Database Technology Research Articles

Neural Network-based Pattern Recognition in the Framework of Edge Computing

Neural network (NN) model has been widely used in pattern recognition (PR), speech recognition, image processing and other fields, but its application in edge computing (EC) environment faces performance and energy consumption problems. This article first introduced the basic structure and training process of NN, including backpropagation algorithms. Then, this article presented a NN modeling approach based on EC, including NN model compression, distributed NN model and knowledge distillation approach. Finally, this article implemented a PR model for the MNIST (Mixed National Institute of Standards and Technology database) dataset and analyzed the experimental results. The experimental outcomes indicated that the presented approach can significantly enhance the performance of the NN model in the EC environment, while ensuring a high recognition accuracy. The NN modeling approach based on EC can reduce the amount of computation and storage of the NN, thus improving the operating efficiency of the NN in the EC environment by 6%-12%. The NN modeling approach based on EC can optimize the performance and efficiency of the NN model in the EC environment, and provide new ideas and approaches for the application of NN in the EC environment.

Adjustable artificial neuron based on vortex magnetic tunnel junction

In this Letter, we demonstrate an adjustable artificial neuron based on vortex magnetic tunnel junction (MTJ). By applying a bias current to vortex MTJ, the device exhibits splendid characteristics of stochastic switching and nonlinear rectification. The stochastic switching probability induced by spin transfer torque as a function of bias current can simulate sigmoid activation functions. The nonlinear spin-torque microwave rectification through injection locking is similar to a ReLU-like activation function. These two behaviors further are used to perform the recognition of handwritten digits in the Mixed National Institute of Standards and Technology database, with a produced accuracy of up to 93.56% and 93.25%, respectively. Our work provides a potential way for the construction of artificial neuron based on vortex MTJ.

Construction of environmental vibration prediction model for subway transportation based on machine learning algorithm and database technology

Vibrations generated in the metro transport environment are mainly caused by, vibrations generated by the interaction between the metro and the track during operation. and the change of vibration factors will affect the normal operation of the subway. However, it is difficult to have a model that can achieve the characteristics of high accuracy, fast computing speed and wide range of use in the traditional metro rail transportation environment prediction. Therefore, this research uses database theory and machine learning algorithms to predict the vibration of subway transportation environment. The experimental results show that the average difference between the whole prediction value and the real value is 1.4 dB, of which the maximum difference error value is 0.29%, the maximum error difference is 8.2%, and the approximate value is 6.2 dB, and the four averages predicted in 40 m are relatively small as 1.6 dB, and the average error value of prediction ability between 40 and 100 m is 1.72 dB, and the experimental prediction value and real value are in good agreement. The agreement between the experimental prediction and the real value is very good. Therefore, the model is able to predict the vibration model of the subway transportation environment with a high degree of agreement and accuracy.

Prospects for applying distributed database technology in medical property resources management within military health care

When considering the problems of digital transformation of various spheres of life support for the country and maintaining the defense capability of the state, it seems advisable to focus on the most effective digital technologies. Among them, in the context of an increasing risk of cyber threats, blockchain technology, which has a promisingly increasing potential for information security and demonstrates dynamic development in many areas of socio-economic life, primarily attracts at- tention. The article discusses the historical aspects of the development of blockchain tech- nology, presents the results of a study of its main characteristics, international and domestic experience of application, and also presents prospects for implementa- tion in the management of medical property resources. It has been determined that in the context of using distributed database technology in the field of military healthcare, the preferred solution is to use a closed type of blockchain, characterized by a high degree of digital security and scalability. Based on the results obtained during the study, the main advantages and limita- tions of implementing distributed database technology within military healthcare were highlighted. It has been determined that in order to increase the efficiency of the medical ser- vice, it is promising to build a single digital loop for managing medical property resources on the basis of blockchain technology in accordance with the territorial medical support system. It is concluded that digital transformation through the integration of blockchain technology into the practical activities of a medical service can be an effective tool for optimizing the processes of managing medical property resources. However, further comprehensive study of this issue is required, as well as justification and development of possible modifications of existing digital solutions, taking into ac- count the needs of military healthcare and the need to rationalize the provision of medical equipment to assigned contingents.

A Reflective Study of Computerized Database Application Technology and Maintenance

In the era of digitalization, computer databases have become an indispensable technological support across various industries. They not only house a vast amount of data but also enable efficient management and swift retrieval of this information. Concurrently, advancements in database application technology and maintenance have become crucial in ensuring the stable operation of information systems. However, contemporary computer database technology still faces numerous challenges such as data compatibility, automation levels, and flexibility in data transmission. Therefore, this study focuses on the key application techniques and maintenance mechanisms of computer databases, exploring measures to enhance data compatibility, improve production automation, strengthen the flexibility of data transmission, and bolster technical support. These efforts aim to drive the development of computer databases towards efficiency, stability, and security, thus laying a solid data foundation for the sustainable development of the entire information society.

Anomaly detection using deep learning based model with feature attention

<span lang="EN-US">Anomaly detection is a difficult problem with numerous industrial applications, such as analyzing the quality of objects using images. Anomaly detection is the process of identifying outliers in a given dataset. Recently, machine learning approaches to computer vision problems have outperformed classical state-of-the-art approaches. Anomaly detection problems can be solved using supervised approaches. However, labelled datasets are hard to obtain. Thus, many researchers have taken an unsupervised approach to solving the problem of anomaly detection. In this study, we use an adversarial auto encoder model as a base model and create a custom model to detect anomalies in images and videos. The model was trained exclusively on normal data. The modified national institute of standards and technology database (MNIST) dataset achieved an area under curve (AUC) score of 0.872 for anomaly detection, while the University of California San Diego (UCSD) anomaly dataset (Video dataset) achieved an AUC score of 0.74 for Ped1 and 0.87 for Ped2. To calculate the anomaly score, the concept of attention weights is combined with the reconstruction loss, and the proposed method outperformed other similar methods designed for the same problem. However, the usefulness of the proposed model was demonstrated through the detection of anomalies, and the model is still being improved for use in real-world situations.</span>

Element Regulation and Dimensional Engineering Co-Optimization of Perovskite Memristors for Synaptic Plasticity Applications.

Capitalizing on rapid carrier migration characteristics and outstanding photoelectric conversion performance, halide perovskite memristors demonstrate an exceptional resistive switching performance. However, they have consistently faced constraints due to material stability issues. This study systematically employs elemental modulation and dimension engineering to effectively control perovskite memristors with different dimensions and A-site elements. Compared to pure 3D and 2D perovskites, the quasi-2D perovskite memristor, specifically BA0.15MA0.85PbI3, is identified as the optimal choice through observations of resistive switching (HRS current < 10-5 A, ON/OFF ratio > 103, endurance cycles > 1000, and retention time > 104 s) and synaptic plasticity characteristics. Subsequently, a comprehensive investigation into various synaptic plasticity aspects, including paired-pulse facilitation (PPF), spike-variability-dependent plasticity (SVDP), spike-rate-dependent plasticity (SRDP), and spike-timing-dependent plasticity (STDP), is conducted. Practical applications, such as memory-forgetting-memory and recognition of the Modified National Institute of Standards and Technology (MNIST) database handwritten data set (accuracy rate reaching 94.8%), are explored and successfully realized. This article provides good theoretical guidance for synaptic-like simulation in perovskite memristors.

빅데이터 분석을 통한 사이버대학의 미디어커뮤니케이션 전개 행태 연구 및 지방 사이버대의 대응방향 제언

This study aims to analyze the media communication activities of cyber universities that are developing in the online space in the competitive market of cyber universities, which is intensifying day by day, and to analyze the communication messages related to cyber universities accumulated online as a result. For the empirical investigation, we utilized big data-based technology and analyzed through text mining and semantic linkage network analysis using the keyword 'cyber university', and conducted frequency analysis, centrality analysis, cluster analysis (CONCOR), and N-gram analysis. Through this study, we derived media communication strategies that can be taken by cyber universities and improvements for establishing media communication strategies of cyber universities in Daegu Gyeongbuk region.

Riding the tides of change: A bibliometric expedition on technology-enabled circular economy

This paper presents a bibliometric expedition to explore the potential of the technology-enabled circular economy. The study focuses on publications related to the circular economy and technology to maximize the quantity and materials used during production and recycling for the sustainability of the circular economy. Scopus was used to identify technology-supported circular economy publications. The study employs bibliometric analysis to analyze references, document content, and networks using R and RStudio tools, VOSviewer, and Microsoft Excel. The investigation revealed 524 documents from 1974 to 2023. The research area was analyzed using coauthorship, co-occurrence, and bibliometric coupling analysis. The results show the development of research on circular economy issues, trends in factors through the most dominant network, and the attribution of circular economy publications supported by technology in the Scopus index database. This study provides insights into circular economy practices in manufacturing companies and the potential of a circular economy supported by technology for manufacturing.

BDIDA-IoT: A Blockchain-Based Decentralized Identity Architecture Enhances the Efficiency of IoT Data Flow

With the booming development of the Internet of Things (IoT) industry, millions of data are generated every day. How to use and manage these data safely and efficiently has become a hot issue of concern to people. Due to the accumulation of IoT data, the isolated data island phenomenon makes it difficult to connect and interact with each data owner, and the security and privacy of IoT data also become a challenge. Blockchain is a decentralized database technology that uses distributed accounting to ensure reliable data transmission and access, along with smart contracts that can be executed automatically to program and manipulate data. At the same time, blockchain techniques have stronger security and privacy, which can better meet the needs of users. In this paper, we analyze the current management mode and challenges of IoT data and propose an IoT data platform based on blockchain. The proposed platform takes into account important factors including IoT data traceability, IoT data trusted transactions, etc. Our IoT data platform achieves the trusted management and transaction of IoT data. We also propose follow-up optimization solutions to expand the application scope of our platform and achieve more efficient management of IoT data.

The application of database systems in information management

Database technology has always been a focal point of interest for enterprises and organizations in the field of information management. With the continuous growth and diversification of information, effective information management has become crucial. This paper aims to explore the extensive applications of database systems in information management. Firstly, the paper reviews relational and non-relational databases. Subsequently, it delves into the current applications of database systems in various domains, including enterprise management, retail, education, and government and public services. In the realm of enterprise management, database systems provide a solid foundation for information management by ensuring the timeliness, accuracy, and reliability of data. In the retail industry, they support inventory management, sales analysis, and enhance the user experience. In education, database systems are used for student information management, teaching data analysis, and online learning. In the government and public services sector, they facilitate information sharing and data transparency, while playing a critical role in crisis management and emergency response. This paper highlights the significance and diverse applications of database systems in different domains, offering insights into the current research trends and future prospects in this field.

The Impact of Database on Geographical Information System and Smart Cities

The research discusses the substantial influence of databases in smart cities, affecting various facets and people's lives. It emphasizes the impact on Information Technology (IT) spending and database features, allowing access from anywhere at any time. Key elements crucial for smart city implementation include sustainability, adaptability, governance, improved living conditions, resource management, and city amenities. The research highlights defined components of smart cities and their applications, such as healthcare, mass transit, education, and energy services. It also focuses on how integrating Geographic Information Systems (GIS) aids decision-making. The study emphasizes the use of cloud computing for smart city database systems, outlining its benefits in data collection, storage, and analysis across various cloud nodes and facilities. It also highlights industries such as health insurance, public transportation, smart buildings, and energy that benefit from these technologies in decision-making processes. The paper's objective is to review database applications in smart cities and explore the potential use of big data..

Effectiveness of musculoskeletal manipulations in patients with neck pain: a protocol for a systematic review and network meta-analysis

IntroductionNeck pain is a common problem that severely affects physical and mental health. While musculoskeletal manipulations are recommended as the first-line treatment for adults with neck pain, the comparative effectiveness...

A Common Data Dictionary and Common Data Model for Additive Manufacturing

Additive manufacturing (AM) leverages emerging technologies and well-adopted processes to produce near-net-shape products. The advancement of AM technology requires data management tools to collect, store, and share information through the product development lifecycle and across the material and machine value chain. To address the need for sharing data among AM developers and practitioners, an AM common data dictionary (AM-CDD) was first developed based on community consensus to provide a common lexicon for AM, and later standardized by ASTM International. Following the AM-CDD work, the development of a common data model (AM-CDM) defining the structure and relationships of the key concepts, and terms in the AM-CDD is being developed. These efforts have greatly facilitated system integrations and AM data exchanges among various organizations. This work outlines the effort to create the AM-CDD and AM-CDM, with a focus on the design of the AM-CDM. Two use cases are provided to demonstrate the adoption of these efforts and the interoperability enabled by the AM-CDM for different engineering applications managed by different types of database technology. In these case studies, the AM-CDM is implemented in two distinct formats to curate AM data from NIST—the first in XML from their additive manufacturing material database and the second in OWL from their 2022 AM bench database. These use cases present the power of the AM-CDM for data representation, querying, and seamless data exchange. Our implementation experiences and some challenges are highlighted that can assist others in future adoptions of the AM-CDM for data integration and data exchange applications.

Comparative Analysis of Phytocompound Variations in Leaves, Bark and Roots of Allspice (Pimenta Dioica) Collections in Tanzania

Allspice, scientifically known as Pimenta dioica, holds potential as a natural source of beneficial compounds that have been historically used to address various human health concerns. The aim of this research was to explore differences in the compounds found in parts of Allspice (i.e., the leaves, bark, and roots). Petroleum ether, dichloromethane, and methanol were used to extract the substances from each part; the resulting crude extracts were then analyzed using gas chromatography mass spectrometry. To interpret the obtained data, the National Institute of Standards and Technology database was referred to for a spectra analysis. The findings indicated that the leaves contained 81 phytocompounds, bark had 18 types, and roots exhibited 12 varieties. Prominent phytocompounds found in the leaves were eugenol in 72.24% – 73.91% of the total compounds detected. Bark was primarily composed of 1,4-Methanoazulene, decahydro-4,8,8-trimethyl-9-methylene-, [1S-(1. alpha.,3a. beta.,4. alpha.,8a. beta.)] in 74.35% – 84.24%; while roots contained γ-sitosterol at an 86.08% concentration level. In terms of solvent performance, methanol exhibited high efficiency on leaves, while dichloromethane demonstrated optimal results on bark and roots. The findings confirm significant variations in phytocompound composition in different parts of Allspice and underscores the importance of considering specific types of phytocompounds, as well as extraction techniques to achieve valuable outcomes.

Identification and verification of anoikis-related gene markers to predict the prognosis of patients with bladder cancer and assist in the diagnosis and treatment of bladder cancer.

The recurrence and mortality rates of bladder cancer are extremely high, and its diagnosis and treatment are global concerns. The mechanism of anoikis is closely related to tumor metastasis. First, we obtained all the data needed for this study from a public database through a formal operational process. The data were then analyzed by bioinformatics technology. Through the limma package, we screened and obtained 313 anoikis-related genes [false discovery rate (FDR) <0.05, |log fold change (FC) | >0.585]. Then, through univariate independent prognostic analysis, we further screened 146 genes (P<0.05) related to the prognosis of bladder cancer from 313 differential genes. These 146 prognostically relevant differential genes were used for least absolute shrinkage and selection operator (LASSO) regression for further screening to obtain model-related genes and output model formulas. Through the nomogram, we can calculate the survival rate of patients more accurately. The accuracy of the nomogram was also confirmed by calibration curves, independent prognostic analysis, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) curves. We then analysed the sensitivity of immunotherapy in bladder cancer patients with different risk scores via Tumor Immune Dysfunction and Exclusion (TIDE). Through bioinformatics technology and public databases, a prognostic model including 9 anoikis-related genes (KLF12, INHBB, CASP6, TGFBR3, FASN, TPM1, OGT, RAC3, ID4) was obtained. Integrating risk scores with clinical information, we obtained a nomogram that can accurately predict patient survival. By querying the immunohistochemical results of the Human Protein Atlas database, two of the nine model-related genes (FASN, RAC3) have the value of further research and are expected to become new biomarkers to assist the diagnosis and treatment of bladder cancer. Through immune-related analysis, we found that patients in the low-risk group appeared to be more suitable for immunotherapy, while drug sensitivity analysis showed that bladder cancer patients in the high-risk group were more sensitive to common chemotherapy drugs. In this study, a prognostic model that can accurately predict the prognosis of patients with bladder cancer was constructed. FASN and RAC3 are expected to become a new biomarker for the diagnosis and treatment of bladder cancer.

Prediction of stability constants of metal-ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity.

The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted logK values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal-ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R2 of 0.959 ± 0.007). For relative logK predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. We showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.

Examining the Progression of Blockchain Technology and Its Implementation During Digital Economy

Blockchain technology is a peer-to-peer, distributed, and innovative database and disruptive information technology. It has features such as decentralized organization, tamper resistance, and anonymity. It has become the most critical element in developing a new generation of information infrastructure in China and a core component in driving the digital economy. Blockchain relies on the Internet ecosystem, providing not only the foundation for trusted protocols but also unlimited potential applications in inclusive financial services, digital assets, e-government, and other fields. It integrates the most advanced information technologies in modern society, making it of great significance in reshaping and developing the digital economy. This article explores the characteristics and development history of blockchain technology, as well as its current research status and potential theoretical value and practical significance when applied to digital economic scenarios. At the same time, it will discuss the current limitations of blockchain research, possible future directions, and development methods.

Big data Research on the Application of Tracking Policy Audit in the Implementation of Tax Reduction and Fee

Since 2018, chinese government have conducted tracking audits on the implementation of tax reduction and fee reduction policies, facing the challenges of insufficient audit resources and massive unstructured data storage. This article attempts to introduce big data technology into tracking audit policies. This article elaborates on the importance and urgency of tracking and auditing the implementation of tax and fee reduction policies. The impact of big data technology in the field of auditing on the tracking and auditing of the implementation of tax and fee reduction policies is also discussed. It is proposed that auditing agencies should apply big data technology to the tracking and auditing of the implementation of tax and fee reduction policies. And we also attempted to design an audit plan based on Neo4j graphical database technology, which includes audit objectives and scope, audit content and focus, specific big data audit steps and audit results. In response to the problems that hinder the application of big data technology, suggestions have been put forward to improve the standardization of the data process system, actively promote more efficient big data technology, and increase the training of big data audit talents.

Neo4jsbml: import systems biology markup language data into the graph database Neo4j.

Systems Biology Markup Language (SBML) has emerged as a standard for representing biological models, facilitating model sharing and interoperability. It stores many types of data and complex relationships, complicating data management and analysis. Traditional database management systems struggle to effectively capture these complex networks of interactions within biological systems. Graph-oriented databases perform well in managing interactions between different entities. We present neo4jsbml, a new solution that bridges the gap between the Systems Biology Markup Language data and the Neo4j database, for storing, querying and analyzing data. The Systems Biology Markup Language organizes biological entities in a hierarchical structure, reflecting their interdependencies. The inherent graphical structure represents these hierarchical relationships, offering a natural and efficient means of navigating and exploring the model's components. Neo4j is an excellent solution for handling this type of data. By representing entities as nodes and their relationships as edges, Cypher, Neo4j's query language, efficiently traverses this type of graph representing complex biological networks. We have developed neo4jsbml, a Python library for importing Systems Biology Markup Language data into a Neo4j database using a user-defined schema. By leveraging Neo4j's graphical database technology, exploration of complex biological networks becomes intuitive and information retrieval efficient. Neo4jsbml is a tool designed to import Systems Biology Markup Language data into a Neo4j database. Only the desired data is loaded into the Neo4j database. neo4jsbml is user-friendly and can become a useful new companion for visualizing and analyzing metabolic models through the Neo4j graphical database. neo4jsbml is open source software and available at https://github.com/brsynth/neo4jsbml.