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随着能源系统的去中心化程度不断加深，分解协调式算法开始广泛应用于求解能源领域的优化问题。但是，分解协调式算法需要子优化问题向协调层算法上传协调变量，存在隐私泄露的风险，并且协调层自身还存在着信任问题。为了解决这些问题，引入Paillier加密和区块链共识机制，对分解协调式算法进行改进。在分解协调式算法的每次迭代中，各个发电商的出力通过Paillier加密后以密文的形式广播给其他发电商，某个发电商作为领导节点负责根据密文计算出所有发电商的总出力，并将所有密文和总出力打包生成区块后广播给所有发电商。所有发电商都会对区块内容的正确性进行检查，检查通过后将区块加入到账本中，完成分解协调式算法的本次迭代。算例结果证明了该算法中Paillier加密能够保护各个发电商的隐私，共识机制能够防止领导节点恶意篡改区块内容，在发电商间构建信任。

具有极强随机性和不确定性的分布式能源、可控负荷、电动汽车等的发展，既给电力系统的灵活性问题带来挑战，又可以作为灵活性资源（flexible resources,FRs）给电网带来机遇。虚拟电厂（virtual power plant,VPP）作为FRs集成聚合的主要形式，实现对海量灵活性闲散资源终端的聚能、储能、供能与用能。该研究聚焦于VPP内部的资源管理，首先从能量、空间、时间三个尺度指出VPP实施资源集群及协同调控过程中面临的技术挑战；然后阐述云管边端架构下VPP的层级结构与基本功能，并对资源建模、资源集群、协同调控涉及的关键技术及相关通信方法进行综述；最后对未来VPP需要探索的研究方向进行了展望。

随着分布式能源的快速发展，传统的中心化能源交易系统遇到了建造和维护中心化数据库的成本较高、数据被篡改后会酿成严重后果和难以保障用户隐私这三大问题。针对这些问题，设计了一种基于联盟链的去中心化能源交易系统。首先，分析了该系统的组成部分，利用联盟链的准入机制对用户进行监管。介绍了该系统的交易流程，包括网络节点的配置，使用数字证书注册管理员和用户，借鉴链上链下协同架构实现分布式交易撮合等。接着，介绍了该系统使用的分布式交易撮合算法。然后，利用Hyperledger Fabric平台研发了该系统，介绍了相关的软件架构。最后，分析了使用联盟链的优点，从系统总成本、园区内分布式光伏发电自销占比两个方面将设计的系统与中心化的点对网交易系统进行比较，验证了该系统用于分布式能源交易的可行性。

以某大跨度非规则单层网壳为研究背景,应用ANSYS通用有限元分析软件对网壳结构进行了建模并重点模拟了风荷载、雪荷载、温度效应等多种非对称荷载作用,进行了静力分析、特征值屈曲分析以及非线性稳定分析。得到了网壳结构在15种典型荷载组合下的内力、位移结果,分析了非对称荷载对网壳结构静力性能的影响;考虑了几何非线性、材料非线性、初始缺陷,得到了网壳结构在非对称荷载作用下的稳定系数,分析了非对称荷载对网壳结构稳定性能的影响。强调指出非对称荷载作用在大跨度单层网壳结构设计中的重要性,为大跨度非规则单层网壳设计提供参考。

With the improvement of people' s living standards people have higher requirements for the comfort of household apparel. Breathability is one of the key factors affecting the comfort of household apparel and is the most concerned by household apparel consumers. At present research on the comfort of household apparel is still in a blank period both domestically and internationally. There is a lack of research on the breathability of various household apparel fabrics with different fabric compositions and textures and there is relatively little research on predicting the comfort of household apparel. Based on this this article selects 58 common household apparel fabrics with different fabric compositions and textures on the market and constructs a genetic algorithm improved BP neural network model to predict the breathability performance of household apparel. Firstly to study the relationship between various influencing factors and air permeability of household apparel fabrics the grey relational analysis GRA method was used to analyze the degree of influence of each influencing factor on the air permeability of household apparel fabrics. The factors with higher correlation were selected as input parameters for the model in this study namely density yarn diameter thickness and weight. Secondly due to the shortcomings of BP neural network such as proneness to local minima slow learning rate and long training time this study used genetic algorithm GA to optimize the structural parameters of BP neural network and constructed a genetic algorithm optimized BP GRA-GA-BP neural network prediction model based on grey correlation analysis. Genetic algorithm can optimize the structural parameters of the model find the best parameter combination and solve complex and high-dimensional problems without being affected by local optimal solutions. 58 household apparel fabrics with different fabric compositions and textures were selected of which 42 were model training samples and 16 were test samples to validate the established model. The parameters of each factor including fabric density yarn diameter thickness weight and air permeability were tested as input parameters for the GRA-GA-BP neural network. The results show that the measured and predicted values of air permeability had a small error with a relative error of between 0. 80% and 28. 53% and an average relative error of 8. 39% a comparison chart between the measured and predicted values of air permeability was drawn and it was found that the two curves are basically consistent indicating high prediction accuracy of the model. Finally OriginPro software was used to analyze the correlation between the measured and predicted values of air permeability and the goodness of fit R² was 0. 976 very close to 1 indicating that the model' s prediction effect is good. The prediction model has a small prediction error good prediction effect high prediction accuracy good fitting effect between the measured and predicted values of air permeability and a strong correlation between the measured and predicted values. This article enriches the research on predicting the comfort of household apparel. The model can accurately predict the breathability of household apparel fabrics to a certain extent saving manpower and costs required for experiments. It has important reference significance for household apparel designers to design based on household apparel comfort performance. At the same time it provides a reference route for predicting the comfort of household apparel. Researchers can start from the perspective of household apparel comfort combine subjective and objective experiments and construct corresponding household apparel comfort evaluation and prediction models. [ABSTRACT FROM AUTHOR]

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Objective This study aims to examine the potential mechanism of Shenfu Yixin Granules on heart failure (HF) based on network pharmacology, molecular docking, and experimental verification. Methods (1) The active components of herbs in Shenfu Yixin Granules were screened and retrieved through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) PubChem database and Swiss Target Prediction platform were used to predict targets. GeneCards and OMIM databases were used to screen HF-related targets. The intersection of active ingredient targets of Shenfu Yixin Granules and HF-related targets was performed by using Venny 2.1.0 platform to obtain common targets, which were the potential targets for anti-HF effect of Shenfu Yixin Granules. The potential targets were imported into the STRING database to construct a protein-protein interaction (PPI) network and screen the core targets of Shenfu Yixin Granules for the treatment of HF. GO functional and KEGG pathway enrichment analysis of potential targets were carried out by using DAVID database. AutoDock Vina software was used for molecular docking validation of key active ingredients and core targets. (2) SD rats were randomly allocated into sham operation group, model group, Shenfu Yixin Granules (5.28 g.kg-1) group, and positive control group (sacubitril-valsartan, 20.8 mg·kg-1), with eight rats in each group. A rat model of HF after myocardial infarction was established by ligating the left anterior descending coronary artery. The rats were subsequently administered orally with the corresponding drugs once daily for a period of four weeks. Cardiac function including left ventricular ejection fraction (LVEF) and left ventricular fraction shortening (LVFS) in rats was assessed by echocardiography. Additionally, the histopathological alterations in rat heart tissue were examined using hematoxylin-eosin (HE) staining and Masson staining. The serum levels of brain natriuretic peptide (BNP), artial natriuretic peptide (ANP), and aldosterone (ALD) were determined by enzyme-linked immunosorbent assay (ELISA). Furthermore, real-time quantitative PCR and Western Blot were employed to detect mRNA and protein expressions of CAVI、F2 and MAPKI in heart tissue. (1) A total of 210 active ingredients and 1 196 targets of Shenfu Yixin Granules, as well as 801 HF-related targets were obtained. Venny 2.1.0 platform was used to acquire 97 potential targets (common targets) of Shenfu Yixin Granules for the treatment of HF. Key active ingredients, such as quercetin, luteolin, arachidonic acid, kaempferol, and tanshinaldehyde were screened by "drugs-active ingredients-disease-targets" network analysis. The core targets including MAPK1、F2、CAV1、 EDNI and GJAI were identified through PPI network analysis. The potential targets are mainly concentrated in multiple biological processes, namely, the positive regulation of gene expression, cardiac development, and the positive regulation of MAPK cascade, and involve multi key pathways including MAPK signaling pathway, HIF-1 signaling pathway and PI3K/Akt signaling pathway etc. Good binding activities were observed between MΑΡΚΙ, CAVI, EDNI, F2 and quercetin, luteolin, kaempferol, tanshinaldehyde. as well as MAPKI, F2 and arachidonic acid. (2) Compared with sham operation group, LVEF and LVFS of rats significantly reduced (P< 0.01), heart mass index obviously increased (P < 0.05) in the model group. Myocardial tissue appears obvious pathological damage, and the degree of interstitial fibrosis was serious. The collagen volume fraction of the heart significantly increased (P < 0.01) The levels of serum BNP, ANP and ALD significantly increased (P < 0.01) The mRNA and protein expressions of CAVI、F2 and MAPK1 in heart tissue significantly increased ( P < 0.05 P< 0.01). Compared with the model group, LVEF and LVFS of rats obviously increased (P < 0.01), but the decrease in heart mass index was not significant (P > 0.05) in Shenfu Yixin Granules group and positive control group. The pathological damage in myocardial tissues was significantly improved, the degree of interstitial fibrosis was significantly reduced. The collagen volume fraction of the heart significantly decreased (P < 0.01) . The levels of serum BNP, ANP and ALD significantly decreased (P < 0.01) . The mRNA and protein expressions of CAVI、F2 and MAPKI in heart tissue significantly decreased ( P < 0.05, P < 0.01 ) . Conclusion Shenfu Yixin Granules may improve heart function and myocardial fibrosis in heart failure rats through the interaction between the active ingredients (quercetin, luteolin, arachidonic acid, kaempferol, and tanshinaldehyde) and targets (MAPK1, F2, CAV-1, and EDN1), so as to regulate MAPK signaling pathway and PI3K/Akt signaling pathway. [ABSTRACT FROM AUTHOR]

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This paper analyzes the issues in the digital information construction of experimental teaching demonstration center in colleges and universities, highlighting the significance of education digitalization in the reform and teaching of chemistry experiments. Based on the characteristics of the chemical experimental center in Heilongjiang university, the paper summarizes the digital construction and exploration of the center in three levels: digital information platform, digital teaching resources and digital teaching mode. The center has developed digital teaching resources including chemical experiment safety with clear guidance and three-dimensional structure, virtual simulation with advanced system and comprehensive innovation, and basic chemical experiment with rich content and various forms. The blended teaching modes combining online merge-offline hybrid chemistry experiment safety, theory class, offline experiments, virtual simulations, and MOOCs have been established. The results achieved provide valuable insights for the digital construction of similar university laboratories. [ABSTRACT FROM AUTHOR]

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bjectives: Pose estimation of light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) relies on scan matching algorithm with high accuracy and reliability. Methods: Based on the iterative closest point (ICP) algorithm in LiDAR odometry and mapping (LOAM), we propose a re⁃ silient real-time LiDAR SLAM algorithm where the normal distributions transform (NDT) is flexibly se⁃ lected according to a so-called environmental feature value (EFV) for identifying unstructured scenes. Re⁃ sults: Experimental results show that the EFV can effectively distinguish unstructured scenes, and the de⁃ bugging method of EFV threshold is given. The analysis of localization and mapping experiments show that compared with the classic LiDAR SLAM algorithms such as LOAM, the proposed algorithm has a great improvement in accuracy and reliability, of which the outdoor accuracy can be obtained from meter level to decimeter level. Moreover, the method can build a map and obtain a global consistent map when facing handheld data. Conclusions: Therefore, the proposed method has good environmental adaptability, thus enriching and developing the SLAM method for complex environments. [ABSTRACT FROM AUTHOR]

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3D datum transformation is widely used in geodesy, photogrammetry, point cloud registration and many other fields, and there have been many studies of 3D datum transformation problems for large angles and arbitrary scales. When the observations contain gross errors, the estimated transformation parameters are adversely affected and even severely distorted. In order to deal with large-angle 3D datum transformation problems that contain gross errors, this paper first abstracts the large-angle 3D datum transformation problem as constrained least squares (CLS) problem, and derives the least-squares solutions for the parameters under orthogonal constraints. Then, a distinctive sensitivity analysis approach is introduced into this CLS problem. The local sensitivity of the weighted sum of squared residuals to the perturbations of observations in the CLS problem is discussed, and then, the local test statistics are constructed based on these sensitivity indicators, deducing a data snooping algorithm for CLS problem. Finally, simulations and experiments with real data are carried out to verify the effectiveness of the algorithm. The computational results of the simulated and real experiment show that the proposed data-snooping algorithm using the sensitivity-based test statistics can effectually decrease the negative impact of the outliers and derive reliable parameters, which effectively solves the problem of processing gross errors in large-angle 3D datum transformation. [ABSTRACT FROM AUTHOR]

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