Main Function Modules Research Articles

GeoHi-GNN: Geometry-aware hierarchical graph representation learning for normal estimation

Normal estimation has been one of the key tasks in point cloud analysis, while it is challenging when facing with severe noises or complex regions. The challenges mainly come from the selection of supporting points for estimation, that is, improper selections of points and points' scale will lead to insufficient information, loss of details, etc. To this end, this paper proposes one feature-centric fitting scheme, GeoHi-GNN, by learning geometry-aware hierarchical graph representation for fitting weights estimation. The main functional module is the continuously conducted Hierarchically Geometric-aware (HG) module, consisting of two core operations, namely, the graph node construction (GNC) and the geometric-aware dynamic graph convolution (GDGC). GNC aims to aggregate the feature information onto a smaller number of nodes, providing global-to-local information while avoiding the interferences from noises in larger scales. With these nodes distributed in different scales, GDGC dynamically updates the node features regarding to both intrinsic feature and extrinsic geometric information. Finally, the hierarchical graphical features are cascaded to estimate the weights for supporting points in the surface fitting. Through the extensive experiments and comprehensive comparisons with the state-of-the-arts, our scheme has exhibited many attractive advantages such as being geometry-aware and robust, empowering further applications like more accurate surface reconstruction.

Mobile Learning and Resource Sharing Mode of College English Translation Based on 5G Mobile Communication Technology

The application of 5G and other mobile communication technologies to education and teaching, and the construction of College English Translation mobile learning and resource sharing model and related models have important scientific value and significance for the effective organization and management of learning resources and the improvement of the speed and efficiency of college english translation resource retrieval. This research mainly combines the characteristics of complementarity and expansion of mobile learning technology for traditional online learning technology. Based on Moodle online teaching platform, this research develops a mobile learning and resource sharing mode for college english translation based on 5G mobile communication technology, designs and implements a mobile learning model based on Moodle platform, and designs the system architecture, functional modules, learning mode and learning process, Finally, the main functional modules of the system model are implemented and tested. To promote mobile learning in the teaching field to play a helping role in a wide range. The experimental results show that the AKAZE algorithm is used to optimize the model, and the overall accuracy rate reaches 74.7%, and the test probability value P (siq)=0.00, which is significantly less than the significant level of 0.05. Therefore, we believe that the application of mobile learning instructional design mode will help students improve their academic performance.

Management information platform based on Internet of Things

In order to study the financial management mode and innovation of electric power enterprises under the current situation, a research method of electric power financial management innovation in the era of Internet of Things is proposed. According to the network topology of the system and the analysis of the Android system, the client of the financial management system, the main functional modules of the system and the data warehouse are designed. The client of the financial management system will divide the client into an information interaction unit and a data calculation unit according to the actual needs of the client; the main functional modules of the system are divided into account management unit, salary management unit, project management unit, system management unit, and fixed asset management unit. Unit, revenue and expenditure management unit and report management unit, the sub-units of each unit are divided into brief analysis, and the outpatient registration fee management module and inpatient fee management module of the revenue and expenditure management unit are introduced in detail; in the hospital financial data warehouse, in the application layer An interactive platform is built between it and the data warehouse, and the module is integrated into the existing data warehouse to realize the processing, storage and operation of massive data. The proposed system has a large operating safety factor, high user satisfaction, and an average system flexibility factor of 0.94. The power financial management information platform based on the Internet of Things has a very good performance in the application. Therefore, the method in this paper can enhance the work efficiency of electric power enterprises and enhance the core competitiveness of electric power enterprises.

Protein interactome analysis of ATP1B1 in alveolar epithelial cells using Co-Immunoprecipitation mass spectrometry and parallel reaction monitoring assay

AimsAlveolar epithelial barrier integrity is essential for lung homeostasis. Na, K-ATPase β1 subunit (ATP1B1) involves alveolar edema fluid clearance and alveolar epithelial barrier stability. However, the underlying molecular mechanism of ATP1B1 in alveolar epithelial cells still needs to be understood. Main methodsWe utilized Co-Immunoprecipitation mass spectrometry proteomic analysis, protein-protein interaction (PPI) analysis, enrichment analysis, and parallel reaction monitoring (PRM) analysis to investigate proteins interacting with ATP1B1 in A549 cells. Key findingsA total of 159 proteins were identified as significant proteins interacting with ATP1B1 in A549 cells. Ribosomal and heat shock proteins were major constituents of the two main functional modules based on the PPI network. Enrichment analysis showed that significant proteins were involved in protein translation, posttranslational processing, and function regulation. Moreover, 10 proteins of interest were verified by PRM, and fold changes in 6 proteins were consistent with proteomics results. Finally, HSP90AB1, EIF4A1, TUBB4B, HSPA8, STAT1, and PLEC were considered candidates for binding to ATP1B1 to function in alveolar epithelial cells. SignificanceOur study provides new insights into the role of ATP1B1 in alveolar epithelial cells and indicates that six proteins, in particular HSP90AB1, may be key proteins interacting with and regulating ATP1B1, which might be potential targets for the treatment of acute respiratory distress syndrome.

Research on Social Network Data Analysis and Criminal Network Identification in Criminal Law

Abstract The public security system is increasingly utilizing technology and big data for criminal network identification, making the identification of criminal suspects through social data a crucial aspect of current criminal network identification. Based on our research into the verbal features of networks used to portray criminal suspects, we constructed a criminal network identification system using social networks and complex network theory. We then designed the overall framework and main functional modules. We established the case data warehouse of public security agencies and built the social relationship network of criminal suspects. This paper compares the effectiveness of the complex network (CN) algorithm with other community delineation algorithms and utilizes the criminal network identification system it constructs for case data mining and analysis. The extended modularity values of the CN algorithm proposed in this paper on four real datasets are the largest among all algorithms, and the community delineation effect is optimal. In the mining of criminal gangs, a1, a3, and a15 represent the heads of the gangs, while a56 shares similar duty content with a1, a3, and a15, albeit at a lower rank. A65 is in charge of connecting and communicating with gang members.

The Construction of a Multimodal Teaching Content Analysis System for English in Colleges and Universities

Abstract Based on multimodal theory, this paper constructs a multimodal content analysis system for college English with the digital model of teaching content, teaching interactive technology, and virtual reality technology as the main functional modules. The digital transformation of teaching content is done through the use of the particle swarm optimization algorithm and Wiki collaboration, and the learning objects in the learning content library are processed and transformed. The main technical aspects of shot detection, key frame determination, feature extraction, and feature matching are improved to establish the interactive function of English teaching videos. The quadratic error measurement algorithm enhances the edge folding algorithm and streamlines the 3D model to realize the virtual teaching simulation function. Students majoring in business English at a university in Kunming, Yunnan Province, China, are used as research objects to carry out the teaching application practice of this paper’s system. The total average English unit scores of the experimental class increased from 68.2 to 72.1 in the pre-test, and the recognition, comprehension, and utilization dimensions of the learning effect were higher than those of the control class by 0.46, 0.66, and 0.51, respectively, which showed a significant difference (P<0.05). The experimental group of subject students also had a better experience using the system in this paper.

Construction of Online Interactive Teaching Platform for College English Based on Multilayer Perceptual Machine Modeling

Abstract Most of the existing English online teaching platforms are just digital replicas of offline teaching, which cannot fully utilize the advantages of information technology. Based on the learning theory of constructivism, this paper builds the functional architecture of a personalized English interactive teaching platform and completes the design of the main functional modules. According to the quantitative results of the platform’s user proposition requirements, a matrix description method for English test papers is proposed, and an algorithm is designed to realize the automatic generation of test papers. Meanwhile, the resource recommendation mechanism of the English platform is optimized using a multilayer perceptual machine. Unstructured English learning resources are processed based on the word vector representation method, and for the problem of insufficient processing of nonlinear data in the DN-CBR model, multilayer training is carried out using the MLP to improve the perceptual degree of this part of the data, and the design goals are verified through the test of the teaching platform. The correlation coefficient between self-assessment difficulty and topic difficulty is 0.368, and the correlation coefficient between self-assessment difficulty and positive error is 0.163, indicating that there is a moderate correlation between learners’ self-assessment difficulty and the difficulty of the system topics and between learners’ self-assessment difficulty and positive error. The MLP-based pushed questions’ difficulty is adapted to the learner’s personalization level by dynamically revising it based on their learning process data.

Design and Implementation of Beam Control System Based on FPGA

As the use of phased array technology becomes more widespread, it is receiving increased attention. Among them, the excellent performance of the beam control system will directly affect the performance of the whole phased array. FPGA is the preferred control chip for developing a beam control system. Therefore, this paper designs a beam control system architecture based on FPGA, designs the main functional modules of the system in detail, and conducts simulation experiments on its important functions. The results show that the system can quickly and accurately calculate the phase shift code required by the antenna unit to achieve fast beam response.

Intelligent Safety Risk Analysis and Decision-Making System for Underground Metal Mines Based on Big Data

In view of the new situation faced by safety risk management in underground metal mines, based on a comprehensive analysis of the current situation of mine safety management business and system construction requirements, the main functional modules, overall architecture, and data interaction mode of the intelligent safety risk analysis and decision-making system were analyzed and designed. On the basis of elaborating the implementation process of the main functional modules of the system, such as multi-source safety information collection and governance, and safety risk intelligence analysis and visualization, a safety risk intelligence analysis and decision-making system was constructed, which provided efficient and real-time intelligent application and analysis services for safety in the production of underground metal mines and realized the whole process information management of collection, aggregation, processing, analysis, and visual display of multi-source mine safety risk information. The application of the system has made an essential change in the mode of mine safety risk management, realizing the active safety management goal of shifting safety risk management from post-analysis to pre-prevention, helping to improve the pertinence and efficiency of safety risk management, and greatly reducing the risk of mine safety accidents.

A blockchain-enabled framework for reverse supply chain management of power batteries

The production and sales of electric vehicles in China are continuously increasing, and a massive wave of scrap batteries is about to emerge. However, there are many challenges in the power battery recycling, such as a disorganized recycling system, a lack of standards, and low efficiency. In addition, the power battery recycling supply chain faces issues such as a lack of transparency, inadequate data management, low trustworthiness, and counterfeit products. Therefore, it's crucial to establish an efficient and sustainable power battery supply chain recycling system. Blockchain technology can provide solutions in decentralized data management, privacy protection, and anti-counterfeiting traceability. It addresses existing problems and improves the Power Batteries Reverse Supply Chain (PBRSC) ecosystem. Thus, this paper proposes a blockchain-based PBRSC management integration framework. Firstly, a four-layer blockchain power battery recycling platform is designed. Secondly, based on the proposed PBRSC framework, this paper studies three key component technologies: double-chain structure and its storage mechanism, information tracing module, and smart contract enabled battery trading management. Then, the validity and feasibility of the double-chain structure was demonstrated experimentally. Finally, the above architecture and key technologies are implemented in the system, the main functional modules are displayed on the interface, and the effectiveness of the framework is explained in cooperation with related enterprises.

Hyperconnectivity of Two Separate Long-Range Cholinergic Systems Contributes to the Reorganization of the Brain Functional Connectivity during Nicotine Withdrawal in Male Mice.

Chronic nicotine results in dependence with withdrawal symptoms on discontinuation of use, through desensitization of nicotinic acetylcholine receptors and altered cholinergic neurotransmission. Nicotine withdrawal is associated with increased whole-brain functional connectivity and decreased network modularity; however, the role of cholinergic neurons in those changes is unknown. To identify the contribution of nicotinic receptors and cholinergic regions to changes in the functional network, we analyzed the contribution of the main cholinergic regions to brain-wide activation of the immediate early-gene Fos during withdrawal in male mice and correlated these changes with the expression of nicotinic receptor mRNA throughout the brain. We show that the main functional connectivity modules included the main long-range cholinergic regions, which were highly synchronized with the rest of the brain. However, despite this hyperconnectivity, they were organized into two anticorrelated networks that were separated into basal forebrain-projecting and brainstem-thalamic-projecting cholinergic regions, validating a long-standing hypothesis of the organization of the brain cholinergic systems. Moreover, baseline (without nicotine) expression of Chrna2, Chrna3, Chrna10, and Chrnd mRNA of each brain region correlated with withdrawal-induced changes in Fos expression. Finally, by mining the Allen Brain mRNA expression database, we were able to identify 1755 gene candidates and three pathways (Sox2-Oct4-Nanog, JAK-STAT, and MeCP2-GABA) that may contribute to nicotine withdrawal-induced Fos expression. These results identify the dual contribution of the basal forebrain and brainstem-thalamic cholinergic systems to whole-brain functional connectivity during withdrawal; and identify nicotinic receptors and novel cellular pathways that may be critical for the transition to nicotine dependence.

Simulation experiment of Internet online art design teaching based on computer image segmentation and cluster algorithm.

With the continuous development of the economy, people pay more attention to spiritual needs, so the cultural industry has developed by leaps and bounds. The development of Chinese culture and art is transforming toward internationalization and industrialization, and new art forms and styles are constantly emerging. In addition, China's online education is currently in a critical period of transformation. The form of online education is comprehensively deepened, first of all in the field of basic vocational education. The second is the training of students in basic education. At present, the art design teaching courses in Chinese schools have not been fully digitized, and they are faced with the problems of outdated teaching materials, slow updating speed and high purchase cost. Based on this, this study designs an online art teaching system by introducing computer image segmentation technology. Through analysis, it can be concluded that the core algorithm of the system is superior to FCM, FCM_S1 and FCM_S2, etc., and is excellent in division entropy, coefficient and accuracy, and can fully drive the system designed in this paper. The main functional modules of the system include learning guidance, course guidance and teaching resources. Through the simulation experiment, the online art design teaching system of the Internet can fully meet the needs of many students to access through the intranet at the same time, and the external network can meet 500 concurrent users without pressure. So, the overall system has good performance. This paper introduces the computer image segmentation technology into the field of art design teaching and designs a kind of effective Internet online system.

A Scheme for Distribution Automation Terminal Design Based on ESIM

To realize the operation monitoring and control of distribution network, improve the reliability of power supply and reduce the intensity of operation and maintenance, a design scheme of distribution automation terminal based on eSIM card is proposed. Based on the analysis of eSIM technology and its characteristics, the basic workflow of eSIM card is provided in combination with the remote configuration requirements of power system. Then the main control function and communication module design of the distribution network system are analyzed and explained in detail, with emphasis on the communication interface circuit and software design. The system also integrates AngularJS and SpringBoot to make the platform have the technical characteristics of separation of front and back stations and strong reliability. At the same time, the dial-up and positioning information are integrated into one module to realize real-time monitoring and control of the remote master station. The experimental analysis results show that the scheme has certain advantages in the reliability of distribution automation terminals and the simplification of operation and maintenance management, which increases the integration and functional diversity of power equipment.

GDDS: Python software for GNSS data download

With the rapid development of global navigation satellite system (GNSS), GNSS data products have been widely used for high-precision positioning and navigation applications. They are typically downloaded from the international GNSS service (IGS) analysis centers and continuously operating reference stations (CORS). However, the conventional GNSS data download method is cumbersome, repetitive, and time-consuming, and it is challenging to meet the demands for rapid acquisition of multi-source data products. Therefore, we have developed a GNSS data download software with Python, which provides an interactive interface for the Windows or Linux operating system to realize the efficient and stable download for a large amount of GNSS data. The software includes five main function modules: Global IGS Data, Post-Processing Product, Regional CORS Data, Custom Download, and Data Decompression. It has the characteristics of diverse data products, map interaction support, and station information retrieval, which can meet the needs of different users.

Development and verification of lead-bismuth cooled fast reactor calculation code system Mosasaur

Lead-bismuth cooled fast reactor calculation code system named MOSASAUR has been developed to meet the simulation requirements from LBFR engineering design. An overview of MOSASAUR developments is provided in this paper, four main functional modules and their models are introduced: cross-sections generation module, flux spectrum correction module, core simulation module and sensitivity and uncertainty analysis module. Verification and validation results of numerical benchmark calculations, code-to-code comparisons with the Monte-Carlo code and critical experimental calculations shown in this paper prove the capabilities of MOSASAUR in dealing with lead-bismuth cooled fast reactor analysis problems with good performances. Numerical results demonstrate that compared with the Monte-Carlo code, the relative errors of eigenvalues are smaller than 350pcm when the calculations were carried out with the same nuclear data file. Compared with the measured values, the errors will increase due to the simulation details and the measurement accuracy.

GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels.

Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.

Development of Monitoring System for Heavy Metal Pollution in Sea Water of Mangrove Wetland in Leizhou Peninsula

Mangrove is a wetland ecosystem with high productivity in tropical and subtropical regions, which is of great significance for maintaining ecological diversity and protecting wetlands. With the rapid development of urbanization and industrialization, human activities such as the use of agricultural fertilizers and pollutant discharge have brought a series of problems. A large number of heavy metal pollutants enter rivers, lakes, seas and soils in different ways, bringing heavy pressure to the restoration of the ecological environment. Leizhou Peninsula Mangrove Nature Reserve, as one of the largest nature reserves in Chinese Mainland, is highly valued. Aiming at the study of heavy metal pollution in Leizhou Peninsula mangrove wetland, this paper proposes a monitoring system for heavy metal pollution in seawater of mangrove wetland. Wireless packet switching technology based on Global System for Mobile Communications (GSM) system, which provides end-to-end and wide area wireless IP connection. The main function modules of the system include: data input, real-time display of monitoring data, data query, data statistics and analysis, decision-making and early warning, early warning information release, etc. Through this technology, real-time monitoring of heavy metal pollution in sea water of mangroves in Leizhou Peninsula can be realized, which can effectively solve the problem of mangrove protection and maintain the ecological environment.

Lightweight Authentication Billing Enhancement Mechanism Based on the Fourier Fast Transform Algorithm

In recent years, with the development and extensive application of Internet technology and computer technology, and under the role of the continuous expansion and enrollment expansion of colleges and universities, how to effectively solve the Internet problems faced by teachers and students in colleges and universities has gradually become an important problem that the university information management department should solve in time. In order to continuously improve the efficiency of Internet access in universities, the Internet authentication and billing system for universities proposed in this paper take J2EE as the development platform and combines RADIUS protocol and PPPoE protocol to provide users with diversified Internet billing methods and strategies. At the same time, based on the Fourier fast transform algorithm, the development environment for the Internet authentication and billing system for colleges and universities is deployed, and after the Internet authentication and billing system is implemented, from the functional point of view of the system, the main functional modules are designed and implemented, including the authentication management function. module, billing management function module. The experimental results have shown that the throughput of PPPoE access server in the campus network using the Fourier rapid transform algorithm for traffic load balancing delay is small and stable between 200 ms and 300 ms after the optimization, which provides a feasible solution to promote the healthy and harmonious development of the university campus network.

Intelligent Face Recognition System Based on Universal Design Concept.

The rapid development of science and technology, i.e., integrated modules that are actuators and sensors, promotes the comprehensive popularization of intelligent products in people's life. More particularly, with the advent of the hybrid of the Internet of things and artificial intelligence, more and more activities preferably linked to the human beings have been automated and developed. Among those fields, intelligent face recognition has also become a basic technology in work and life. This technology has been widely used in various products and is well known by people. However, the intelligent face recognition system developed at present lacks universal design concept, and the designed system cannot be applied to various products. During the use of users, there are some problems, such as difficult operation and unfriendly interface. In order to improve the satisfaction of users' physical examination and the accuracy of intelligent face recognition, this study develops an intelligent face recognition system based on the universal design concept. First, the universal design concept is briefly described, and the calculation process of face detection algorithm and face detection algorithm based on the optical flow method is introduced in detail. Then, when designing a face recognition system, this algorithm is used to build a complete system framework. The main functional modules in this system are face detection module, face recognition module, and face training module. The functions of each module are described in detail. Finally, the face feature extraction results of the face detection algorithm based on the optical flow method are verified on the Yale face database and PIE face database. The results show that the algorithm has the highest detection and recognition rate. At the same time, the ORL face database is used to compare and analyze the system performance. The face image recognition rate of this algorithm is 92, which is the highest compared with other algorithms.

Design of Travel Route Identification and Scheduling System Based on Artificial Intelligence-Aided Image Segmentation.

This study designs a travel recognition and scheduling system using artificial intelligence and image segmentation techniques. To address the problem of low division quality of current point division algorithms, this study proposes a streaming graph division model based on a sliding window (GraphWin), which dynamically adjusts the amount of information (vertex degree information and adjacency information) referenced at each division according to the current division quality and division time by introducing a sliding window mechanism, to achieve the highest possible division while allowing loss of certain division efficiency. The goal is to improve the division quality as much as possible while allowing a certain loss of division efficiency. To meet the user's need to travel through multiple destinations with the shortest route, this thesis proposes a deep reinforcement learning actor-critic (AC)-based multiobjective point path planning algorithm. The algorithm builds a strategy network and an evaluation network based on actor-critic's multiobjective point path planning, updates the strategy network and evaluation network parameters using AC optimization training, reduces the reliance of the algorithm model on a large amount of high-quality label data, and speeds up the convergence speed of the deep reinforcement learning algorithm by pretraining, finally completing the multiobjective point access sequential path planning task. Finally, the personalized travel recommendation system is designed and implemented, and the system performance analysis is conducted to clarify the system requirements in terms of functional and nonfunctional aspects: the system architecture, system functional modules, and database tables are designed to conduct use case testing of the main functional modules of the system, and the usability of the attraction recommendation algorithm is verified through the concrete implementation of the functional modules such as attraction recommendation in the system.