Search

This study conducts a comprehensive bibliometric analysis of highly cited papers in geosciences from 2013 to 2023, examining the temporal distribution of publications, the top 20 most cited papers, categorization by Web of Science categories (WC), journal distribution, and the relationship between open access and citation frequency. The analysis reveals significant growth in geoscientific research, particularly from 2016 onwards. Highly cited papers cover diverse and multidisciplinary topics, with leading journals identified as key dissemination platforms. The study also highlights the positive impact of open access on citation rates, emphasizing the importance of accessibility in enhancing research visibility and influence. These findings provide strategic guidance for researchers and institutions aiming to maximize the impact of their scientific contributions. [ABSTRACT FROM AUTHOR]

Understanding the intellectual structure of geosciences is crucial for identifying emerging topics, which can guide future research and funding strategies. This study employs Latent Dirichlet Allocation (LDA) topic modeling and co-word network analysis to explore the intellectual landscape of highly cited geoscientific papers. Through empirical analysis, the study identifies 15 research areas, including climate change, geological processes, environmental impacts, and advancements in data analysis and remote sensing. These findings highlight the prominent role of big data analysis and machine learning methods across various geoscientific domains. Additionally, a notable gap has been identified in the integration of these computational methods with research on organic chemistry and formation processes, suggesting a potential direction for future exploration and innovation. [ABSTRACT FROM AUTHOR]

In order to solve the problem of smart grid data asset registration, this paper proposes a smart grid data asset registration system based on blockchain. The system uses the decentralization and immutable characteristics of blockchain to achieve the digital management of devices and assets in the smart grid, and uses smart contracts to encrypt and authenticate the data to ensure that the data is real and reliable. At the same time, the system can incorporate data asset registration into the scope of smart grid management, forming a complete data asset management system. In this paper, the blockchain technology is briefly introduced, and then the framework of smart grid data asset registration system based on blockchain is proposed, and its feasibility is analyzed. Finally, the paper introduces the application of smart grid data asset registration system based on blockchain technology in a provincial power grid company. [ABSTRACT FROM AUTHOR]

Over and above technical skills, software development graduates need to also poses team-based skills, such as, leadership, oral communication and project management by the time they graduate from tertiary institutions. In order to fulfil this requirement, institutions introduced a project-based module, generally referred to as capstone, to be undertaken by students on the their final year. The essence of this module, amongst other things, is to expose students to the real-world professional experience through partnership with industry. Much research has been conducted on students software projects, but very little investigation has gone into understanding how students evaluate the success of their projects. In is in this light that this study was conducted. A systematic literature review (SLR) was performed on empirical studies published between 2000 and 2022, to establish criteria used by students in evaluating software project success. The investigation led to the discovery of one (1) paper, which identified three most important success criteria used by students. However, this study identified some short-comings (eg students had to pretend to be employed graduates when evaluating the imaginary projects) with the approach used by the abovementioned research paper in determining the students' success criteria. Beside the fact that there is only one study that was found that addresses the study's phenomenon, the identified short-comings on the paper indicate that there is a need for empirical research studies on this matter. [ABSTRACT FROM AUTHOR]

This paper discusses the Digital Product Passport (DPP) as a key tool for achieving a circular economy. An architecture of the DPP is presented built upon the principles of data spaces and W3C Decentralized Identifiers (DIDs). By leveraging data spaces, the DPP enables secure and controlled data exchange among stakeholders, fostering transparency, traceability, and collaboration throughout the product's lifecycle. The use of decentralized identifiers ensures the uniqueness and verifiability of product-related information, facilitating seamless access and sharing of data. The DPP architecture offers a promising framework for realizing the circular economy by promoting resource efficiency, sustainable practices, and informed decision-making. [ABSTRACT FROM AUTHOR]

To generate a research overview of digital banking, we did a bibliometric analysis. In our bibliometric analysis, we identified 2475 papers about digital banking that were published in the previous 10 years (2013–2023) from 1492 different sources under the direction of 6777 academics. The bibliographic information was gathered using the Scopus database. The dataset was examined using the bibliometric R program Biblioshiny. The number of digital banking research publications published each year, the most relevant author, the most cited articles, thematic maps, and trending topics are all listed. A keyword analysis showed that "blockchain", "security", "digital banking", and "digital storage" appeared most often. The research results will be significant to scholars, researchers, and policymakers in banking, as the study showed the roadmap and pathways to scientifically understanding current and future research trends. [ABSTRACT FROM AUTHOR]

Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models' information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots. [ABSTRACT FROM AUTHOR]

The high costs of environmentally responsible and socially just approaches are some of the factors that hinder the implementation of sustainable projects in the industrial sector. However, upstream collaborative integration in the corrugated cardboard industry in southeastern Brazil is a reality. In this context, this study aimed to highlight the structure of a collaborative sustainable project in the context of the corrugated cardboard industry, to analyze ESG risk management in the corrugated cardboard industry in the Southeast Region of Brazil, the relationship between ESG risk management and social responsibility in corrugated cardboard industries, the strengths and weaknesses of risk management models in use by the consulted participants, and to propose innovations regarding risk analysis, applicable in sustainable projects with a collaborative bias. The methodology adopted was the case study, with consultation with managers in professional activity in the industrial sector, corroborated by document analysis and systematic review of the literature. We identified preliminary risk analysis (PRA) and failure mode analysis (FMEA) as key practices used by managers in the corrugated paper industry in the region. These practices are insufficient in the convergence of objectives, engagement with the community, and extension of the lifecycle of products and processes. Our findings show that the sustainability of a project may be an extension of the risk analyses carried out in the initial phase, mainly about strategic decision-making and the operationalization of investments. Without bias to the need for risk analysis, the life cycle analysis is also an effective way of highlighting the critical factors, weaknesses, and strengths of a sustainable project. The study indicates the so-called GUT matrix approach (gravity – urgency – tendency) as the most suitable for innovation in risk analyses oriented towards sustainable projects in the analyzed sector. [ABSTRACT FROM AUTHOR]

Knowledge graphs (KGs) are gaining prominence for their efficacy in data integration and knowledge representation within Current Research Information Systems (CRIS) Systems. By employing a semantic data model to represent entities, attributes, and their relationships, they prove versatile across scientific applications. In the era of Science platformization, particularly within CRIS systems, KGs serve to amalgamate diverse data sources and formats, facilitating the creation of interconnected data models. This enables stakeholders to access comprehensive, consistent information pertinent to their endeavors. This paper examines the pivotal roles of KGs in current and future data integration within CRIS systems, emphasizing their contributions to scientific reasoning. While their benefits include flexible knowledge modeling, support for semantic queries, and interoperability with various data sources, they face systemic limitations, particularly in methodological and technological aspects, hindering classical scientific investigations. The paper underscores the necessity for novel approaches to address these limitations, offering insights, use cases, and best practices for implementing KGs in CRIS systems. This paves the way for research institutions and scientific organizations to enhance their data analytics capabilities and support scientific reasoning effectively. [ABSTRACT FROM AUTHOR]

Research data is increasingly recognised as an essential output type in the context of research documentation. This paper aims to examine the possible use cases for showcasing research datasets in research information systems. The analysis indicates that three primary use cases play a key role in the implementation of a dataset module in an institutional or in a regional/national CRIS. The paper describes the use cases and places them in the specific context of the University of Vienna, while also referring to existing case studies of other institutions. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into current research information systems (CRIS) is becoming increasingly prevalent, driven by the need for enhanced operational efficiency and data accessibility. While AI offers promising opportunities for improving CRIS functionalities, it also raises significant ethical challenges that must be addressed. This paper examines the ethical implications of AI implementation in CRIS, focusing on issues such as autonomy, discrimination, data protection, and user rights. Drawing on insights from machine, technology, digital, and robot ethics, the paper explores strategies for mitigating these challenges and fostering responsible AI use in CRIS. Additionally, it highlights the impact of AI technology on CRIS staff and emphasizes the importance of considering societal and personal effects alongside technological advancements. By raising awareness of ethical considerations and promoting ethical AI practices, this paper aims to contribute to the development and acceptance of AI technologies in CRIS, particularly in the light of forthcoming regulations such as the EU AI Act. [ABSTRACT FROM AUTHOR]

The paper analyses the implications of the emergence of artificial intelligence (AI), especially generative AI, on current research information systems (CRIS). It reviews the recent European regulations for high-risk AI systems, the Spanish AI strategy, and the IntelComp project as use cases. The study found that the maturity of CRIS systems, coupled with the increasing complexity due to data aggregation, sets the stage for innovative AI applications. The paper proposes key domains where AI can impact and be applied in CRIS, including data management, research assessment, and advanced analytics. It also provides examples of how generative AI can be leveraged to enhance scientific information management within CRIS. The findings highlight the need to ensure the responsible and ethical development of AI technologies in the research domain. [ABSTRACT FROM AUTHOR]

Integrating Artificial Intelligence (AI) into Current Research Information Systems (CRIS) offers significant opportunities to enhance research management. This paper explores AI's potential to automate data handling, improve analytical capabilities, and enhance user experiences within CRIS. Key areas of impact include data enrichment, advanced information retrieval, trend analysis, and predictive analytics. The paper also addresses the challenges and ethical considerations of AI integration, such as data privacy, security, and algorithmic bias. Insights from a Live Poll at the CRIS2024 conference reveal high familiarity with AI among participants, optimism about its potential, and recognition of implementation challenges. By overcoming these obstacles, AI can transform CRIS, making research management more efficient and effective. The paper concludes by advocating for collaboration and dialogue to guide the responsible integration of AI in CRIS, ensuring alignment with stakeholder interests. [ABSTRACT FROM AUTHOR]

In this paper, we adopted an improved ant algorithm to conduct reliability optimization research on existing computer systems. On this basis, a novel adaptive search method was proposed and a new search strategy was adopted to improve search speed. Through experiments, it has been shown that this method has good network reliability, convergence speed, global optimization ability, and efficiency in using paths. In practical applications, the average reliability of this method is 92.3%, and its effectiveness has been verified through experiments. Based on the above research results, the improved ant algorithm proposed in this paper improves the reliability of the network and optimizes the overall performance of the network. [ABSTRACT FROM AUTHOR]

This paper focuses on the topic of renewable energy integration and carbon neutrality green technology innovation based on intelligent microgrid and focuses on the design and optimization of intelligent microgrid control to improve the utilization efficiency of renewable energy and achieve the goal of carbon neutrality. In this paper, the hardware structure of the microgrid is designed, and the solar, wind and biomass power generation systems are integrated, and the battery energy storage and supercapacitor energy storage systems are optimized. Subsequently, a complete software control system was developed, and based on the application of big data processing and machine learning technology, the intelligent management of the microgrid was realized. Then, based on the research of intelligent control strategy and optimization algorithm, the overall response speed and operation efficiency of the system were improved, and a simulation platform was built to achieve effective simulation experiments, and then the efficiency and stability of the system were verified. [ABSTRACT FROM AUTHOR]

This study specifically discusses the low-carbon emission production scheduling model and algorithm of intelligent manufacturing based on energy and environmental efficiency. This paper constructs a multi-objective optimization model and uses genetic algorithm to solve the problem, so as to verify the effectiveness of the model in many aspects, such as improving production efficiency, reducing energy consumption, and reducing carbon emissions. Using the experimental analysis results, it can be seen that the algorithm in this paper can improve the production efficiency of enterprises, reduce their energy consumption, reduce carbon emissions, and provide scientific basis and technical support for the vision of green manufacturing. [ABSTRACT FROM AUTHOR]

In today's rapidly developing chemical industry, safety production and equipment management have become the primary tasks of enterprises. However, the complexity and unpredictability of chemical equipment make safety management(SM) face many challenges and difficulties. Therefore, the demand for chemical enterprises to apply computer information(CI) technology to improve the level of equipment SM has become increasingly prominent. Based on the investigation of a chemical enterprise, this paper verifies that the application of new technologies such as artificial intelligence and Internet of Things can effectively improve the level of equipment SM. In the past five years, the number of safety accidents in equipment management in chemical enterprises has decreased year by year, and the accuracy of fault prediction has increased from 60% to 80%, which shows that the application of new technology can effectively improve the safety performance and operation efficiency of equipment, and at the same time improve the economic benefits of enterprises. On this basis, this paper puts forward some related strategies to improve the level of equipment SM by using CI technology. [ABSTRACT FROM AUTHOR]

The advent of the era of big data provides unprecedented opportunities for the transformation and innovation of corporate accounting, but also poses severe challenges to its development. Taking Company M as an example, this paper analyzes the accounting transformation under the current big data environment, and raises many new issues, such as the security of accounting information and the authenticity of information, and the transformation of accounting information data processing mode. At the same time, enterprises are also facing many opportunities, such as: the impact on accounting confirmation, enterprise financial forecasting, decision-making and cost control, and the management of enterprise accounting electronic documents in the cloud computing environment and big data environment. This paper intends to study the accounting information system under the environment of cloud computing and big data, aiming to improve the security and reliability of enterprise accounting information. This article constructed an accounting information big data analysis platform. Through experimental testing, it was found that before using the platform, the cost of hardware investment and system maintenance in 2019 was 19,803 yuan, while the cost after using the platform was 13,256 yuan. Studies have shown that using big data processing technology to extract useful information from huge financial and accounting databases can help enhance the core competitiveness of enterprises. [ABSTRACT FROM AUTHOR]

College students are in the stage of transitioning from teenagers to adults, and there are many problems that need to be adapted and adjusted in life, learning, and interacting with others. If these issues are not handled properly, it would lead to many mental health problems that affect normal learning and life. The paper use the data mining algorithm of Big data. Through systematic intelligent evaluation, it can gain an overall understanding of the mental status of students. For students, it is effective to have a clear understanding of their mental health status, and to communicate with their home and school in a timely manner regarding their psychological problems. Early treatment of mental health can be more effective in completing rehabilitation; For universities, they can have a clear understanding of each student's psychological situation to avoid some difficult situations. At the same time, universities can timely intervene and treat students with psychological problems. This paper use data mining algorithms to build an intelligent evaluation system for mental health. Through research, it is found that the accuracy of this method is 95% at the lowest and 97% at the highest. This method can comprehensively and objectively describe the changing characteristics of college students' mental health, and the results of mental health intelligence assessment are stable, providing valuable reference for their mental health management. [ABSTRACT FROM AUTHOR]

With the rise of ChatGPT, the anthropomorphic dialogue form of chatbots is sought after and relied on by a large number of users. Compared with traditional social real objects, chatbots are more controllable in social relationships. At the same time, compared with traditional real social objects, chatbots may show better emotional identification with objects. The structural equation modeling is a path analysis method based on the factor analysis and linear regression, which can be used to analyze the path relationship between intricate variables. Based on the structural equation modeling, this paper analyzes the potential pathways for artificial intelligence chatbots to induce the user's emotional dependence. The results show that the "sense of identification" and "sense of control" will cause people to have emotional dependence on the artificial intelligence chatbots, with p < 0.05. However, the psychological distance plays a regulating role in the identification-emotional dependence and the sense of control-emotional dependence, respectively. The research results in this paper show that there is a certain theoretical basis for the chatting intelligent robots to transform from a tool role to a social and emotional role. [ABSTRACT FROM AUTHOR]

Knowledge graph based search is becoming increasingly close to users' reading habits, providing a more user-friendly interactive experience. Integrating knowledge graphs as auxiliary information into recommendation algorithms can model and integrate semantic information of learning resources, providing more diverse personalized recommendation results and better alleviating the problem of information overload. In order to solve the problem of readers searching for suitable digital book resources and improve reading efficiency, the first step is to study the construction of knowledge graphs, including core tasks such as knowledge extraction, knowledge representation, and knowledge storage; Then, a mathematical model is constructed, based on the optimized graph convolutional network layer and the quantification algorithm of knowledge point centrality, to construct a universal computational framework for attention mechanism; Finally, simulation experiments were conducted to compare and analyze different models through evaluation indicators, in order to verify the advantages of the model constructed in this paper. The experimental results show that the research method proposed in this paper is superior to conventional methods such as FM, CKE, DNN, NFM, CFKG, KGAT, and KGCN. [ABSTRACT FROM AUTHOR]

The performance evaluation involves a variety of composite factors, and most factors are subjectively determined by the people, so the fuzziness of this evaluation is inevitable. This paper firstly focuses on this issue based on the dynamic influence of digital twin technology input for different kinds of digital twin technology activities. Then this paper carries out a detailed analysis and research on the main core and criterion of digital twin technology. Thirdly this paper puts forward the following suggestions so as to give full play to the role of digital twin technology input of three different kinds in activities. Lastly this paper constructs a novel quantifiable weight model of performance evaluation by applying fuzzy mathematics theory. The calculation results indicate that this model can keep an eye on the evolution rule of digital twin technology to timely change the system are the optimal plans and measures based on weight analysis, and their weights are 0.336, 0.334 and 0.330 respectively, and the rationality of quantifiable weight behavior has a significant impact on the technology development. [ABSTRACT FROM AUTHOR]

Computer geometric graphics modeling refers to the restoration of the target graphics in the computer graphics environment, and the visual presentation of users, which has been applied in the fields of graphics, virtual reality and computer-aided design. However, the traditional modeling system has some problems, such as low precision and low efficiency, so this paper proposes to use the point cloud data to extract the geometric features of the target graph. In this paper, the Particle swarm optimization-Extreme learning machine (PSO-ELM) algorithm is proposed later, and its principle and operation process are analyzed, and then the algorithm is used to improve the performance of the modeling system. In the comparative experiment of modeling accuracy in this paper, the average modeling accuracy of the intelligent modeling system using the proposed method is 96.97%, while that of the basic system is 94.70%. It is not difficult to see that the experimental results fully reflect the improvement of the modeling accuracy of the modeling system brought by the method in this paper, which is helpful to the application of computer geometric graphics modeling system in practical work. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, intelligent recommendation system has become an indispensable part of people's daily life. It has not only greatly enhanced the technological experience, but also caused people to pay more and more attention to their surroundings. This paper first introduces an algorithm based on intelligent recommendation, which plays a key role in the process of web browsing; then, this paper designs an advertising recognition model through the study of feature extraction methods and combined with advertising, aiming to solve the above problems, and verifies the accuracy and practicability of the model to help improve the overall performance of the website, so as to maximize the value utilization rate of the intelligent recommendation system in the sales promotion work of merchants. The verification results show that the processing time of advertising recognition fluctuates sharply throughout the period, sometimes increasing rapidly and sometimes decreasing rapidly. The reason for this fluctuation may be caused by different amounts of advertising and data. At the fourth point in time, the processing time for AD recognition peaked at 6 seconds, indicating that the system needed to process more ads at that point in time. At 6:00, the processing time for AD recognition quickly drops to just 3 seconds, which means that the system is processing a smaller number of ads at this point in time. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology, the amount of information and data is constantly increasing, leading to higher and higher demands for network big data analysis platforms. This paper primarily delves into the application of deep learning technology in digital text classification. In this paper, a deep learning-based algorithm is introduced in detail. It outlines the method of extracting specific numerical eigenvectors from large-scale non-negative eigenattribute samples and converting them into machine-readable code. ased on the aforementioned methodology, an analysis platform is designed, and its basic performance is simulated and tested. The test results indicate that the platform's ability to process abnormal data in Round A is 80, Round B is 90, Round C is 70, Round D is 85, and Round E is 75. The exception handling capacity of each category is represented visually through a bar chart, where the height of the bar signifies the size of the exception handling capacity for that specific category. [ABSTRACT FROM AUTHOR]

This paper summarizes the video sample collection and processing methods based on artificial intelligence platform, focusing on video noise cancellation, content segmentation and classification, and feature extraction and representation techniques. The paper believes that the deep learning technology, especially the convolutional neural network, shows great potential in image recognition and video analysis, and effectively improves the level of automation and accuracy of video processing. This paper discusses the importance of building a large-scale and high-quality video sample library, and how to improve the processing efficiency and accuracy of video data through intelligent technology. [ABSTRACT FROM AUTHOR]

This paper takes Ali yun as a case, deeply discusses the importance and implementation path of low carbon operation strategy of data center in cloud computing environment, summarizes the current situation of data center energy consumption and its impact on environment, and points out the necessity of low carbon operation. The paper discusses in detail the specific design of low-carbon data center operation strategies, including infrastructure optimization, server hardware selection, virtual machine management, data storage and transmission optimization, and carbon footprint tracking and monitoring. The article also highlights the key role of establishing industry standards, certification systems, technical training, knowledge sharing, and policy support and incentives in promoting low-carbon operational strategies. The article emphasizes the importance of multi-party collaboration and joint efforts to achieve the green transformation of data centers and the sustainable development of the digital world. [ABSTRACT FROM AUTHOR]

With the progress of electrical equipment, various faults often occur during its operation, which adversely affect the efficiency and quality of power supply. In this paper, the neural network algorithm is used to detect the system parameters. This paper first discusses the shortcomings and defects of traditional diagnosis methods, and then proposes a new method combining with existing technologies to build fault state identification criteria and classifiers based on neural network model, and also uses a variety of intelligent algorithms such as fuzzy reasoning and support vector machine to solve the common short-circuit problem of electrical equipment on the power line, so as to provide reliable security for actual operation and help the power sector. The performance of the diagnostic system is tested in this paper. The test results show that in the process of real-time monitoring, analysis and feedback processing of the fault identification system, the fault detection time is 5.2, 5.1, 4.9, 4.7, 4.6, 4.5, 4.4, 4.3, 4.2, 4.1 seconds by applying the neural network algorithm. This data represents a significant improvement in fault detection speed, from 5.2 seconds to 4.1 seconds. [ABSTRACT FROM AUTHOR]

The rapid development of digital media content and the increasing demand of users for high-quality content have made digital media content distribution technology more and more important in practical applications. Cloud computing, as an infrastructure, has the advantages of flexible scalability and high quality for storage, management and distribution of digital media content. However, more in-depth research is needed to understand the impact of content distribution strategies under different network environments and media types. In this paper, based on the cloud computing platform, a series of comparative experiments were designed to evaluate the performance of cloud computing based digital media content distribution technology from different aspects. Factors considered in this paper included different network types, bandwidth, latency, packet loss rate, media types (video, audio, image) and distribution strategies. The experimental data is collected and analyzed, and the key indicators such as download time and playback success rate are evaluated. After analyzing the experimental results, we find that the performance of digital media content distribution is significantly affected by different network environments and media types. Cloud computing based content distribution strategies have been proved to achieve the effect of fast download and high success playback rate. The response time of cloud based distribution strategies is up to 4.7 seconds, and the bandwidth utilization rate reaches 89%, and the minimum latency is 104 ms. Under the same conditions, the performance of cloud computing is generally better than that of Content Delivery Network (CDN) and Peer-to-Peer (P2P). This study is a comprehensive experimental study of cloud computing based digital media content distribution technology. It has in-depth understanding of the performance of network environment, media types and distribution strategies. These research results have important reference and guidance to optimize digital media content distribution technology and improve the user experience. [ABSTRACT FROM AUTHOR]

With the rapid development of science and technology, artificial intelligence technology benefits the field of marketing. Based on artificial intelligence technology, media communication marketing has enriched marketing means and adopted virtual endorsers for marketing publicity. From the perspective of virtual endorsers interaction, this paper studies the impact of virtual endorsers interaction on consumers' brand attitude, conducts empirical analysis through situational experiments and explores the internal mechanism, and finds that: (1) Product involvement moderates the impact of "whether virtual endorsers interact with people" on consumers' brand attitudes. For products with high involvement, virtual endorsers have a significant positive impact on consumer brand attitudes when interacting with people. For low-involvement products, "whether virtual endorsers interact with people" has no significant impact on consumers' brand attitudes; (2) Self-identity plays a mediating role in this regulation. This paper verifies the feasibility of the application of artificial intelligence in media communication marketing to a certain extent, which will help to enrich the related research of virtual spokesmen. At the same time, it provides suggestions and references for enterprise marketing practice. [ABSTRACT FROM AUTHOR]

How to further improve the efficiency and sustainability of the sharing economy is a thorny question. Many platforms are beginning to adopt methods based on intelligent data analysis, and this study aims to explore the advantages of the sharing economy model based on intelligent data analysis in terms of transaction efficiency. This paper adopts the methods of literature review and case analysis, collects and analyzes relevant literature and actual cases of sharing economy platform, studies the sharing economy model based on intelligent data analysis, and uses intelligent data analysis technology to describe the construction of sharing economy platform, and optimizes the selection of data sets. The research results show that the income growth rate of residents under the sharing economy model in this paper is between 10% and 20%, and the sharing economy model based on intelligent data analysis has obvious advantages over the traditional model in terms of transaction efficiency. [ABSTRACT FROM AUTHOR]

Because of its high-speed flight and wide field of view, UAV can carry out comprehensive monitoring and target searching in a wide area, high altitude and long distance environment, and become an important tool of modern information inspection. Drone inspection technology has been widely used in many industries such as power, logistics, agriculture and security, especially in forest inspection, which plays a key role in protecting forest resources and maintaining ecological balance. The traditional manual inspection method has some problems, such as low efficiency, high missing rate and personnel safety risk, so it is difficult to meet the needs of modern inspection. This paper presents and implements a UAV inspection system based on computer vision. The system carries out inspection through the autonomous route planning of the UAV, collects image data and transmits it to the embedded device for analysis to extract the target monitoring information. Finally, the system generates the corresponding work order and sends it to the client, realizing the efficient, accurate and safe UAV inspection. This paper not only optimizes the UAV inspection algorithm design, but also improves the accuracy and efficiency of image recognition by applying the maximum likelihood estimation method, which provides reliable technical support for various inspection tasks. [ABSTRACT FROM AUTHOR]

With the rapid development of electronic information technology, the performance of embedded microcontroller is constantly improved, and the software of UAV flight control system is also developing in the direction of multi-function and multi-task. Traditional flight control software is usually written in C language, resulting in its scalability and modular design is limited. In view of the relatively consistent core content of the flight control system software, object-oriented thought and component technology are introduced to carry out modular design and implementation of the core part, improve the scalability and flexibility of the system, and facilitate the rapid addition of new functions. Based on the analysis of the shortcomings of the existing flight control software design, this paper proposes to use C++ language to componentize the driver code, design each submodule in the form of classes, and build the overall software framework according to the dependence relationship of the software level. Through component technology, the design and implementation of the driving component are completed, and the design principle of the driving component is discussed, and the implementation process of the component is explained in detail through the concrete code, and the derivation relationship between the components is analyzed. The subscription-release communication mechanism based on component relationship is designed, the communication interface between modules is standardized, and the independence of each module of flight control software is improved. Based on the driver component, the design and implementation of each task thread are completed, the data interaction and synchronization relationship between the task threads are discussed in detail, and the implementation details and execution process of the task thread are analyzed in combination with specific components. The relationship between the driver component and the task is sorted out, the integration of flight control software is completed, and the expansibility of the driver component and the independence of the task thread are analyzed. Through functional tests, including mission execution efficiency, cycle delay, and the effectiveness of the inertial navigation module, and test flights on the quadrotor platform, the results show that the optimized flight control software has significantly improved in performance and stability. The research and optimization in this paper not only enhance the scalability and modularization design of UAV flight control system, but also provide a valuable reference for the development of flight control software in the future. [ABSTRACT FROM AUTHOR]

In the teaching of English as a second language in China, there has been a long time problem of "subjectively emphasizing the quality of vocabulary, but actually seeking the quantity of vocabulary", and it is difficult to make a substantial breakthrough in the overall teaching level. Based on the principle of educational objective taxonomy and the research method of cognitive linguistics, this paper proposes and establishes a set of theoretical framework of vocabulary escape model with vocabulary dimension theory, mastery level theory, vocabulary hierarchy structure theory, vocabulary escape and transformation theory and vocabulary component analysis method as the core. On this basis, the general principle and method of English vocabulary teaching system based vocabulary escape model and B-Learning. Through the innovative vocabulary escape model and teaching design model, this paper aims to break through the bottleneck of traditional English vocabulary teaching and improve the teaching effect. The research results not only enrich the theory of vocabulary teaching, but also provide theoretical and practical support for the research and development of online English vocabulary teaching system. Future research could further optimize the model and combine big data and artificial intelligence technologies to meet the needs of different learners and promote the overall progress of English vocabulary teaching. [ABSTRACT FROM AUTHOR]

In recent years, blockchain technology has received attention because of its decentralized, immutable and other characteristics, but it faces storage and retrieval challenges. To address these challenges, this paper introduces IOTA distributed ledger technology, which solves the scalability and cost problems of traditional blockchains. By analyzing and experimenting the Tangle, the underlying consensus structure of IOTA, this paper reveals the main factors affecting its development, and proposes a segmented adaptive cutting-edge transaction selection algorithm to optimize the system performance. At the same time, based on IOTA distributed ledger, this paper proposes a data encryption storage and retrieval scheme, which speeds up the data link and retrieval speed, and ensures the integrity and security of data. Finally, this paper discusses the application of blockchain in accounting informatization, and puts forward the scheme of building a new generation of accounting informatization platform, which is of great value to the construction of accounting informatization. [ABSTRACT FROM AUTHOR]

In the context of the complex and changing international situation and the uncertain global political and economic situation, cross-border trade faces many new challenges. In the form of "barter", cross-border barter avoids the credit creation needs of financial intermediaries, but puts forward higher requirements for the information coordination and resource integration of each subject in the transaction chain. The construction of a new type of cross-border barter biological system emphasizes the cooperation and interaction among the participants, and effectively integrates the barter link by means of financial technology, which is of great significance to promote the development of new cross-border barter trade. In this context, this paper proposes an ecosystem framework combining fintech and cross-border barter, and deeply studies the settlement model based on cross-border barter. The results show that the new cross-border barter ecosystem consists of platform leaders, barter trading entities, logistics warehousing supporters and environmental parasitism groups. The application of fintech can promote the collaboration of all parties in the ecosystem and realize the complementary advantages of system members. Through evolutionary game analysis, this paper finds that promoting cooperation among various groups, optimizing incentive policies of leaders and establishing fair profit distribution mechanism are conducive to the stable development of cross-border barter ecosystem. This paper designs a blockchain-based cross-border barter settlement model by referring to the Trade Swap Support Instrument (INSTEX) and the Bank of Kunlun's settlement model for Iran. Blockchain technology is used to achieve property rights allocation and value transfer of barter assets, and the barter settlement model of cross-border circulation of goods and domestic settlement of funds is realized through the "closed loop" of debt repayment, further exploring the deep-level settlement problems of cross-border barter, and providing theoretical support and practical guidance for the development of new cross-border barter trade. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing technology, the data outsourcing service model in cloud environment is becoming increasingly popular. However, while cloud storage technology provides users with efficient services, its complex architecture also brings significant challenges to data privacy security. Because cloud service providers are not completely trusted, there is a risk of tampering or leaking user data, and third-party audits or malicious attacks by unauthorized users may also lead to data damage or loss. Therefore, how to realize the privacy protection and integrity audit of outsourced data in the cloud environment has become an important research topic of information security management. This paper focuses on the information security management method in the cloud environment, and deeply discusses the privacy protection and integrity audit technology of outsourced data. Aiming at the possible risk of tampering or leakage in the process of managing outsourced data by cloud service providers, a variety of privacy protection schemes based on data staining, data segmentation confusion and secure access control of private data are proposed. We study and propose a multi-copy integrity audit method that supports dynamic update of data, which realizes efficient dynamic operation and security verification through signature algorithm and random mask technology. We construct a privacy-protected data dynamic update integrity audit method, which utilizes hierarchical multi-branch tree data structure and random mask technology to significantly improve audit efficiency and security. In this paper, hash message authentication code (HMAC) and indistinguishable obfuscation (IO) techniques are used to propose a low-cost and efficient third-party audit scheme, which further reduces the computational overhead and verification cost. [ABSTRACT FROM AUTHOR]

Cold chain logistics has become a core link in ensuring drug quality and safety, especially for temperature sensitive drugs such as vaccines and biologics. This paper aims to develop a drug cold chain transportation risk assessment and early warning system based on big data, utilizing advanced technologies such as big data analysis and machine learning to achieve real-time monitoring, intelligent analysis, and early warning of various risk factors in the drug cold chain transportation process. This paper proposes a comprehensive architecture that includes data collection layer, data transmission layer, data processing and storage layer, analysis engine, warning and response module, user interface, and system integration interface. This paper collects key environmental data in real-time through sensors, such as temperature and humidity, and uses a secure data transmission layer to transmit the data to the central processing system. The construction of the risk assessment model is based on a series of quantifiable risk indicators, including temperature deviation, humidity deviation, location deviation, etc., and through data standardization and weight allocation, combined with a machine learning model to perform risk scoring and grading. The early warning mechanism implementation part includes early warning rules and threshold settings as well as real-time monitoring and dynamic warning to ensure that the system can respond to potential risks in a timely manner. The results of the system performance evaluation show that the risk evolution prediction deviation rate remains at a very low level, with a maximum of 1.4%, the false alarm rate is very low, with a maximum of 0.15% and a minimum of 0.03%, and the risk situational awareness sensitivity coefficient remains at a high level, with a minimum of 0.8. These results indicate that the developed system performs well in terms of risk prediction accuracy, warning timeliness and risk perception sensitivity. [ABSTRACT FROM AUTHOR]

In order to serve the "Manufacturing Power", adapt to the new scientific and technological revolution and industrial change, and support the innovation-driven development, the Ministry of Education (MOE) has been vigorously promoting the construction of the "new engineering discipline", exploring a new engineering education model in China, and serving the construction of the "Education Power". Education Power". New Engineering is an important initiative for Chinese universities to realise the principle of cultivating innovative talents, and it is also a major development trend and choice for the development of Chinese higher education. In the rapid development of education today, the integration of modern information technology into classroom experiments is essential to improve the teaching effect. This paper discusses the application of MATLAB in mathematical modelling to analyse experimental data, especially in physics experiments. Using MATLAB, students can deepen their understanding of mathematical concepts and physical principles through visualisation and interactive programming. In this study, MATLAB was used to assist teaching and learning, and a comparison experiment was conducted between a control group and an experimental group. The results show that MATLAB can significantly improve students' understanding and retention of knowledge, stimulate learning interest and enhance modelling ability. Analysis of the experimental data showed that the average score of students in the MATLAB-assisted class was 71.06, which was significantly higher than that of the traditional class (65.64), highlighting the effectiveness of MATLAB in enhancing students' understanding and retention of physics concepts. However, some challenges such as increased complexity and additional learning burden were also identified. The paper concludes with recommendations for the effective integration of MATLAB into the curriculum, emphasising its role as an aid to traditional physics teaching. [ABSTRACT FROM AUTHOR]

Personalized intelligent service system for the elderly has attracted much attention from the society. With the increasing aging problem, people pay more and more attention to how to use information technology to meet the needs of the elderly. Through the investigation and analysis of the needs of the elderly information family communication system, this paper presents the process of system design and implementation. The whole scheme design of family social system for the elderly based on information technology is proposed, and the function and data requirements of the system are deeply discussed. This paper focuses on the key technologies needed for the implementation of the system, including social network structure, user interest modeling and personalized recommendation technology, as well as the principles of human-computer interaction design for the elderly. The design and implementation process of the prototype system is presented, and the function modules and performance of the system are evaluated and improved. Through these work, the aim is to provide better intelligent and personalized services for the elderly, and promote the solution of the aging problem and the improvement of the quality of life of the elderly. [ABSTRACT FROM AUTHOR]

Under the background of digitalization and networking, the fashion industry is undergoing unprecedented changes. In such a big environment, online clothing design based on artificial intelligence technology is a brand-new innovation and development direction. In this paper, the online fashion design is taken as the research object, and the latest artificial intelligence algorithm is combined to discuss the fashion design automation and personalized design method based on AI(Artificial Intelligence). The online clothing design system based on artificial intelligence uses deep learning, image recognition and machine learning methods to analyze user preferences, historical data and fashion trends, and automatically generate personalized and innovative clothing design schemes. In addition, this study also compares the differences between artificial intelligence-driven clothing design and traditional clothing design in creativity, cost-effectiveness and user satisfaction. In the AI design method, the design cycle is generally shorter, in which the automatic primary, intermediate and advanced design takes 8 days, 6 days and 4 days respectively, while the expert-aided design and fully customized design cycle takes 5 days and 7 days respectively. The research results of this paper will promote the automation and personalization of fashion design, and open up a brand-new working mode for the development of fashion industry. [ABSTRACT FROM AUTHOR]

When recommendation algorithms are used in different languages and environments, the application of traditional recommendation algorithms will face many challenges. This study explores recommendation algorithms based on POI similarity and the matching method of translation machine to improve the accuracy and personalization of recommendations. This paper constructs a POI database including tourist attractions information, calculates the similarity between the attractions by similarity calculation methods, and utilizes translation machine technology to match the user language preferences with the attraction information. The recommendation algorithm designed by the paper is to use the results from POI similarity and translation machine matching to generate a personalized recommendation for tourist attractions. From the experimental evaluation, the recommendation algorithms based on POI similarity and translation machine matching have shown significant results in the recommendation, which can provide a more accurate and personalized recommendation results based on the user's interest and the language user prefers. [ABSTRACT FROM AUTHOR]

Education is very important for all individuals, including people with disabilities such as the deaf, to advance the nation and survive. In this case, effective communication of educational material is important to create a conducive learning environment. Most people also believe that deaf people can make a big contribution to society if they receive the right education, and this becomes a challenge for deaf teachers and students. Therefore, this paper aims to find a solution that can become a means of independent education for deaf people. This paper is using design thinking method to achieve the goal. The result of this research is the development of an educational mobile application specifically for the deaf, namely 'V-Voice'. This application provides various learning features, such as; animated videos, education games and texts that are attractively designed. Through 'V-Voice', it is hoped that deaf people can study harder independently and be helped to manage information and develop their soft skills and hard skills. [ABSTRACT FROM AUTHOR]

The development of the internet and social media means that people can easily convey various personal opinions or expressions. In line with this, many researchers are interested in examining the sentiments they make, considering that nowadays their opinions can be useful for various sectors such as product analysis, latest news analysis, or even political opinion analysis. Several approaches used to analyze public opinion on social media are Machine Learning and Deep Learning. This paper is devoted to finding the best AI-based models for sentiment analysis on social media especially those that are known for higher precision in complexity solving by using Systematic Literature Review (SLR). Of the many papers with the same topic interest, there are a total of 13 papers that meet our criteria which include title, methods, and results. Then, the papers are evaluated one by one in-depth and compared to find the best results. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attacks pose a significant threat to online services by flooding targets with unusually high volumes of traffic or data, disrupting services and causing financial and reputational damage. This paper explores the workings and impact of DDoS attacks, with a variety of methods used by attackers to exploit vulnerabilities in the target infrastructure. To address these risks, this paper advocates the application of Machine Learning (ML) techniques. ML allows computers to learn patterns from data and make decisions autonomously and offers a proactive defense against DDoS attacks. Using UNSW-NB15 Dataset, this study evaluates the performance of eight ML algorithms Logistic Regression, KNN, SVM, Random Forest, Decision Tree, LSTM, MLP, and GRU, the evaluation of this study is based on Accuracy, Precision, Recall, and F1 Score metrics. The results show that Random Forest is the most effective algorithm, showing superior performance across all metrics with 97.68% accuracy. This explains the potential of Machine Learning, specifically the Random Forest algorithm. can be considered to improve security and reduce the risk posed by DDoS attacks. [ABSTRACT FROM AUTHOR]

Smishing is a mobile device attack in which the attacker sends text messages with malicious URLs, phone numbers, or E-Mail IDs to the victim with the intent of stealing sensitive user data such as bank account details, passwords, user credentials, credit card details, and so on. The attacker instructs the user to click on the link or contact the phone number or email address supplied in the SMS. The expanding usage of mobile phones and their integration into numerous communication systems has raised cyberattack susceptibility. The paper focus on the impact of smishing attacks, which primarily target public announcement systems using mobile phones. Smishing, a misleading tactic that uses SMS communication, could interrupt important public communication channels, and jeopardise people's security and privacy. The purpose of this paper is to examine the nature of smishing attacks on public announcement systems and offer solutions to reduce the risks. The paper aims to give insights into attacker techniques, vulnerabilities in current systems, and the repercussions on public trust and safety by studying real-world smishing episodes and their effects on public announcement systems. The findings help to propose effective preventative measures and incident response techniques to protect public announcement systems against smishing attacks, improving the overall security and dependability of mobile-based communication networks in public domains. [ABSTRACT FROM AUTHOR]

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study. [ABSTRACT FROM AUTHOR]

Traffic accidents are a significant global problem, with human error being a major contributing factor. Autonomous vehicles have emerged as a promising solution to address this issue. Vehicle pedestrians understand the environment through sensor is crucial in this field because sensor data can be used by vehicle to drive safely such as navigate the route, avoid collision with other object such as pedestrian, car, building, animals, and many more. This paper proposed a comparison of 3D object detection models for autonomous vehicles using a study literature review technique. This technique consists of ten steps from including defining research question to produce the result. As a result, 9 papers were retrieved from 90 papers and then all object detection models are grouped into three parts to determine the best model based on dataset that being used such as KITTI, nuScenes, and Waymo Open Dataset. [ABSTRACT FROM AUTHOR]