Air co-gasification process of LDPE/HDPE waste based on thermodynamic modeling: Hybrid multi-criteria decision-making techniques with sensitivity analysis

Gasification process is an efficient thermochemical disposal technique and is growing fast in the plastic waste field, nowadays. Co-gasification of low-density and high-density polyethylene waste with different composition ratios was developed in this study and subjected to a hybrid multi-criteria decision-making (MCDM) techniques after validation by comparing with experimental data. A comprehensive parametric study was conducted by considering interaction effects of gasification parameters on the outputs. The MCDM problem involved eleven alternatives and five energy, environmental and economic criteria of gasification performance indicators including hydrogen production, energy efficiency, carbon dioxide emission, heating value of syngas and purchasing cost. The criteria weighting was performed using analytical hierarchy process, Shannon entropy technique and their composite weighting method. TOPSIS and VIKOR techniques were coupled by criteria weighting methods for alternative ranking. L0H100 alternative, i.e. composition of 0 wt% of low-density polyethylene waste and 100 wt% of high-density polyethylene waste, was the best alternative in almost all cases. This alternative had the minimum CO₂ emission of 4.98 g/mol, the minimum purchasing cost of 0.96 $/kg and led to the maximum heating value of 126.8 kJ/mol. The findings revealed that VIKOR technique was sensitive to the criteria weights; however, TOPSIS method was independent from the changing the criteria weights.     

考虑柔性负荷无功调节能力的配网日前两阶段无功随机优化调度

针对高比例分布式光伏接入配网所引起的电压控制问题，利用柔性负荷的无功调节能力，并考虑光伏和负荷的不确定性，提出了配网日前两阶段无功随机优化调度方法。首先，在第1阶段根据光伏和负荷的预测值，优化反映电容器组动作和柔性负荷无功调度的离散决策变量。其次，在第2阶段考虑光伏和负荷的不确定性，优化光伏的无功出力和柔性负荷的无功调度量。在优化模型中，设置了电压偏差最小化和柔性负荷的无功调度次数最小化两种目标函数，采用ε约束法对多目标优化问题进行求解，并利用熵权优劣解距离(technique for order preference by similarity to ideal solution, TOPSIS)法选择最优折衷解。在修改的IEEE33节点系统进行仿真，结果表明所提方法能有效缓解由高比例分布式光伏并网所引起的电压越限问题。     

需求导向下的园林专业课程体系构建研究——以安徽农业大学为例

在教育部建设一流本科专业建设点要求的背景下，按照农林院校园林一流专业建设点的标准，课程体系的更新必须要着眼更高的要求。溯源国内农林院校园林专业的发展特点，以建设一流课程的要求为依据，梳理出以“林学类专业”为特色且融合“建筑学类”专业的特征要求。在此基础上，遵从安徽农业大学园林专业教师团队、教学资源以及培养方案的专业定位和学情，构建以需求为导向的专业课程体系。运用TOPSIS熵权法对专业课程体系的问卷调查数据进行合理性分析，得到总体课程体系、主干课程体系和创新实践课程体系的最优理想解，进一步确立以“园林工程”和“园林花卉学”为“联动双核心”的园林专业课程体系。优化课程体系，拟整体达到“新农科”特色结合多学科交叉的多元化教学要求，培养新时代引领行业实践和研究创新的园林人才，满足为建设高品质人居环境提供园林建设研究人才的需求导向，实现安徽农业大学园林专业的培养目标，培养新时代高品质的园林高级人才。同时也为其他院校构建农林院校园林专业课程体系提供了参考。     

基于改进TOPSIS方法的温榆河水环境质量综合评价研究

区域水环境质量综合评价已成为水资源保护的一项重要基础性工作,简要介绍TOPSIS方法的原理及其应用范围,针对传统TOPSIS法由主观判断确定指标权重方法的不确定性及应用相对接近度计算方法不能完全反映出各方案的优劣性的问题,提出了熵权法和改进的相对接近度算法,使TOPSIS法用于环境质量综合评价以及多目标决策分析更趋完善,评价结果更合理客观。并用基于改进的TOPSIS法对温榆河流域水环境质量进行了综合评价。     

基于Grover融合理论的无线传感网络路由算法研究

如何在各种网络资源受限制的情况，实现高质量的信息传输是无线传感网络研究领域的关键问题之一。首先，分析了网络传输中所需要考虑的受限制因素，并提出各种因素的计算办法；然后，针对确保服务质量的多目标规划算法存在计算量过大的缺陷，借鉴量子搜索算法中的Grover理论用以降低信息传输过程的搜索计算量；最后，通过Grover理论得到的各种资源路由选择方案，本文采用了计算机控制中的D-S信息融合理论，将多目标规划转化为单目标规划。为了验证本文所提出的Grover融合路由算法，文章建立MATLAB仿真环境，对比传统的DSR路由协议与多目标规划TOPSIS算法，可见本文所提出的算法在降低网络搜索计算量、延长网络生存时间、降低网络时延方面具有较大的改善。     

基于改进TOPSIS法的水资源配置方案评价

以综合效益最优为目标层,水资源利用、经济效益、社会效益、生态效益为准则层,建立临海市水资源配置方案评价指标体系,采用改进TOPSIS法对"五水共治"理念下的4个区域水资源配置方案展开评价。结果表明,高节水、加强水资源开发、保障生态需水的方案4为最优方案。研究旨在为临海市经济、社会、资源、环境的全面发展提供理论支撑。     

Relevancy contemplation in medical data analytics and ranking of feature selection algorithms

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.