Application of MSD prevention practices by nursing staff working in healthcare settings

ObjectiveThe objective of this article is to examine the application of MSD prevention practices among nursing staff and to identify organizational factors that may or may not support their application.MethodsWe measured the application of prevention practices and its determinants by means of a questionnaire filled out by 399 nurses and nursing staff in Canada. A qualitative component was conducted with two focus groups in order to validate and enrich the interpretation of the survey results.ResultsResults show that most respondents “often” (4) apply MSD prevention practices in their daily professional tasks. Significant differences were observed based on position, mission of the institution, degree, and training.ConclusionsThe characteristics of the nursing staff (position, training, etc.) and the specificities of the setting must be taken into consideration in MSD prevention interventions for nursing staff.     

基于改进人工蜂群算法的多目标绿色柔性作业车间调度研究

针对多目标绿色柔性作业车间调度问题（MGFJSP）的特点，提出从碳排放量、噪声和废弃物这3个指标来综合评定环境污染程度，建立了以最小化最大完成时间和环境污染程度为优化目标的MGFJSP模型，并提出了一种改进的人工蜂群算法来求解该模型。算法的具体改进包括：设计了一种三维向量的编码和对应解码方案，在跟随蜂搜索阶段引入一种有效的动态邻域搜索操作来提高算法的局部搜索能力，在侦查蜂阶段提出产生新食物源的策略用于增加种群的多样性。最后进行了实验研究与算法对比，以验证所建模型和所提算法的有效性。     

Uncertain location–allocation decisions for a bi-objective two-stage supply chain network design problem with environmental impacts

In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chance-constrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.     

长江经济带环境污染治理投资对生态效率的影响研究

环境污染治理投资是生态环境保护的资金来源，其投资规模和投资形式对生态效率产生着重大影响。本文构建生态效率测度指标和非期望的超效率SBM模型，测定了长江经济带9省2市的生态效率水平，再利用Tobit模型量化研究环境污染治理投资的总量及各投资形式对生态效率的影响程度，得出长江经济带环境污染治理投资对生态效率具有正向影响但影响程度一般，3种环境污染治理投资类型中的当年完成环保验收项目环保投资对生态效率影响程度最大且显著。     

澜湄环境利益共同体模式的评价与选择研究

推进澜湄合作是构建周边命运共同体的具体实践，以“同饮一江水，命运紧相连”为主题的澜湄合作机制也为构建澜湄国家命运共同体提供了推进方向。环境问题是澜湄流域最为核心的问题之一，面向澜湄合作中的环境利益问题，文章结合共同体的理念，基于霍尔三维结构理论，总结出合作伙伴环境利益共同体模式、合作联盟环境利益共同体模式与一体化环境利益共同体模式的三种环境利益共同体模式，通过建立环境利益共同体模式的评价指标体系，利用网络层次分析法对澜湄环境利益共同体模式进行评价，选择适合澜沧江-湄公河的环境利益共同体模式。结果表明，在当前的澜湄现状下，以“澜湄六国通过开发流域资源发展本国经济而同时受到其他主体监督以保证不破坏澜沧江-湄公河的生态环境”为主要内容的合作联盟环境利益共同体模式更适合澜湄流域各国，这一模式可以有效解决流域各国经济与环境“两难重”的困境。     

Prediction of the water content of biodiesel using ANN-MLP: An environmental application

Fuel quality, especially biodiesel, is highly dependent on its water content, and the major sources of water in the fuel relate to the transportation, production, and storage processes. In this present contribution, the multilayer perceptron artificial neural network (MLP-ANN) was applied to predict the water content of biodiesel and diesel blend in terms of temperature and composition. The proposed algorithm was trained and tested by utilizing 400 experimental data points which were extracted from the literature. Based on the results, the MLP-ANN model has great ability to estimate the water content of biodiesel and diesel blend. The R-squared (R²), root mean square error, average absolute relative deviation, and a?bsolute deviation parameters for the total data set are obtained, respectively, as 0.99784, 123919.1172, 3.3632, and 1.17%, which indicate the effective performance suggested by ANN. As the computational study is cheaper and easier than the experimental study, the developed software could be considered as an alternative for laboratory study, and the environmental effect of biodiesel and produced undesired product after biodiesel combustion which is directly related to the water content of biodiesel is estimable with the information released in this study.     

石化项目地下水保护措施有效性分析

石化产品在储运过程中若发生污染事故,污染物会通过包气带渗入到地下水中,对地下水质量产生影响。通过预测,制定科学的地下水防治措施,是防止项目对地下水环境造成影响的有效手段。以广东某石化建设项目为例,在查明水文地质条件的基础上,预测和评价项目运营期对地下水环境可能造成的影响,并针对这种影响提出相应的防渗技术措施且预测了防渗措施的有效性。预测结果表明:采取防渗措施后,污染源强对地下水的影响范围明显缩小,说明防渗措施能有效的防止污染物进入地下水,保护地下水资源。     

载荷影响下的机电阻抗协整结构损伤识别方法

高频机电阻抗(electromechanical impedance, 简称EMI)方法利用粘贴在结构表面的压电传感器(piezoelectric transducer, 简称PZT)进行主动激励，通过连续监测和分析PZT机电导纳信号的变化评估结构的健康状态；然而EMI方法容易受到环境工况变化的影响，导致结构损伤的误报。针对此问题，采用时间序列协整方法处理及消除结构工作载荷对阻抗谱特征信号的影响。该方法是基于结构动荷载作用下PZT阻抗谱导纳信号的非平稳特征，将动荷载影响下的阻抗谱非平稳时间序列经线性组合变换成平稳时间序列，根据得到的协整余量序列有效判断结构的健康状态。为验证该方法的有效性，开展了动应力影响下铝梁结构的螺栓松动损伤识别实验。结果表明，协整消除了动态应力对EMI方法的影响，当铝梁内存在持续变化的应力时，仍可以准确识别螺栓松动。机电阻抗协整方法能够消除结构健康监测中荷载作用的影响，及时准确地进行结构损伤识别。     

矿用无人卡车国内外研究现状及关键技术

分析了国内外露天矿山用卡车无人驾驶技术发展现状,详细介绍了露天矿山卡车无人驾驶关键技术;通过分析对比前装线控和后装线控两种技术路线的优缺点以及超声波雷达、毫米波雷达、激光雷达、4D光场智能感知系统、红外传感器、视觉传感器等环境感知技术的优缺点;提出多传感器高度融合将是露天矿山卡车实现全天候环境感知的发展方向。通过分析对比惯性导航系统、GNSS差分定位技术、车联网定位技术、电子地图定位技术、视觉传感器等定位导航技术的差异,指出多定位技术融合是露天矿山无人驾驶卡车定位方式的必然选择。结合露天煤矿的生产实际,提出将现有的无人驾驶各种技术算法与露天矿山标准作业流程有机融合是实现卡车无人驾驶的必由之路。最后指出无人驾驶卡车应用5G技术是露天矿山安全生产的关键和核心,是无人驾驶的必备技术。     

Action-research-based optimisation model for health care behaviour change in rural India

In this research, we utilise a multi-method braided approach for a real-world health care intervention in rural India. We first use action research to conceptually underpin the role of self-help groups, disease profiles and appropriate interventions in health care behaviour change and improvement. Action research is then coupled with a non-linear integer-programming-based simulation to determine how health care resources need to be distributed over time, disease type and groups. The action research provides specific research questions and distributional forms needed for the optimisation analysis, while the non-linear integer-programming simulation provides specific recommendations on where and when to distribute resources. External validity of the study is ensured by the adoption of recommendations in the field as part of the action research. Findings from this research have important policy implications. For example, it identifies the value of continuous embedded interventions as opposed to periodic ones and the importance of flexibility in terms of which community groups and disease types to serve over time. This research also provides an important bridge between the operations research, health care in developing nations, organisational theory and humanitarian operations bodies of knowledge.