Design of E-commerce Data Scalable Storage System Based on Mobile Internet Communication Technology

The traditional encrypted storage system is inefficient when it encrypts the data of the Internet of Things, and there are few IOT data nodes that can be encrypted in a short time. In order to solve the above problems, a new Internet of Things data effective information encryption storage system is proposed. The hardware and software of the system are mainly designed. The chip selected for the collector is TTSAD251, which can expand the collection range. The processor is set with multiple cores to reduce the system power consumption. The memory uses SPRTAN-2 chip as the structure chip. The software work consists of three parts: collecting effective information of Internet of Things big data, establishing encrypted documents and storing effective information of big data of Internet of Things. In order to detect the working effect of the system, the experimental comparison with the traditional system shows that the proposed encryption storage system can improve the storage range of big data effective information of the Internet of Things by 20.58%, and the work efficiency by 5.64%. Compared with the traditional system, the designed system also has obvious advantages in the number of big data node secrets. In different files, the average number of big data information node encryption in this system is about 166,700. The experimental data show that the designed system has ideal application performance and provides a reliable basis for related fields.

Research on the Improvement of Big Data Feature Investment Analysis Algorithm for Abnormal Trading in the Financial Securities Market

Traditional investment analysis algorithms usually only analyze the similarity between financial time series and financial data, which leads to inaccurate and inefficient analysis of investment characteristics. In addition, the trading volume of financial securities market is huge, the amount of investment data is also very large, and the detection of abnormal transactions is difficult. The aim of feature extraction is to obtain mathematical features that can be recognized by machine. Different from the traditional methods, this paper studies and improves the big data investment analysis algorithm of abnormal transactions in financial securities market. After processing the captured trading data of financial securities market, the big data feature of abnormal trading is extracted. Combined with the abnormal trading and the financial securities market, the investment strategy is determined. The optimization objective function is set and the genetic algorithm is used to improve the investment analysis algorithm. The simulation experiment verifies the improved investment analysis algorithm, and the average Accuracy of investment analysis is increased by at least 11.24%, the ROI is significantly improved, and the efficiency is higher, which indicates that the proposed algorithm has ideal application performance.

Tailoring Co-Doping of Cobalt and Nitrogen in Fullerene-Based Carbon Composite and Its Effect for Supercapacitive Performance

Fullerene-based carbons have attracted great attention in applications of energy storage and conversion due to their unique properties and tunable architectures. However, fullerene's poor long-range conductivity limits their application performance...

Investigating the Effect of COVID-19 Infection on P300 Based BCI Application Performance

The COVID-19, which has rapidly spread and infected millions of people from all over the world, causes various problems including psychiatric, economic, educational as well as health. Many studies have been reported that COVID-19 can be characterized by vascular damage predominantly involving micro vessels. In this study, we proposed a method to examine whether COVID-19 effects on brain computer interface (BCI) performance or not. We collected P300 based electroencephalogram (EEG) signals from six subjects before and after the COVID-19 infection. For classifying the P300 and non-P300 EEG signals, single output and two-layer artificial neural network was utilized. Based on the t-test analysis, it was observed that there was a significant difference between the before and after COVID-19 infection test groups especially on Oz channel in occipital region for alpha=0.05 percent while that of for alpha=0.01 percent shows no statistical difference for P300 classification results. The latency values, on the other hand, before and after COVID-19 infection did not represent any difference for both significance levels. It is clearly understood from the literature that COVID-19 negatively effects to the microvascular bed. Therefore, it might be expected that it could cause to reduce the P300 based BCI performance. This was the first study to investigate the impact of COVID-19 on P300-based BCI performance, taking into account the EEG signals of the COVID-19 infection. The obtained results showed that although the COVID-19 infection did not generally effected P300 based BCI application performance and latency values, the performance of the occipital region electrodes slightly effected.

Implementation of a Graphic Interface Development Tool for Prolog

The work examines the current problems of the spread of use of logical programming in the development of commercial multi-platform software applications, tools for convenient development of a modern graphical interface to the logical programs. Libraries with similar concepts of use have been analyzed and described. The purpose of the proposed concept, which is implemented as an open source library, is described, and the advantages of the proposed tools over similar existing tools are indicated. The main feature and advantage of the proposed concept is the implementation of Prolog business logic and interface by means of JavaScript usage of child processes. The proposed concept of interface to Prolog takes full advantage of the possibilities provided by async await. A framework library has been created for the use of Logic Programming in graphical interface development without losses in the application performance. The paper describes the proposed concept and the developed framework (library). The ways to further improve the possibilities for expanding the purpose of the implemented library were identified. The directions of further simplification for programmers of integration of the graphic interface to logical programs have been defined. A significant advantage of the proposed tool is the easy-to-use functions to wrap and control the correctness of requests to the Prolog. The main goal of the library is to create an environment for the Prolog developers where they can create any type of software, which is meant to be user friendly, fast, and cross platform using modern and flexible. This concept also tries to solve disadvantages and architectural problems that were found in other libraries. The safety of library functionality has been analyzed. The concept of potential horizontal application scalability is described. Conclusions and future of libraries were introduced, in which the usage of TypeScript for type-safety and avoidance of run-time errors is mentioned. Overall, the library extends the use of Prolog beyond logical programming and takes a leap forward in its progress.

Mathematical Model For Forwarding Packets In Communication Network

In recent years, SDN technology has been applied to several networks such as wide area network (WAN). IT provides many benefits, such as: enhancing data transfer, promoting Application performance and reducing deployment costs. Software Defined-WAN networks lack studies and references. This paper introduced a system for SD-WAN network using PH/PH/C queues. It concentrates on the study of algebraic estimates the probability distribution of the system states. The Matrix-Geometric solution procedure of a phase type distribution queue with first-come first-served discipline is used.