Impact of defects on the decomposition of chemical warfare agent simulants in Zr-based metal organic frameworks

Metal organic frameworks (MOFs) containing zirconium secondary building units (SBUs) in UiO-67 and related MOFs, are highly active for neutralizing both the chemical warfare agents and simulants, such as dimethyl methylphosphonate (DMMP). However, two recent publications gave conflicting reports of DMMP reaction with UiO-67 under ultra high vacuum (UHV) conditions, with one reporting chemisorption and reaction (Wang et al., J Phys Chem C, 2017, 121, 11261–11272) and the other reporting only physisorption and reversible desorption (Ruffley et al., J Phys Chem C, 2019, 123, 19748–19758) from very similar temperature programmed desorption experiments. We show that the discrepancy between these experiments may be explained by different levels of missing linker defects in the UiO-67 samples. We present density functional theory calculations showing that SBU sites having two-adjacent missing linkers exhibit reaction barriers that are about 30 kJ/mol lower than SBU sites having a single missing linker. We also show that topology of the undercoordinated sites plays an important role in the reaction barrier under UHV conditions.     

Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the required task. Feature Selection based on the use of statistical and correlation matrices for multivariate analysis. For Random Search and Grid Search models, extensive comparison findings are produced utilizing retrieval, F1 score, and precision measurements. The models are evaluated using the metrics and kappa statistics that illustrate the three models’ comparability. The study effort focuses on optimizing function selection, tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification. Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.     

基于深度EPG的智能导航平台建设

随着三网融合的大力推进以及双向互动业务的大力开展,运营商使用原有简单的EPG不足以支撑开展更多的增值业务,不能主动为用户推荐节目,不能为运营商带来新的收入机会。作为重要的创新能力支撑系统,介绍了江苏有线智能导航平台的设计与应用,为相关广电运营商提供一定的设计参考。     

基于Word2Vec模型特征扩展的Web搜索结果聚类性能的改进

对于用户泛化和模糊的查询，将Web搜索引擎返回的列表内容聚类处理，便于用户有效查找感兴趣的内容。由于返回的列表由称为片段（snippet）的短文本组成，而传统的单词频率-逆文档频率（TF-IDF）特征选择模型不能适用于稀疏的短文本，使得聚类性能下降。一个有效的方法就是通过一个外部的知识库对短文本进行扩展。受到基于神经网络词表示方法的启发，提出了通过词嵌入技术的Word2Vec模型对短文本扩展，即采用Word2Vec模型的TopN个最相似的单词用于对片段（snippet）的扩展，扩展文档使得TF-IDF模型特征选择得到聚类性能的提高。同时考虑到通用性单词造成的噪声引入，对扩展文档的TF-IDF矩阵进行了词频权重修正。实验在两个公开数据集ODP239和SearchSnippets上完成，将所提方法和纯snippet无扩展的方法、基于Wordnet的特征扩展方法和基于Wikipedia的特征扩展方法进行了对比。实验结果表明，所提方法在聚类性能方面优于对比方法。     

基于k-shell的复杂网络最短路径近似算法

复杂网络最短路径经典算法的处理效率较低，不适用于大规模复杂网络，而现有近似算法通用性有限，且计算准确率不理想，不能满足规模日益扩大的复杂网络中的最短路径计算需求。针对于此，提出基于[k]-shell的复杂网络最短路径近似算法。算法利用节点的[k]-shell值进行网络划分并引导搜索路径，利用超点聚合处理[k]-shell子网来降低路径搜索中节点和连边的规模，通过在路径搜索过程使用双向搜索树方法提高算法的计算效率和准确率。实验结果表明，算法通用性较好，在现实与仿真大规模复杂网络中均具有较高的计算效率和准确率。     

An improved cuckoo search algorithm for the problem of electric distribution network reconfiguration

Power loss reduction has an important role in operating electric distribution network system. There are a lot of methods for reduction power loss such as capacitor placement, distributed generation placement and electric distribution network reconfiguration (EDNR). Among these methods, the EDNR is an efficient technique to reduce power loss due to without taking any costs. However, the EDNR problem is a nonlinear, discrete problem and lots of extreme points. Therefore, it is necessary to have efficient methods for solving the EDNR problem. In this paper, an improved cuckoo search algorithm (ICSA) is proposed for solving the EDNR problem. In which, based on disadvantages of exploration and exploitation process of cuckoo search algorithm (CSA) for solving the EDNR problem, a local search mechanism is added to exploit candidate solutions existing around the current best solution. The calculated results on the simple distribution networks to complex distribution networks show that ICSA has ability for finding the global optimal solution with much smaller iterations and better quality of obtained solution compared with CSA and some other improved versions of CSA. The performance comparisons with other existing methods available in previous studies and the software of Power System Simulator/Advanced Distribution Engineering Productivity Tool (PSS/ADEPT) also lead to the better electric distribution network configuration with smaller total power losses. As a result, ICSA is a potential and reliable method for solving the EDNR problems.     

Multiphase topology optimization with a single variable using the phase-field design method

In this study, a multimaterial topology optimization method using a single variable is proposed by combining the solid isotropic material with penalization method and the reaction-diffusion equation. Unlike ordinary multimaterial optimization, which requires several variables depending on the number of material types, this method intends to represent various materials as one variable. The proposed method combines two special functions in the sensitivity analysis of the objective function to converge the design variable into prespecified density values defined for each of the multimaterials. The composition constraint based on a normal distribution function is also introduced to estimate the distribution of each target density value in a single variable. It enables density exchange between multiple materials by increasing or decreasing the amount of a specific material. The proposed method is applied to structural and electromagnetic problems to verify its effectiveness, and its usefulness is also confirmed from the viewpoint of cost and computation time.     

基于平面不封闭图形切割的路径规划

在平面切割环节中，如何确定更短切割路径以减少加工成本、降低设备损耗和提升切割质量是工业应用和学术研究的重点。目前国内外对平面切割路径的研究主要集中于封闭图形，为此，针对在激光刀模行业中不封闭图形的问题，提出一种基于禁忌搜索的贪婪算法和贪婪准则的局部优化。首先使用构建型的贪婪算法和改进型的禁忌搜索算法相结合的方式对加工过程中的图元路径进行优化排序，随后提出贪婪准则的局部优化系数，削弱贪婪算法的“贪心”思想，解决加工路径的规划和优化问题。实验数据表明，禁忌搜索的贪婪算法和局部优化在对切割路径的规划和空行程优化上有显著效果，空行程减少50%以上，并且其优化性能和图元数量成正比，能有效地解决刀模行业以及其他激光雕刻行业中图形不封闭的切割难题。     

基于FAHP的机械振动WSN均衡拓扑构建方法

针对机械振动无线传感器网络因拓扑不均衡导致传输时延和网络传输能耗增加的问题,提出了一种基于模糊层次分析(fuzzy analytic hierarchy process,简称FAHP)的均衡拓扑构建方法,该方法由构建模糊判断矩阵和计算权重向量组成。首先,传感器节点进行簇内通信获取信标广播信息,将信标节点网络决策因子统一量纲化,利用网络决策因子构建模糊判断矩阵;其次,检验模糊判断矩阵一致性,采用行和归一化处理或拉格朗日最小二乘法计算权重向量;最后,传感器节点通过权重向量计算出各个信标节点综合权值,关联最优信标节点为父节点加入网络,将提出的模糊层次分析拓扑构建方法与基于链路质量单准则构建网络拓扑机制进行对比。实验结果表明,该方法能有效改善传输时延和机械振动无线传感器节点网络寿命。     

Scheduling distributed flowshops with flexible assembly and set-up time to minimise makespan

The scheduling problems under distributed production or flexible assembly settings have achieved increasing attention in recent years. This paper considers scheduling the integration of these two environments and proposes an original distributed flowshop scheduling problem with flexible assembly and set-up time. Distributed production stage is deployed several homogeneous flowshop factories that process the jobs to be assembled into final products in the flexible assembly stage. The objective is to find a schedule, including a production subschedule for jobs and an assembly subschedule for products, to minimise the makespan. Such a scheduling problem involves four successive decisions: assigning jobs to production factories, sequencing jobs at every factory, designating an assembly machine for each product and sequencing products on each assembly machine. The computational model is first established, and then a constructive heuristic (TPHS) and two hybrid metaheuristics (HVNS and HPSO) are proposed. Numerical experiments have been carried out and results validate the algorithmic feasibility and effectiveness. TPHS can obtain reasonable solutions in a shorter time, while metaheuristics can report better solutions using more yet acceptable time. HPSO is statistically comparable yet less robust compared with HVNS for small-scale instances. For the large-scale case, HPSO outperforms HVNS on both effectiveness and robustness.