Continuous Improvement Of Technology Research Articles

Feasibility of Using Seq-GAN Model in Vulnerability Detection of Industrial Control Protocols

Continuous improvement of internet technology has driven the continuous progress and improvement of industrial control systems, and provided more support for security vulnerability detection in this field. Combining the GAN model, the detection model based on Seq-GAN in industrial control protocol vulnerabilities constructed in this article provides more options for further improving the security of industrial control systems, and can detect and analyse security vulnerabilities in industrial control protocols more efficiently and accurately. By comparing the performance of different models for security vulnerability detection, the Seq-GAN model has smaller prediction errors, can also obtain higher G-mean and F1-score values, and has sufficient reliability. At the same time, it can also improve the efficiency of vulnerability detection in industrial control systems, and can achieve better comprehensive detection performance. Therefore, the application of the Seq-GAN model in industrial control protocol vulnerability detection can provide more support for improving security detection in this field.

Construction and Analysis of QPSO-LSTM Model in Network Security Situation Prediction

The continuous improvement of artificial intelligence technology has deepened its application in many fields and provided more support for predicting network security situations. QPSO-LSTM model based on LSTM neural network and fused with QPSO algorithm provides more options for improving network security situation prediction, further enhancing the effectiveness of network security situation prediction, and enabling more efficient and accurate prediction and analysis of network security situations. By comparing the applications of different types of algorithms in network security situation prediction, it was found that the QPSO-LSTM model has smaller prediction errors, can achieve higher prediction accuracy, and can also obtain higher F1-score and AUC values; the shorter identifying runtime also lays the foundation for improving the speed and efficiency of network security situation prediction. Therefore, in the field of network security situation prediction, the application of QPSO-LSTM model can provide more support for further improvement and improvement of security situation prediction performance in this field.

The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis

Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.

Enhanced MADM framework for fuzzy comprehensive evaluation of attack ability of basketball defenders with the triangular fuzzy neutrosophic numbers

With the continuous improvement of modern basketball technology, higher requirements have been put forward for the personal abilities of basketball players. As the core of an organization, the offensive ability of a defender largely determines the team’s performance. Therefore, it is necessary to objectively evaluate the attacking ability of defenders. Traditional techniques cannot objectively reflect the true level of players due to their strong subjectivity. Therefore, establishing a scientific evaluation technique is particularly important. The fuzzy comprehensive evaluation of attack ability of basketball defenders is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) technique is designed with help of cross-entropy and triangular fuzzy neutrosophic sets (TFNSs). Furthermore, Then, TFNN-CE technique is addressed to solve the MADM. Finally, a numerical example for fuzzy comprehensive evaluation of attack ability of basketball defenders is given and some comparisons are conducted to r illustrate advantages of the designed technique. The main contribution of this paper is addressed: (1) The TFNN-CE technique is addressed under TFNSs; (2) the TFNN-CE technique is addressed for MADM under TFNSs; (2) the TFNN-CE technique for fuzzy comprehensive evaluation of attack ability of basketball defenders is addressed; (3) Through the several efficient comparisons, it is addressed that TFNN-CE technique is effective for fuzzy comprehensive evaluation of attack ability of basketball defenders.

Aluminum Continues to Shine in Commercial Aircraft Applications

Abstract During the 20th century, the use of aluminum alloys helped the Allied Powers win World War II and made modern global air travel possible. Continuous improvements in engine technology, alloys, and manufacturing methods enabled the development of practical and efficient aircraft with varying passenger capacity and range capability. Conventional wrought aluminum alloys make up 70-80% of the weight of single-aisle airliners. Aluminum sheet, plate, forgings, extrusions, and castings all continue to be utilized in modern aircraft construction. This article explores a brief history of the special alloys, tempers, and product forms required to meet the unique challenges of flight.

Intelligent mine safety risk based on knowledge graph: hotspots and frontiers.

The safety of mining has always been a concern. The occurrence of safety accidents not only endangers human health, but also causes serious damage to the ecological environment. With the continuous upgrade and improvement of mining technology, most mines are undergoing intelligent construction and transformation. In order to analyze security risks that should be focused on the construction of intelligent mines and the technical challenges that will be faced, we used the Web of Science (WOS) Core Collection to identify 283 publications on the field of security risks in intelligent mines from 2013 to 2022. We combined the Vosviewer, CiteSpace, and Bibliometrix R software packages to conduct an in-depth analysis and exquisite visualization of the literature, including the authors, journals, countries, hot topics, and research frontiers. This paper can help scholars comprehensively and quickly understand the research status and hotspots in the field of intelligent mine safety and risk, and it provides theoretical support for further research and exploration in the future.

Design of Industrial IoT Intrusion Security Detection System Based on LightGBM Feature Algorithm and Multi-layer Perception Network

Continuous improvement of machine learning technology has provided more support for intrusion security detection in the industrial Internet of Things (IoT). The intrusion security detection system based on LightGBM feature algorithm and multi-layer perception network fusion provides more options for improving security detection, further enhancing the effectiveness of industrial IoT intrusion security detection. By comparing the applications of different models in the security detection process, the model constructed based on the LightGBM feature algorithm can achieve higher accuracy and precision in industrial IoT intrusion security detection, as well as higher F1-score and AUC values; A more reasonable detection time also lays the foundation for improving the overall efficiency of industrial IoT intrusion security detection. Therefore, in the field of industrial IoT intrusion security detection, the detection model constructed in this article can provide more support for further improvement and improvement of IoT intrusion security detection performance.

Element relationship and extension path of clinical evidence knowledge map with Chinese patent medicine

This study aims to construct the element relationship and extension path of clinical evidence knowledge map with Chinese patent medicine, providing basic technical support for the formation and transformation of the evidence chain of Chinese patent medicine and providing collection, induction, and summary schemes for massive and disorganized clinical data. Based on the elements of evidence-based PICOS, the conventional construction methods of knowledge graph were collected and summarized. Firstly, the data entities related to Chinese patent medicine were classified, and entity linking was performed(disambiguation). Secondly, the study associated and classified the attribute information of the data entity. Finally, the logical relationship between entities was constructed, and then the element relationship and extension path of the knowledge map conforming to the characteristics of clinical evidence of Chinese patent medicine were summarized. The construction of the clinical evidence knowledge map of Chinese patent medicine was mainly based on process design and logical structure, and the element relationship of the knowledge map was expressed according to the PICOS principle and evidence level. The extension path crossed three levels(model layer, data layer application, and new evidence application), and the study gradually explored the path from disease, core evaluation indicators, Chinese patent medicine, core prescriptions, syndrome and treatment rules, and medical case comparison(evolution law) to new drug research and development. In this study, the top-level design of the construction of the clinical evidence knowledge map of Chinese patent medicine has been clarified, but it still needs the joint efforts of interdisciplinary disciplines. With the continuous improvement of the map construction technology in line with the characteristics of TCM, the study can provide necessary basic technical support and reference for the development of the TCM discipline.

Artificial Intelligence, Digital Imaging, and Robotics Technologies for Surgical Vitreoretinal Diseases

ObjectiveTo review recent technological advancement in imaging, surgical visualization, robotics technology and the use of artificial intelligence in surgical vitreoretinal (VR) diseases. BackgroundTechnological advancements in imaging enhance both pre-operative and intra-operative management of surgical VR diseases. Widefield imaging in fundal photography and optical coherence tomography (OCT) can improve assessment of peripheral retinal disorders like retinal detachments, degeneration, and tumors. OCT angiography provides a rapid and non-invasive imaging of the retinal and choroidal vasculature. Surgical visualization has also improved with intraoperative OCT providing a detailed real-time assessment of retinal layers to guide surgical decisions. Heads-up display and head-mounted display utilize three-dimensional technology to provide surgeons with enhanced visual guidance and improved ergonomics during surgery. Intraocular robotics technology allows for greater surgical precision and is shown to be useful in retinal vein cannulation and subretinal drug delivery. In addition, deep learning techniques leverages on diverse data including widefield retinal photography and OCT for better predictive accuracy in classification, segmentation, and prognostication of many surgical VR diseases. ConclusionThis review article summarized the latest updates in these areas and highlights the importance of continuous innovation and improvement in technology within the field. These advancements have the potential to reshape management of surgical VR diseases in the very near future and to ultimately improve patient care.

The Sustainable Technology Research on High-Efficiency Conversion and Utilization of Biomass-Based Energy

This study delves into the sustainability of biomass-based energy as an alternative energy source, its technical conversion methods, and its environmental and economic impacts. Biomass resources, including plants, agricultural residues, and forestry residues, are not only widely distributed and renewable but also exhibit significant potential in reducing greenhouse gas emissions and combating climate change. The research focuses on three primary conversion technologies for biomass energy: physical conversion, chemical conversion, and biological conversion. It further explores key technologies for enhancing the sustainability of biomass-based energy, including biomass pretreatment, optimization techniques, and the integration of efficient energy conversion systems. The results indicate that, despite challenges, with continuous technological innovation and improvement, biomass energy can become a more sustainable energy option.

Computational models of bone fracture healing and applications: a review.

Fracture healing is a very complex physiological process involving multiple events at different temporal and spatial scales, such as cell migration and tissue differentiation, in which mechanical stimuli and biochemical factors assume key roles. With the continuous improvement of computer technology in recent years, computer models have provided excellent solutions for studying the complex process of bone healing. These models not only provide profound insights into the mechanisms of fracture healing, but also have important implications for clinical treatment strategies. In this review, we first provide an overview of research in the field of computational models of fracture healing based on CiteSpace software, followed by a summary of recent advances, and a discussion of the limitations of these models and future directions for improvement. Finally, we provide a systematic summary of the application of computational models of fracture healing in three areas: bone tissue engineering, fixator optimization and clinical treatment strategies. The application of computational models of bone healing in clinical treatment is immature, but an inevitable trend, and as these models become more refined, their role in guiding clinical treatment will become more prominent.

Incorporating health IT into primary care transformation.

To understand the role of health information technology (IT) vendors and health IT functionality in supporting advanced primary care. We synthesized multiple rounds of surveys and interviews (2017-2022) from a mixed-methods evaluation of Comprehensive Primary Care Plus (CPC+), a multipayer model developed by CMS. CPC+ was the first federal advanced primary care reform effort that formalized health IT vendors' roles in supporting health IT implementation and specified detailed health IT requirements for practices. We conducted content analysis to identify cross-cutting themes related to health IT for both practices and vendors, comparing similarities and differences across participants and (when possible) over time. Vendors and practices reported advances in registries and dashboards for improved information management within the practice as well as strengthened relationships between vendors and practices that supported health IT implementation. However, CPC+ practices noted several gaps or challenges using existing functionalities, and both vendors and practices reported broader challenges for more transformative health IT change, particularly the lack of interoperable health information exchange needed to support care management and care coordination. Key factors constraining vendors' investment in further advances included long product development schedules, making it difficult to respond to rapidly evolving model requirements. Vendors also shared that CPC+ practices represented a small fraction of their client base, so investing in developing new functionality was not strategic unless it was more broadly relevant outside CPC+. Continued collaboration among health IT vendors, practices, policy makers, and payers could support continued technological improvements, particularly related to information exchange and communication. Aligning requirements more closely with other federal and private models could also help mitigate the risk for vendors.

A Review of Seismic Performance of Infill Wall Frame Structures under Flexible Connections

With the continuous improvement of seismic-resistant technology and the development of social and economic level, the research on seismic performance of infill wall frame structure has been deepened, and the flexible connection between walls and frames has been gradually emphasized. The purpose of this paper is to systematically elaborate the experimental research and finite element analysis method research on the seismic performance of infill wall frame structure under flexible connection according to the research results of scholars from various countries, point out the key technical problems that need to be solved in the research process of the seismic performance of infill wall frame structure under flexible connection, and put forward the outlook of the future research.

Research on Online English Teaching Platform Based On Cloud Computing Technology

The basic skills required for learning a foreign language are listening, reading, writing, speaking and translation. For a long time, most English teaching has been based on the lecture method, and students' thinking has been severely restricted, seriously hindering the development of their subjective initiative. With the continuous improvement and development of educational teaching theories and the rapid progress of science and technology, the combination of the two has continued to play a role in teaching. From the initial electronic courseware to teaching software, to independent learning platforms, all reflect the profound influence of technology on education. This article focuses on the application of modern technology in English language teaching, comparing cloud-based teaching aid platforms with computer-assisted learning software, and demonstrating both theoretically and practically. This paper compares cloud computing-based teaching aids with computer-assisted learning software and demonstrates the positive effects of cloud computing in English teaching from both theoretical and practical perspectives. The emergence of cloud computing has opened up a new path for teaching to try out. It is believed that with the continuous improvement of technology and research, cloud computing plays a greater and more important role in the field of education and teaching.

Surgical management for the intra-abdominal infection secondary to perforation of digestive tract

Abdominal infection caused by digestive tract perforation is a common cause of emergency surgery. In most cases, resection of the diseased intestinal segment is required. After resection, whether to perform a one-stage anastomosis or a stomy is the key to perplexing clinicians. With the continuous improvement of surgical technology and the increasing demand of patients to improve their quality of life, one-stage resection and anastomosis have become the most ideal surgical method. However, due to the concern about postoperative anastomotic leakage, the clinical practice of postoperative stoma rate is still high. This article reviews the surgical treatment of abdominal infection caused by gastrointestinal perforation in recent years, and discusses various preventive measures for anastomotic leakage after primary anastomosis, so as to improve the rate of primary anastomosis, which is of great significance for improving the quality of life of patients and reducing the medical burden.

Advances in nutritional metabolic therapy to impede the progression of critical illness.

With the advancement of medical care and the continuous improvement of organ support technologies, some critically ill patients survive the acute phase of their illness but still experience persistent organ dysfunction, necessitating long-term reliance on intensive care and organ support, known as chronic critical illness. Chronic critical illness is characterized by prolonged hospital stays, high mortality rates, and significant resource consumption. Patients with chronic critical illness often suffer from malnutrition, compromised immune function, and poor baseline health, which, combined with factors like shock or trauma, can lead to intestinal mucosal damage. Therefore, effective nutritional intervention for patients with chronic critical illness remains a key research focus. Nutritional therapy has emerged as one of the essential components of the overall treatment strategy for chronic critical illness. This paper aims to provide a comprehensive review of the latest research progress in nutritional support therapy for patients with chronic critical illness.

Contemporary Trends of Digitalisation of Management Processes in Higher Education: Data Analytics, Cloud Technologies and Artificial Intelligence Authors

The continuous development, improvement and diversification of digital technologies cause the issue of rethinking their use in education, in particular, in the context of digitalisation of management processes at higher education institutions. It is important to understand the current state and trends of digital technologies, which become the basis for digitalisation of management processes in higher education. For this purpose, the article has analysed Ukrainian publications in the Google Scholar search engine to identify scientific works dealing with the digitalisation of management processes in higher education, using Scopus AI, the tool based on artificial intelligence, to obtain a conceptual map of digitalisation technologies of management processes in education, studied publications in the Scopus international scientometric database to determine the significance of digital technologies described in these publications. The article analyses digital technologies currently used for management tasks in higher education, namely: data analytics, cloud technologies, and artificial intelligence technologies. The main advantages of the digitalisation of management processes in higher education are highlighted: effective administration, improvement of the decision-making process, improvement of the provision of educational services, improvement of teaching. Challenges and difficulties accompanied by digitalisation of management processes in higher education are presented: insufficient financing of higher education; lack of understanding of the educational process by the participants (from students to managers) regarding processes taking place in the modern digital society, as well as emerging or changing technologies; the need for competent management and teaching staff, a high level of digitalisation of society at the national level, orientation of educational process participants in a large number of digital technologies. As a result of the study, the current trends in digitalisation of management processes in higher education are summarised (continuation of the process of introducing digital technologies into the educational process in general and management processes in higher education in particular; integration of these digital technologies into learning management systems; introduction of updated digital technologies into the teaching process; improvement of education through digital transformation of management processes.

Analysis of Strategies and Effectiveness of Big Data Technology for Business Decision Optimization

Abstract Business decision-making is a decision-making process that relies heavily on business data, and with the continuous improvement of information technology, the level of intelligence of business decision-making has been increasing. The paper suggests relevant optimization strategies for business decision-making and creates an intelligent business decision-making process that utilizes big data. This paper mainly applies the association rule mining algorithm based on big data to business decision-making, extracting valuable information for business decision-makers from a large database of business information systems. In order to verify the operational efficiency of the improved algorithm, the traditional Apriori algorithm, FP-Growth algorithm, HBE-Apriori algorithm, and the improved algorithm are compared at the same time, and the results of the experiments show that the improved algorithm’s operational efficiency in the process of finding the maximal frequent itemset is significantly improved compared with the other three algorithms, which provides a basis for the analysis of the effectiveness of the optimization of business decision-making. The example of optimization of business decisions through big data technology found that after the optimization of business decision optimization, the asset quality and solvency of the enterprise are improved, and the operating capacity is unchanged due to the low operating capacity of the total assets of the enterprise. This paper’s method for optimizing business decisions based on association rules is shown to be feasible.

Digital transformations in arts and entertainment

This paper examines a series of digital transformations in arts and entertainment focused creative industries ( television, recorded music, live concerts, museums, computer games) impacted by digital innovations arising within these industries. Our paper demonstrates how continuous improvement in semiconductor technology (based on the doubling of chip density every two years as predicted by Moore’s law) has fostered advances in digital device capabilities, speed, portability, cost reduction and device integration that has made possible an array of innovative digital products, services and methods of engaging arts and entertainment audiences. Our analysis focuses on six innovative transformations: the digitization of the television set and transformation of television viewing and services, MP3 sound compression transformation of the recorded music industry, video streaming transformation of television programming, 3 D holography and hologram transformation of live concerts, multimedia, augmented reality (AR) and virtual reality (VR) transformation of museum audience engagement, and eSports transformation of video game industry participation and audience engagement. We also note the creative destructive impacts on non-digitized (analog) elements of some of these industries and identify transformational trends shaping the futures of these industries.

Artificial Intelligence Technology Helps Inform the Sports Industry

Abstract The continuous development and improvement of information technology and the arrival of the knowledge economy have injected new vitality into the development of the sports industry. In the context of artificial intelligence, this paper constructs a graph convolutional neural network model. It applies it to the field of sports teaching, creating an adjacency matrix based on image information to optimize the information processing speed of the neural network model. The standardized way is used to recognize and guide the various sports movements performed by students in the classroom. Through effectiveness analysis and FMS experiments, it was found that the graph convolutional neural network has a high recognition rate, and the matching rate for each section of the movement is above 70%. The mean values of angle features of students with very different body sizes remained at the same level with minimal differences under the same shooting angle. From the perspective of students’ sports performance, in the fourth week of the middle and late experimental period, the results of the experimental group and the control group students were 82.39±2.10 and 73.64±3.62, respectively, which was a difference of nearly 10 points. The results of the control group students had already lagged behind the experimental group students substantially from the perspective of the score, and the difference was significant at P=0.000<0.05. The convolutional neural network can effectively improve sports movement standardization, which in turn enhances the performance of students in physical education and promotes the information development of the sports industry.