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To evaluate the efficacy of Wen-Luo-Tong Granules (WLT) local administration in the treatment of patients with peripheral neuropathy (PN) induced by chemotherapy or target therapy.This study is a randomized, double-blinded, and placebo-controlled trial. Seventy-eight patients with PN induced by chemotherapy or target therapy were enrolled from China-Japan Friendship Hospital between July 2019 and January 2020. They were randomly assigned to WLT (39 cases) and control groups (39 cases) using a block randomization method. The WLT group received WLT (hand and foot bath) plus oral Mecobalamin for 1 week, while the control group received placebo plus oral Mecobalamin. The primary endpoint was PN grade evaluated by the National Cancer Institute-Common Toxicity Criteria for Adverse Events (NCI-CTCAE). The secondary endpoints included quantitative touch-detection threshold, neuropathy symptoms, Quality of Life Questionnaire-Chemotherapy Induced Peripheral Neuropathy (QLQ-CIPN20), and Quality of Life Questionnaire-Core30 (QLQ-C30).After treatment, the PN grade in the WLT group was significantly lower than that in the control group (1.00 ± 0.29 vs. 1.75 ± 0.68, P0.01). The total effective rate in the WLT group was significantly higher than that in the control group (82.05% vs. 51.28%, P0.01). Compared with the control group, the touch-detection thresholds at fingertips, neuropathy symptom score, QLQ-CIPN 20 (sensory scale, motor scale, autonomic scale, and sum score), and QLQ-C30 (physical functioning, role functioning, emotional functioning, and global health) in the WLT group significantly improved after treatment (P0.01 or P0.05).WLT local administration was significantly effective in the treatment of patients with PN induced by chemotherapy or target therapy. (Trial registration No. ChiCTR1900023862).

Under the background of huge amounts of data in large-scale power grid, the active power optimization calculation is easy to fall into local optimal solution, and meanwhile the calculation demands a higher processing speed. Aiming at these questions, the farmer fishing algorithm which is applied to solve the problem of optimal distribution of active load for coal-fired power units is used to improve the cloud adaptive genetic algorithm (CAGA) for speeding up the convergence phase of CAGA. The concept of cloud computing algorithm is introduced, and parallel design has been done through MapReduce graphs. This method speeds up the calculation and improves the effectiveness of the active load optimization allocation calculation.

The operation of power system generates a huge number of data. Manual screening of bad data is almost impossible to achieve. Bad data detection of the power system is the essential prerequisite for power system state estimation. So combined with the characteristics of bad data of the power system, this paper puts forwards an improved K-Means algorithm based on PSO. The traditional K-Means algorithm has disadvantage that it has low efficiency to determine the cluster’s center, but PSO algorithm has the ability to determine the cluster’s center quickly and accurately, this paper combines them together and take advantages of each of the two. The improved K-Means algorithm is based on PSO and verified the effectiveness of the algorithm. The results satisfy the requirements of screening bad data and reduce the false alarms and the probability of undetected and improve the recognition efficiency.

A combination of Task and Role-based Access Control with multi-constraint is put forward in this paper. It is designed to solve problem of access control management about collaborators in workflow system, whose difficulties lie in complex authorization and low users efficiency. It combines the tasks and roles, classifies tasks, simplifies permissions management, defines the mutually exclusive roles and binding tasks and formulates dynamic users allocation policies by establishing a users execution history table to improving the efficiency. Finally, a specific dynamic access control design is given for electric power enterprise equipment maintenance management workflow, the given example shows that the model and algorithm satisfies the principle of least permission and separation of duties and ensures the workflow system to execute tasks safely and efficiently.

To improve the accuracy of load forecasting and cope with the challenge of single computer’s insufficient computing resource, a short-term distributed load forecasting model based on LSSVM optimized by IPPSO is proposed. Uncertain parameters are optimized by improved parallel particle swarm algorithm which runs on the Spark on Yarn memory computing platform. The real load data provided by EUNITE is used, and experiments and analysis are conducted on an 8-node cloud computing platform. The results show that the accuracy of the algorithm proposed by our paper is better than the traditional functional networks algorithm, the efficiency of the algorithm is better than MR-OSELM-WA, and the algorithm has good ability of parallelization.

A distributed reactive power optimization algorithm is put forward based on cloud computing and improved NSGA-II (fast non-dominated sorting genetic algorithm) in this paper. It is designed to solve problem of multi-objective reactive power optimization with huge amounts of data in power grid, whose difficulties lie in local optimum and slow processing speed. First, NSGA-II's crossover and mutation operator are improved based on Cloud Model, so as to satisfy the adaptive characteristics. In this way, we improved global optimization ability and convergence speed when dealing with large-scale reactive power optimization. Second, we introduced cloud computing, parallelized the proposed algorithm based on MapReduce programming framework. In this way, we achieved distributed improved NSGA-II algorithm, effectively improved the calculation speed of handling massive high-dimensional reactive power optimization. Through theoretical study demonstrated the superiority of the algorithm to solve the Multi-Objective reactive power optimization.

Aiming at solving data integrity protection problems in the cloud , a remote data integrity verification scheme is proposed. Firstly, the data integrity verification is constructed based on homomorphic identification and data fragment structure. Secondly, by introducing random mask, the public verification is realized and by building index-hash table (IHT), the scheme can support dynamic verification. Finally, use the MapReduce for parallel computing, which reduces computation overhead side and storage overhead. The security and performance analyses show that our proposed scheme is secure and reliable.

Considering the confidentiality and integrity of big data in cloud storage, a MapReduce-based AES parallelization scheme is designed by using MapReduce framework of the open source Hadoop in this paper. The scheme takes full advantage of MapReduce and modern cryptography technologies to parallelize AES encryption and decryption process, in the way of data decomposition, which speeds up the efficiency in the implementation of encryption and decryption. Meanwhile, mix plaintext, separate storage and other technical means are taken into account in this scheme to ensure the confidentiality and security of the key and the ciphertext. By analyzing the performance, it is proved that the time consumption of new scheme is significantly reduced comparing with the traditional method.

With development of computer technology, massive information has brought huge challenge on the storage system reliability. A algorithm called HG(Heuristic greedy) algorithm is proposed to optimal calculation path, reduce XOR operation and computational complexity for data recovery, which applies CRS(Cauchy Reed-Solomon) code to cloud storage system HDFS and turns multiply operation of CRS coding to binary matrix multiplication operation.The performance analysis shows that it improves fault tolerance of cloud file system, storage space effectively and timeliness with reduction of additional storage overhead.

In view of the current popular cloud storage access control, some security problems were still existed. To solve the problem which the cloud service provider can’t verify the user write permissions by the CP-ABE based cloud storage access control, in this paper a cloud storage access control method is proposed which is based on the encryption algorithm of the cipher-text (CP-ABE) and the attribute-based signature (ABS). This method not only achieves the cloud storage environment information privacy and integrity, but also supports inheritance authorization and scalability.

The problems of difference of nodes capabilities and the unevenly-distributed bandwidth of the network, widespread exist in the heterogeneous clouding environment. Together with the users randomness of submitting jobs, the problems above lead to server synchronization problems.Under the platform of Hadoop and the situations mentioned above, we come up with a method which is based on the native hadoop speculative algorithm to solve the problems. Through monitoring the load-balance in realtime, dynamically assessing the performance of the node and making the speculative tasks happened in high-performance node which meantime is the nearest node from input split, the algorithm effectively reduces the occupation of the network and accelerates executing speed. The experiment result shows that the method in the execution of which the speculative tasks has a high ratio, significantly improved the efficiency and throughput of the cluster.

The traditional power system shows regional power load density using the dot density way, while it performs some shortages in showing regional density visualization during zooming in webgis process. By improving density point generation algorithm and using heatmap technology, this paper designing and implementing the visualization method of regional electricity load density which is based on heatmap technology in WebGIS (web geographic information system). Also, this paper shows that the image rendering performance and map zoom visual performance are improved when using heatmap method performs regional power load density through experiments.

With the development of smart grid, mass data collected in real time for equipment monitoring requires higher performance of data store. Hence, a storage system based on cloud platform for smart grid equipment monitoring is designed and achieved. Aiming at electric power monitoring data, this paper designs an improved PI revolving door compression algorithm, which improves the performance of data transmission and storage. Related experiments show the effectiveness of the algorithm. The storage system meets the requirements of real-time, mass, high reliability and high performance of data store in smart grid.

In order to grasp the security situation of the network accurately and provide effective information for managers of network.GeesePSOSEN-SVM algorithm is proposed in this paper. It can produce and train multiple independent SVM through Bootstrap method and increase the degree of difference among SVM based on learning theories of negative correlation to construct the fitness function.GeesePSO algorithm is used to calculate the optimal weights of SVM.The algorithm chooses the high weights of SVM to integrate. At last, through the experiment on MATLAB for network security situational prediction,the results show that the absolute prediction error is smaller ,and the right trend rate is higher.

At present, the situations of long period independent operation of each kind of distribution and consumption management system and lack of effective integration, it obstructed the inter-departmental coordination of business operations. According to IEC61968, the paper proposed a smart distribution and consumption integrated management system that integration of existing systems at the distribution and consumption side. The system can solve the problem of information islands. With the scope of the power system data acquisition continues to expand, in the future need mass data storage and analysis capabilities, it is insufficient of the existing storing and information-processing platform of the power system, the cloud storage based on Hadoop can solve the problem. The Experiments show that cloud storage based on Hadoop clusters has characteristics of high throughput rates and high transmission rate, which can meet the mass data storage access requirements of the smart distribution and consumption integrated management system.

With the application of Ethernet communication architecture in electric power telecontrol communication, new requirements of information security have been put forward based on Manufacturing Message Specification (MMS). IEC62351 standard makes two clear security requirements for MMS, which is confidentiality and integrity. Taking into consideration the increasing costs if one algorithm corresponds to one security requirement, this paper designs a MMS-based security message format, applying AES-CCM encryption algorithm to satisfy various safety demands and analyzes algorithms time costs as well as benefits with an example of authenticated encryption mode. Analysis shows that security and interoperability of telecontrol communication can be improved to a large extent using different modes of AES-CCM algorithm for security message transmission under distinct security requirements.

Ciphertext-Policy Attribute-based encryption (CP-ABE) mechanism is an extension of attribute-based encryption which associates the ciphertext and user's private key with the attribute by taking the attribute as a public key. It makes the representation of the access control policy more flexible, thus greatly reduces the network bandwidth and processing overhead of sending node brought by fine-grained access control of data sharing. According to the principle of CP-ABE encryption mechanism for this mechanism, an improved cloud computing-based encryption algorithm was proposed in this paper to overcome the deficiencies of permission changing process under the massive data. Experimental results show that compared with traditional methods, the new mechanism significantly reduces time-consuming.

Integration of large scale renewable energy sources and electric cars into power grid will bring new opportunities and challenges to the operation, control of the power grid and the power market .The microgrid , which integration the distributed generation systems, energy storage element and loads ,has been seen as one effective way to solve the problems. This paper proposed a MEA-based scheduling algorithm, and studied a microgrid that contains 32 nodes and 50 electric cars. Both the MEA algorithm and mixed integer nonlinear programming algorithm (MINLP) can solve the energy resource scheduling problem,but the results of our simulation shows that the total cost of MEA is higher than that of the MINLP,but there is a significant improvement in the efficiency of execution.The results shows that we can use MEA to achieve optimal scheduling,it also verify the effectiveness of the algorithm.