析法相结合,提出了一种新的数控机床可靠性分配方法。在确定影响机床可靠性分配的重要因素种类及分配原则后,利用层次分析法确定数控机床可靠性分配问题的层次模型和可靠性分配中影响因素的重要性权重。运用直觉梯形模糊数准确表达出模糊信息和专家意见,最终确定子系统在可靠性分配中的分配比例。以一数控机床为例,通过和层次分析法进行对比,证明了该方法的有效性。     

云制造平台资源需求的高效匹配策略研究

为快速、精确地从云制造平台资源池中搜索到满足用户需求的资源,实现资源与需求的高效匹配,提出了一种资源需求高效匹配策略。首先,建立了资源与需求的形式化描述模型,在此基础上,采用改进的K-means聚类算法按基本信息进行聚类,对云服务池中的资源进行预处理,形成多个资源簇;其次,计算用户需求与各资源簇聚类中心基本信息的相似度,确立备选资源簇;最后,再分别从资源的状态信息、功能信息和服务信息3个方面对备选资源簇中的备选资源进行筛选匹配。实例分析和研究结果表明:与已有的匹配方法相比,该方法在保持较高匹配精度的同时具有更高的匹配效率。     

模糊时间窗约束下的装配线物料配送方案优化

为解决装配线物料无法及时准确配送的问题,提出了一种动态的联合配送策略,在此基础上以最小化单个产品配送成本、最大化工位满意度为目标,建立模糊时间窗约束下的物料配送数学模型。利用NSGA-Ⅱ算法求解出最佳的工位组划分、小车出发时间和配送路径,并对配送小车容量及配送时间窗进行优化。利用Plant Simulation软件搭建某生产线仿真模型进行模拟,验证了配送方案的可行性。与一般配送策略进行对比,结果表明,在保证配送工位满意度的前提下,优化时间窗能进一步降低单个产品的配送成本。     

Development of new energy management strategy for a household fuel cell/battery hybrid system

This work aims to construct an efficient and robust fuel cell/battery hybrid operating system for a household application. The ability to dispatch the power demands, sustain the state of charge (SOC) of battery, optimize the power consumption, and more importantly, ensure the durability as well as extend the lifetime of a fuel cell system is the basic requirements of the hybrid operating system. New power management strategy based on fuzzy logical combined state machine control is developed, and its effectiveness is compared with various strategies such as dynamic programming (DP), state machine control, and fuzzy logical control with simulation. Experimental results are also presented, except for DP because of difficulties in achieving real‐time implementation and much faster response to load variation. The given current from the energy management system (EMS) as a reference of the fuel cell output current is determined by filtering out various harmful signals. The new power management strategy is applied to a 1‐kW stationary fuel cell/battery hybrid system. Results show that the fuel cell hybrid system can run much smoothly with prolonged lifetime.     

长江经济带工业集聚对水环境污染的影响——基于空间计量与门槛效应的检验

经济发展与生态环境动态良性循环已经成为社会经济发展的重要议题。为实现长江经济带高质量发展，以2007—2018年长江经济带11个省市为样本，采用空间计量和面板门槛模型考察工业集聚与水环境污染之间的关系。研究表明：长江经济带各省市水环境污染具有显著的空间自相关性；长江经济带工业集聚水平和水环境污染表现出先降低后增强的“U”形变化特征，说明工业集聚水平对水环境污染的影响存在一个阈值；长江经济带工业集聚产生的规模效应和结构效应对水环境污染存在显著的双门槛效应，在不同的工业集聚水平下，规模效应和结构效应表现出不同的外部性特征；工业集聚产生的技术溢出效应对水环境污染的影响同样具有显著的双门槛作用；随着各地区工业集聚水平的提高，技术溢出效应对水环境污染的减排作用表现出先强后弱的趋势。