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]

Spam detection in SMS communication is a crucial task for maintaining the quality of messaging services and protecting users from unwanted and potentially harmful messages. Arabic SMS spam detection poses unique challenges due to the rich morphology and complex structure of the Arabic language, which can significantly impact the performance of traditional text classification methods. To address these challenges, this paper presents a novel approach for Arabic SMS spam detection using a hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. The proposed model leverages the strengths of CNNs in capturing local features and patterns in the text and the capability of Bi-LSTM networks to understand long-term dependencies and contextual information. This hybrid architecture is designed to effectively handle the complexities of the Arabic language and improve the accuracy of spam detection. The model was evaluated on a dataset of Arabic SMS messages, consisting of both ham (non-spam) and spam messages. The dataset underwent preprocessing steps, including text cleaning, tokenization, and padding, to prepare it for training the deep learning model. The hybrid model was trained using the Adam optimizer and evaluated using accuracy, precision, recall, and F1 score metrics. Early stopping was implemented to prevent overfitting during the training process. The results demonstrate that the hybrid model achieved high performance, with an accuracy of 0.9699, precision of 0.9739, recall of 0.9675, and an F1 score of 0.9707. These metrics indicate the model's effectiveness in accurately detecting spam in Arabic SMS messages. Additionally, the paper provides visualizations of the confusion matrix, ROC curve, and training-validation loss graph to illustrate the model's performance. The implications of this research are significant for the field of Arabic text classification and spam detection. The proposed hybrid model offers a robust solution for accurately classifying Arabic SMS messages, which can be integrated into messaging platforms to enhance spam detection capabilities and improve user experience. Future work could explore data augmentation techniques, transfer learning, and advanced hybrid architectures to further enhance the model's performance and applicability. [ABSTRACT FROM AUTHOR]

For the problem that the communication between maritime devices may be listened to by the adversary, resulting in the data shared between maritime devices may also be tampered with and forged by the adversary, thus jeopardizing the maritime IoT, this paper firstly starts from the wireless sensor network technology which is the most widely used in the maritime IoT, and then selects and designs a lightweight block cipher algorithm for realizing the secure data sharing of the multi-node devices in the maritime IoT. After analyzing the security of the algorithm, it is concluded that this paper's scheme performs well in terms of anti-differential analysis and linear analysis, and is better than PRESENT algorithm in terms of hardware implementation. [ABSTRACT FROM AUTHOR]

In order to achieve intelligent recognition and auxiliary scoring of subjective questions in paper-based exams, as well as timely detection of various abnormal test papers, assist manual grading and quality inspection, and improve the quality of grading, the application of artificial intelligence in education exam evaluation has been studied. The author focuses on the technological progress of artificial intelligence in educational exam evaluation, introduces the application results of artificial intelligence technology in assisting artificial intelligence in exam marking, and explains the effectiveness of using intelligent assisted scoring in various exam questions such as fill in the blank and essay. The experimental results showed that, compared with manual grading, the human-machine consistency rate for identifying multiple gaps in blank filling questions exceeded 94%, and the human-machine consistency rate calculated based on small questions also exceeded 97%. This result indicates that the machine evaluation results can serve as an auxiliary means for quality inspection and grading, and their effectiveness is reliable. In addition, in terms of subjective question evaluation, there is a high correlation between machine evaluation results and manually rated final report scores, both of which are greater than 0.7. The results indicate that their scores can be regarded as third-party manual scoring, which can be used for scoring quality checks and other aspects, playing a good role in improving scoring effectiveness. Keywords [ABSTRACT FROM AUTHOR]

At present, network education, which takes computer network and technology as the means of education transmission and interaction, has become an important part of modern distance education, reflecting the characteristics of networking, personalization, interactivity and comprehensiveness. This paper studies the technical standards of network teaching platform, realizes the standardization of network teaching platform system, then analyzes several existing distance education teaching modes in China, and designs the function of network teaching platform system. In this paper, through the form of questionnaire survey to understand the specific implementation of the design and implementation of the network teaching platform, in the two user groups of teachers and students, the questionnaire survey analysis results. The results show that both teachers and students can't refuse the convenience of the online teaching platform. The teachers are satisfied with the convenience of the online teaching platform, and only 5.8% of the students have a general sense of the online teaching platform. [ABSTRACT FROM AUTHOR]

This paper begins by analyzing the effect of market economic fluctuations on the digital economy's tourism economy. Second, this paper uses a city cluster in the Yangtze River Delta in China as an example, selects the tourism economic data of the region from 2011 to 2021, and forecasts the tourism development potential of the region from 2022 to 2026 using the conventional methods for predicting economic fluctuations. However, conventional models for predicting economic fluctuations, such as ARIMA, have large prediction errors. This paper combines the RBF neural network and exponential smoothing method and compares them to GM(1, 1), ARIMA, and other models. The findings indicate that the tourism economy in this region is spatially distinct and relatively stable. Therefore, the methodology presented in this paper is capable of accurately predicting tourism demand and growth potential. [ABSTRACT FROM AUTHOR]

This paper puts forward a new mental health evaluation model based on data mining technology, and analyzes the factors that affect mental health. The model understands the influencing factors according to the mental health status of different student groups, then reduces the dimensionality of the original data through principal component analysis and discriminant analysis, and finally establishes a dependent variable prediction model through regression analysis. The experimental results show that the method proposed in this paper can effectively predict students' mental health status. [ABSTRACT FROM AUTHOR]

The problem of resource shortage and environmental pollution is increasing, and the tomobile industry, as one of the heavily polluting industries, should actively practice low-carbon development actions. This paper takes BYD Company as an example. Byd Company is actively transforming from a traditional high-energy-consuming enterprise to a new energy enterprise. This paper uses EVA model to analyze the performance indicators after the transformation, so as to evaluate the value created by BYD's transformation and put forward relevant suggestions. [ABSTRACT FROM AUTHOR]

The existing forensics systems are developing in a more and more perfect direction in terms of forensic steps, legal constraints, audit supervision, etc., but they are still not fully applicable to legal forensics. A general legal forensics system must have a complete forensics process to ensure the smooth progress of the forensics work. One of the purposes of legal forensics is to use legal means to bring criminals to justice. Only by ensuring the legality of the evidence can it be accurately identified case, and therefore also to ensure the legality of the evidence collection process. In order to design a more powerful legal forensics system, this paper introduces a data mining algorithm to extract valuable information from massive data through association rules to improve the efficiency of forensics. At the same time, this paper also proposes three coding schemes for the integrity of the evidence object, and compares the forensic effects of the three schemes. [ABSTRACT FROM AUTHOR]

In order to improve the modeling efficiency and ensure the authenticity of the model, this paper proposes a 3D facial animation generation method based on front and side images. First, the feature point information reflecting the shape characteristics of the face is extracted from the front and side photos of a given face, and then these feature point information is used to modify the general face model to obtain a specific face model. Then, the face texture information in the front and side photos is used to texture map the specific face model, and finally a realistic virtual 3D face animation is obtained. In the aspect of feature extraction, this paper uses an improved color Snake model to accurately locate contour lines. The experimental results show that the system still has good processing performance, and the average processing time is less than 0.1s. On the set V, the light source angle is greater than 78 °, the illumination is seriously uneven, and the recognition rate is very low, which indicates that the texture features are not well extracted. But even in this case, the recognition rate after light compensation has doubled, which proves that the two-dimensional image can realize the three-dimensional facial animation generation technology. [ABSTRACT FROM AUTHOR]

By combining machine learning AI model with traditional credit risk measurement model, this paper aims to more accurately measure the credit default risk of real estate enterprises. Through professional credit management knowledge, the integration of these two models can fill the gaps of traditional models outside of financial data, and use the research experience of traditional models in the financial field to provide guidance for machine learning. This paper focuses on the key issues of capital intensity, exploration of Supply Chain Finance Growth and Credit Risk Control within the Real Estate Sector. By combining qualitative and quantitative methods. Through literature research and empirical analysis, this paper develops a supply chain finance credit risk assessment index system tailored for small and medium-sized enterprises operating in China's real estate sector. The index system comprises 23 risk assessment indicators, and comprehensively considers many factors such as corporate financial status and market environment. Then, a credit risk assessment model for small and medium-sized real estate enterprises is developed from a supply chain finance perspective, utilizing genetic algorithm optimization for Support Vector Machine (GA-SVM)., and the advantages of this model compared with the traditional model in accuracy and generalization ability are verified through comparative analysis of experiments. The research results can help reduce the information asymmetry in the real estate industry, enhance the credit rating evaluation process for small and medium-sized enterprises, and furnish references to aid commercial banks in credit and credit risk management within the real estate supply chain finance sector.which has important theoretical and practical value. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet finance, the problem of financial fraud has become increasingly prominent, which has brought severe challenges to the security and stability of the financial industry. This paper aims to use artificial intelligence (AI) technology to empower data mining models to build an efficient financial fraud detection and prevention system. First of all, through the analysis of the development trend of Internet finance and the current situation of financial fraud, the limitations of traditional prevention and control measures in the face of big data and complex fraud modes are revealed. Secondly, the paper introduces the application prospect of AI technology in the financial field, especially the advantages and potential in data mining and fraud detection. Then, the data mining techniques adopted are discussed in detail, including the application of machine learning-based model recognition and artificial intelligence algorithms, as well as data preprocessing and feature engineering in big data environment. Further, the paper describes the system architecture design and key technology implementation, including model training and optimization, real-time monitoring and early warning solutions. Finally, through the in-depth analysis and evaluation of the experimental results, the paper verifies the remarkable effect and feasibility of the system in financial fraud detection and prevention, and provides a new technical guarantee and risk management strategy for the financial industry. The experimental results show that the model has achieved good results in the actual data in the financial field, and provides effective technical guarantee and support for financial security. [ABSTRACT FROM AUTHOR]

This paper introduces a kind of intelligent lubricating oil quality detector integrating computer technology. On the basis of the traditional lubricating oil quality detection, through the introduction of advanced computer technology, the equipment realizes the automation of the detection process, the intelligence of data processing and the convenience of remote monitoring and management. This paper not only expounds the system structure and working principle of the detector, but also discusses the application of computer technology in the detection process. [ABSTRACT FROM AUTHOR]

The safety and durability of concrete structure is an important issue in engineering quality management. In this paper, an image processing algorithm based on deep learning is proposed to realize real-time quality inspection and automatic defect identification of concrete structures. This algorithm uses the Convolutional Neural Network (CNN) to automatically extract the features of concrete surface quality images, and then identify the existence of defects, thus improving the detection efficiency and accuracy. In this paper, for this method, data samples with different specific structures are collected and manually labeled to the data set; then, a multi-layer CNN model with convolution layer, pool layer and full connection layer is designed to train the model, and then image enhancement technology is used to reduce information noise, and data enhancement technology is used to improve the problem-solving ability of the model. In addition, the strategy of Dropout is used to close some nodes to reduce parameters and prevent over-fitting, and the learning rate is adjusted to optimize the classification effect. In addition, this study constructs an all-weather real-time detection framework, including data acquisition, preprocessing, feature extraction, classification and identification and decision-making alarm system, to ensure the rapid positioning of the detection system. To sum up, the results of this study show that the deep learning image processing algorithm has good contrast performance in the field of real-time quality inspection of concrete structures. CNN model has better performance than GAN (Generative Adversarial Network) and LSTM (Long Short-Term Memory) models in detection time, defect identification resolution and detection accuracy. The maximum detection time is 366ms and the shortest is 213 ms. The successful development of this algorithm provides a new method for automatic detection of concrete structure quality, which has important application value in engineering practice. This research has a broad development prospect. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology, it has become convenient for people to work, study and travel and face recognition is widely used in many fields, but also brings a lot of hidden dangers to security. Aiming at the problem of reduced accuracy of face detection algorithm caused by local occlusion factors, The paper proposes a partial occlusion face recognition method based on DCGAN face repair combined with ResNet. The experimental results show that the algorithm proposed in the paper has better face recognition effect when there is partial occlusion in the face. [ABSTRACT FROM AUTHOR]

In this paper, we construct an enterprise economic management sharing platform based on the feature recognition grid system, firstly, we use the fuzzy matching recognition algorithm to extract the grid features of economic management and map them into the fuzzy set of affiliation features, and then we extract the feature vectors from each grid to get the feature matrix, and finally, according to the similarity degree of the feature sequences, we set a threshold to give the recognition result. The results show that the recognition rate of the platform constructed in this paper reaches more than 99%, the classification accuracy is about 90% on average, and the optimization time is only about 19 s. It is verified that the method of this paper improves the accuracy of enterprise economic management, and has a positive role in promoting the healthy development of enterprises. [ABSTRACT FROM AUTHOR]

The importance of enterprise credit risk management is increasingly prominent. Under the background of financial technology and big data, this paper studies a model combining Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) by comprehensively analyzing multi-dimensional data such as corporate financial statements, credit scores and transaction records, so as to capture and learn the complex characteristics of credit risk of Internet financial enterprises. Firstly, this paper collects the relevant data of Internet finance enterprises from multiple data sources, and carries out standardization processing and missing value filling. Then, CNN-LSTM model is constructed based on these data, and model training and hyperparametric optimization are carried out by adjusting convolution layer and LSTM layer. In addition, this study also designed a comparative experiment to evaluate the performance of CNN-LSTM model and CNN and LSTM models alone in prediction time, prediction accuracy and interpretability. The results show that CNN-LSTM model has significant advantages in predicting the credit risk of Internet finance enterprises. The model also shows a faster response speed, and the maximum warning time is only 701ms, which is much lower than the LSTM and CNN models. The highest accuracy of the model is 94.1%, which is significantly higher than that of LSTM and CNN models. In addition, the model also has high confidence in interpretability, which provides a solid basis for financial decision-making. [ABSTRACT FROM AUTHOR]

The large deformation of soft rock tunnel is an important problem to be solved urgently with the development of engineering technology. In order to improve the application effect of prestressed anchor cable adaptive active support system, this paper puts forward an optimization method based on adaptive genetic algorithm, reviews the research status of prestressed anchor support in soft rock tunnel, and analyzes and evaluates the existing methods. This paper introduces the construction process of AGA model, establishes the problem description model and genetic optimization algorithm selection model, and constructs the adaptive active support model of prestressed anchor cable. The model adopts an optimization algorithm with a total of 100 iterations. The Simple Genetic Algorithm and Parallel Genetic Algorithm are selected to evaluate the anchoring force, the control effect of surrounding rock displacement, the erosion of supporting structure and the construction time by comparative test. The test results show that AGA has a good effect on the anchoring force, the minimum anchoring force is 457 kN, and the displacement of surrounding rock is controlled within 1.21-1.59mm, which has a good influence on the quality of supporting structure and the construction completion time. Finally, the application of AGA model in parameter optimization of prestressed anchor cable in soft rock tunnel is summarized, and the future research direction is put forward. [ABSTRACT FROM AUTHOR]

This paper explores the application of artificial intelligence technology in ensuring the security of digital currencies. With the increasing popularity of digital currencies, ensuring their security has become a crucial concern. AI techniques, such as machine learning and deep learning, can be employed to identify and prevent security threats in digital currency systems. Additionally, it investigates how AI enhances security measures and risk control on digital currency trading platforms, offering insights into real-time risk alert systems. Therefore, through empirical research in four distinct sections, this paper continuously refines and revises the theoretical model proposed in this project. The ultimate goal is to provide new theoretical tools for the management of financial stability in the process of Digital Currency replacing M0. this project gives the research content of the four parts, which include public perception, delivery path, underlying technology and virtual currency bubble, we hope that this research can offer valuable insights to relevant field managers. [ABSTRACT FROM AUTHOR]

The intelligent warehousing control system is a system based on PLC (Programmable Logic Controller) and human-computer interaction (HCI), aiming to improve the efficiency and accuracy of warehousing operations. The system controls the operation and monitoring of storage equipment through PLC, and interacts with operators through human-computer. The system design includes equipment selection, PLC control system design, and human-computer design. In equipment selection, it is necessary to select equipment suitable for automation control to ensure that the system can meet the needs of warehousing operations. This paper introduces a programmable controller design method, that is, using soft and hard to achieve automatic control of memory. The hardware part is mainly composed of PLC, sensor, actuator and so on, which controls the operation of memory and monitors the relevant parameters. In the aspect of software, the related control program is designed by PLC programming language. The test results show that in the traditional warehouse system, the processing time of each commodity is more than 169 seconds; the utilization rate of warehouse is less than 70%, and the logistics operation time of the enterprise using PLC and HCI for intelligent storage control can be reduced to 57 seconds; on this basis, the data processing time of the system is reduced to 43 seconds, and the utilization rate of the warehouse is 93%. Therefore, this paper presents an intelligent warehouse control system based on PLC and man-machine interaction. The system can not only effectively improve the efficiency and accuracy of warehouse operation, but also enhance the safety and reliability of warehouse operation. It provides a reliable and flexible solution for automatic control and monitoring of warehouse. [ABSTRACT FROM AUTHOR]

The supply chain regards suppliers, producers, and consumers as an organic whole, unifying and coordinating the information flow, logistics, and capital flow of all members, and achieving the goal of win-win for all members in the overall operation of cross organization. Demand forecasting is an important factor driving the entire supply chain, and low error rates in forecasting are a common goal pursued by the industry. In order to improve the quality of demand forecasting, enhance the efficiency of supply chain operations, and leverage the important role of machine learning in the era of artificial intelligence, this paper conducts research based on LSTM. Firstly, this paper determines the objective function and constraints for supply chain demand forecasting; Then, this paper constructs a supply chain demand prediction model, based on the LSTM network structure, determine the network training method and model construction process; Finally, this paper conducts simulation experiments and result analysis, configure LSTM parameters, determine model performance evaluation indicators, and compare and analyze actual values with predicted values. The results indicate that the supply chain demand prediction model constructed in this article has very good performance and has promotional value in practice. [ABSTRACT FROM AUTHOR]

The traditional electrical control system has the problem of insufficient function control. This paper designs the engineering electrical control system through the learning algorithm based on fuzzy neural network. The control strategy design module combines fuzzy reasoning with neural network to design intelligent control strategy. Develop a variety of control principles and strategies that can meet the needs and characteristics of the system. The user interface module assists users to monitor and adjust the system, and has the functions of real-time data display and setting control parameters. Fuzzy neural network modeling module combines fuzzy logic with neural network to model the system. In this paper, the sliding window technology is used to collect and process data in real time. When the running time is 1 hour, the system output value is 100.2 and the output fluctuation value is 0.6; When the running time is 2 hours, the system output value is 99.8 and the output fluctuation value is 0.7. The learning algorithm based on fuzzy neural network has little fluctuation and stable performance. [ABSTRACT FROM AUTHOR]

This paper compares and analyzes the present situation and the key technology of the research on the impact loading of the automobile seat at home and abroad, and analyzes the concrete problems existing in the research of the automobile seat in our country at present, this paper puts forward the new principle of impact loading of automobile seat, explains the loading process of impact loading principle in detail, constructs the mathematical model of loading principle and simulates and analyzes the loadability, the hydraulic system of the impact loading system of the automobile seat is designed in detail, and the hydraulic loading process of the automobile seat is refined, the simulation and test of the integrated performance of the vehicle seat impact loading system are carried out, which validates the feasibility of the design and lays a solid foundation for the next development of the entity. [ABSTRACT FROM AUTHOR]

The unstoppable trend of globalization has made the integration of voice recognition with science and technology increasingly close, and has called for the emergence of more powerful oral voice recognition systems. At present, most speech recognition research uses Hidden Markov Model-Gaussian Mixture Modell (HMM-GMM) and onnectionist Temporal Classification (CTC) end-to-end method, which has obvious limitations. Therefore, based on neural network, this paper introduces deep neural network posterior probability algorithm into speech recognition system, and constructs the spoken speech recognition system based on deep neural network posterior probability algorithm. The design principle of the constructed speech system is discussed and the performance analysis and effectiveness evaluation are carried out. The experimental results show that the performance of the neural network-based speech recognition system constructed in this paper has made a breakthrough compared with the traditional technology, and the Character Error Rate (CER) is only 4.6%, and the End-to-End Latency and Equal Error Rate (EER) have reached a low level. This study points out the future development direction of speech recognition and provides the impetus to promote its further development [ABSTRACT FROM AUTHOR]

Landscape design has a long history, from ancient royal gardens to modern urban parks, and its design concepts and techniques are constantly evolving. Nowadays, with the improvement of people's environmental requirements, landscape design is also constantly innovating. Although the landscape has made great progress, in actual operation, there are problems such as communication barriers with customers, difficulty in design implementation, and inability to preview the design effect in real time. Computer augmented reality technology has begun to play an important role in landscape design. Augmented Reality (AR) overlays the virtual 3D model into the real scene, previews the effect of design implementation in real time, carries out data analysis and optimization, and improves the design better to meet the needs of the public. In order to scientifically evaluate the effect of computer augmented reality technology in landscape design, this paper sets two main indicators: efficiency and quality. Efficiency is primarily concerned with the speed difference compared to traditional design methods. By means of comparative experiments, the time required to use augmented reality technology is calculated. Quality is mainly based on user feedback and expert review. Through users' actual experience of landscape design under augmented reality technology, their feedback is collected, and combined with professional review, the performance of augmented reality technology in design quality is comprehensively evaluated. This paper investigates the evaluation results of people in four landscapes (green plants, pavilions, ancient buildings, fountains). The survey results show that the fountain design quality is 80 points, the efficiency score is consistent with the pavilion, the feasibility score exceeds 80 points (82), and the user satisfaction is 5 points higher than the pavilion design. [ABSTRACT FROM AUTHOR]

Machine learning algorithm is one of the most popular research fields in the 21st century, and it is widely used in various industries. This paper first discusses the development status, characteristics and intelligent classification technology of e-commerce based on various methods. Then, through statistical analysis of multiple samples, this paper establishes a model to predict the quantity of goods and other time-related changes in the future period of time, and proposes an improved variance estimate to achieve the best effect under different data scenarios. Finally, the experiment proves that the response time of the linear regression algorithm fluctuates between 20ms and 25ms, and the overall fluctuation is small. For different samples, the response time of the linear regression algorithm is maintained in a relatively stable range, showing the stability of different input data. The response time of the decision tree algorithm fluctuates between 28ms and 33ms, showing a certain fluctuation. For different samples, the response time of decision tree algorithm fluctuates greatly, which may be affected by the characteristics of input data and show different processing efficiency. The response time of support vector machine algorithm fluctuates between 14ms and 18ms, and the change is relatively stable. For different samples, the response time of SVM algorithm is kept at a relatively stable level, showing the consistency of input data processing ability. [ABSTRACT FROM AUTHOR]

Machine vision is based on computer, through the comprehensive utilization of a series of advanced theoretical knowledge such as image acquisition equipment, digital processing technology and related algorithms. It can quickly and accurately extract the required features from the input data and describe them, which plays an important role in modern industrial production. Machine vision can intuitively express the image information that people are exposed to in a certain way, and process and process it accurately and quickly. This paper aims to use machine vision to quickly obtain spatial position, size and other data, combined with artificial intelligence genetic algorithm, particle swarm algorithm, intelligent plane optimization. This paper mainly uses the experimental simulation method and the comparison method to study the optimization method of the system layout, puts forward two improvement schemes of the storage system layout, and makes a comparative analysis of their space loss parameters. The experimental results show that in terms of total space loss, the value of improved scheme 1 is 0.741, which is higher than that of improved scheme 2. [ABSTRACT FROM AUTHOR]

The prosperity of e-commerce has made our lives more convenient, but the logistics behind it has been silently paying. The e-commerce industry is expanding at an unprecedented rate, and with it comes explosive growth in logistics demand, especially in the warehouse distribution link. In order to meet this demand, the warehouse distribution system needs to be improved both in terms of technology and efficiency. This rapid development also brings a series of problems. First of all is the problem of logistics congestion. Due to the surge in orders, especially during peak shopping periods, logistics centers are often under great pressure. This not only affects the speed of delivery, but can also lead to damage or loss of goods. The purpose of this paper is to find an effective method to optimize the logistics warehouse allocation system of e-commerce, and to provide an efficient and reliable logistics optimization scheme for e-commerce enterprises. In this paper, data analysis and simulation methods are used to analyze. The experimental results show that PSO has the largest improvement in the order clerk operation efficiency (13%) and the smallest improvement in the order status (1%). [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, the traditional information management system faces the challenge of efficiency and accuracy when dealing with complex data and decision-making problems. In order to solve these problems, this paper integrates the random leapfrog band selection algorithm into the information management system. This algorithm shows excellent performance in dealing with large-scale, nonlinear and multi-variable optimization problems, resource allocation, scheduling optimization and data mining. This paper mainly explains the design framework of information management system, the optimization of information management by random leap-band selection algorithm and the design of information management system. Finally, two groups of simulation experiment results are as follows: the accuracy of the optimized information management system is 96%, the running time is 7.1min, the algorithm performance is better than the traditional system, and the response time of the system is reduced by 195ms. This research provides an efficient, flexible and scalable solution for dealing with various complex information management system optimization problems. [ABSTRACT FROM AUTHOR]

Hive can store massive data through extended clusters, far exceeding the expansion and storage capabilities of traditional databases, and has become a major tool for building data warehouses in the era of big data. Based on Hive technology, this paper studies the enterprise-level data warehouse architecture composed of data storage layer, Hive data warehouse layer and application layer. Then, the paper studies data warehouse tools, uses HDFS for underlying storage and MapReduce for computing engine, and compares Hive data warehouse with relational database. Then, the ETL process is studied. The data goes through three stages: extraction, conversion and loading, and finally the data flows from the source to the target end. Finally, the system function and test are studied, the subsystems including data processing, data management, data migration and data analysis are constructed, and the core functions are tested. The test results were consistent with the expected results and met the delivery standards. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic grid integration brings many negative effects to the distribution network, and carrying out the research on its consumption capacity (CC) in the distribution network is the key to guarantee the safe and stable operation of the distribution network. This paper takes the maximum power access mode and minimum network loss as the optimization objectives, and takes voltage deviation, voltage fluctuation and short circuit capacity as the constraints to construct the evaluation model of distributed PV power generation CC. In view of the above problems, this paper uses edge computing to construct the evaluation of new energy (NE) CC of the grid, and applies it to the IEEE33 node network and the actual network. Finally, the established mathematical model and algorithm are validated (relative to Monte Carlo, the evaluation time of edge computing and time series production simulation is shorter, 13.11s and 23s, respectively). [ABSTRACT FROM AUTHOR]

The paper is dedicated to the security measures within the concept of MetaHospital. The idea of MetaHospital is an answer to the modern challenges of time where interaction through digital media is no longer a fantasy. In this paper authors describe the idea of MetaHospital and its modules as well as the ways of personal data protection. The data protection model is given and with a detailed description of each stage. Through the incorporation of rigorous data security protocols, MetaHospital guarantees the safeguarding of patient information, cultivating a trustworthy environment for medical care and research endeavours. Committed to progress and patient well-being, MetaHospital persistently refines its practices, leveraging technology and data-driven perspectives to enhance healthcare delivery and optimize patient outcomes. [ABSTRACT FROM AUTHOR]

With the increasing functionality of intelligent programmable controllers and the continuous improvement in power and component density, the problem of rapidly rising temperatures during operation has become increasingly prominent, directly affecting the controller's lifespan and stability[1]. Therefore, improving the heat dissipation efficiency of intelligent programmable controller modules has become a key focus of research. This paper introduces a method of installing aluminum alloy heat sinks on the core chip of the controller and carefully designing the fin shape and size of the heat sink to enhance heat conduction and heat convection effects, improving heat dissipation performance. This paper also constructs a thermal equivalent model of the controller and simulates the impact of different fin parameters on heat dissipation performance. Experimental results show that when the fin height is 5 millimeters, thickness is 2 millimeters, and spacing is 4 millimeters, the heat sink exhibits the highest thermal conductivity and the lowest thermal resistance, achieving optimal heat dissipation effectiveness. [ABSTRACT FROM AUTHOR]

The rotational speed of the hydraulic turbine generator set will change with the load change of the power system, therefore, the speed control system of the unit needs to adjust its operating parameters in time according to the load change. The conventional PID control algorithm is widely used in the current hydraulic turbine speed control system, but it is unable to self-tune the parameters, which makes it difficult to achieve the desired control effect. In order to solve the above problems, this paper combines the neural network and fuzzy control to optimize the PID control parameters, and through the Matlab simulation experiments, it is verified that the control described in this paper can make the hydraulic turbine speed control system have satisfactory control effects in different operating conditions. [ABSTRACT FROM AUTHOR]

This paper examines the pervasive barriers hindering the widespread adoption of energy efficiency measures across various sectors. It categorizes these barriers into economic barriers, high upfront costs, organizational challenges, such as technical expertise and behavioral barriers, namely risk aversion and framing. To address these barriers, a comprehensive review of energy efficiency policies is conducted. These policies include incentives, coercive instruments, award systems, university industry collaboration, and technical support. To review energy efficiency barriers and policies, a methodological approach integrating non-structured snowball sampling with targeted literature review techniques was devised. Finally, the paper underlines that the success of these policies is contingent upon the active involvement, the collaboration, and the feedback of businesses, ensuring the feasibility of these policies. Their proactive engagement is indispensable in tackling energy efficiency barriers and driving the implementation of energy-efficient technologies, thus paving the way for a more sustainable and competitive businesses. [ABSTRACT FROM AUTHOR]

This paper examines the role of energy efficiency in enhancing corporate sustainability and competitiveness. Employing a systematic literature review methodology, we analyze the financial advantages, corporate sustainability alignment, and crisis resilience associated with energy efficiency measures in business operations. The results highlight the significant cost savings, improved profitability, and enhanced competitiveness resulting from energy-efficient practices. Furthermore, we elucidate how energy efficiency initiatives align with broader corporate sustainability objectives, reduce environmental impact, and promote corporate social responsibility. Additionally, the paper discusses how energy efficiency enhances organizational resilience by bolstering preparedness for crises and promoting long-term sustainability and adaptability. The main implications of our findings underscore the importance of prioritizing energy efficiency as a strategic imperative for businesses seeking to achieve sustainability goals while maintaining competitiveness in a rapidly evolving global landscape. [ABSTRACT FROM AUTHOR]

The county-level administrative unit serves as the fundamental building block of the national structure, yet there exists a dearth of comprehensive research on its influence on innovation development. Thus, drawing from a Structural perspective, this paper employs fuzzy-set qualitative comparative analysis to delve into the pivotal factors and conditions shaping county-level innovative development level across different periods from 2006 to 2020. The findings unveil: (1) The advancement of innovation and development at the county level necessitates the amalgamation of various factors. While the structures of different factors are not indispensable for fostering innovative development level. (2) County-level innovation and high development manifest in five configurational modes, classified as 'human-industry', 'human-industry-information', 'industry-finance', 'human capital' and 'information-based' types. (3) Across multi-period comparative analysis, the 'human-industry' configuration consistently emerges, indicating its significant impact on the county-level innovative development level. The trajectory of this configuration's evolution represents the 'dominant trajectory'. (4) The interaction pattern between human capital and industrial structure remains relatively stable throughout the evolution process, while human capital structure and information infrastructure exhibit a complementary relationship. This paper delves into potential avenues for enhancing county-level innovation development, offering fresh insights on bolstering innovation capabilities at the county level. [ABSTRACT FROM AUTHOR]

As an important part of agricultural products, the stability and reliability of the grain supply chain plays a crucial role in ensuring the smooth operation of the national economy and the harmony and stability of the society. The purpose of this paper is to conduct a comprehensive and in-depth assessment of grain supply chain risks from a multi-dimensional perspective. This paper follows the principle of indicator selection to construct a set of perfect evaluation index system. Based on the sample data from 2008 to 2022, the entropy weight TOPSIS model is used to obtain weights and assessment results, and further application of gray correlation analysis for risk dimension analysis. The following conclusions are finally drawn: the overall decreasing trend of China's grain supply chain risk is more significant. In addition, this paper finds that planting, operation and delivery risks are the main dimensions affecting the stability of the grain supply chain. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy sets, Pythagorean fuzzy sets and generalized orthogonal fuzzy sets are widely used in some multi-attribute decision-making problems. The Simple Fermatean fuzzy TOPSIS method addresses that decision makers are completely rational while making decisions, and applies the closeness index to the measurement to rank the alternatives. Prospect theory considers the irrational behavior of the decision maker in the actual decision-making process, decision makers facing the loss are risk-preferring (introducing the coefficient θ), and are risk averse when facing the gain. In this paper, prospect theory and Fermatean fuzzy sets are combined to explore the multi-attribute decision-making problem, and the Fermatean fuzzy TOPSIS method based on prospect theory is proposed. The comprehensive gain/loss ratio is calculated to rank the alternatives. In this paper, eight leading enterprises in the concept of emerging industries are used as examples to illustrate the effectiveness of the method. The results show that the optimal choice is Ganneng Co in the practical example, and the discriminant mean of the absolute value of φ +(S i)/ φ -(S i) obtained by pooling expert fuzzy evaluations using the Fermatean fuzzy TOPSIS method based on prospect theory is greater than the discriminant mean of the closeness index obtained by using the Fermatean fuzzy TOPSIS method, which can be interpreted as the former being a better synthesis of the expert fuzzy evaluations. In the future, the Fermatean fuzzy TOPSIS method based on prospect theory has a wide range of application value in several fields, which can be used to solve complex decision-making problems and improve the accuracy and effectiveness of decision-making. [ABSTRACT FROM AUTHOR]

In current academic research and industry practice of data trading, the effectiveness of market mechanisms is often limited. This paper proposes a data trading mechanism based on data securitization. The mechanism separates data ownership from usage rights and securitizes the ownership, implementing pricing and matching transactions through a two-stage market mechanism involving concentrated pricing and free trading. Essentially, the trading mechanism constructed in this paper reshapes the pricing scenario in the price space formed between users and holders, reflecting the value perception of data by buyers, sellers, and the market in data prices. The paper utilizes reinforcement learning methods to simulate and validate the proposed mechanism, providing preliminary evidence of its practical operability and effectiveness. [ABSTRACT FROM AUTHOR]

The manufacturing industry serves as the mainstay of the modern economic system and enhancing the resilience of the manufacturing supply chain (SC) is crucial in the face of global instability. Numerous research frameworks have been devised to evaluate supply chain resilience（SCR）. However, these frameworks primarily concentrate on the internal SC performance, such as supplier on-time delivery rate and inventory turnover. This paper establishes a four-dimensional evaluation index system for assessing China's manufacturing SCR at the macro level, including risk-resistance, recovery, responsiveness, and learning capabilities. The Self-Organizing Map (SOM) model is used for clustering analysis. Then the empirical analysis is conducted by using data from Chinese manufacturing listed companies between 2011 and 2020. The findings reveal that: ①The overall resilience of China's manufacturing SC is suboptimal: only 3.23% exhibit a superior resilience level, followed by a high resilience level of 6.45%, a medium resilience level of 54.84%, and the low resilience level reaching 35.48%. ②Significant regional disparities exist in the resilience of China's manufacturing SC. This paper proposes several recommendations for enhancing China's manufacturing SCR. [ABSTRACT FROM AUTHOR]