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The application of building extraction involves a wide range of fields, including urban planning, land use analysis and change detection. It is difficult to determine whether each pixel is a building or not because of the large difference within the building category. Therefore, automatic building extraction from aerial images is still a challenging research topic. Although deep convolutional networks have many advantages, the networks used for image-level classification cannot be directly used for pixel-level building extraction tasks. This is caused by successive steps larger than one in the pooling or convolution layer. These operations will reduce the spatial resolution of feature maps. Therefore, the spatial resolution of the output feature map is no longer consistent with that of the input, which cannot meet the task requirements of pixellevel building extraction. In this paper, we propose a encoder-decoder structure based on conditional random field for building extraction in remote sensing images. The problem of boundary information lost by unitary potential energy in traditional conditional random field is solved through multi-scale building information. It also preserves the local structure information. The network consists of two parts: encoder sub-network and decoder sub-network. The encoder sub-network compresses the spatial resolution of the input image to complete the feature extraction. The decoder sub-network improves the spatial resolution from features and completes building extraction. Experimental results show that the proposed framework is superior to other comparison methods in terms of the accuracy on open data sets, and can extract building information in complex scenes well. [ABSTRACT FROM AUTHOR]

INTRODUCTION: With the development of the epidemic, online teaching has gradually become a hot topic. However, unlike traditional teaching programs, there are many types of physical education resources, and the recommendation of related content has always been a difficulty in online teaching. OBJECTIVES: Therefore, this paper designs an accurate recommendation algorithm for online video teaching resources of sports to meet the personalized needs of online learning of sports majors. The data layer of the entire recommendation algorithm stores the video in the database and transmits it to the service processing layer after receiving the data. METHODS: This study was conducted using techniques from social network analysis. After receiving the data, the data layer of the recommendation algorithm stores the video in the database and transmits it to the business processing layer at the same time. The business processing layer uses the designed collaborative filtering resource recommendation algorithm to formulate recommendation results for different users, and push the recommended results to the user display interface of the user layer. RESULTS: The test results of the algorithm show that the designed system has a high recommendation success rate, and the system can still maintain stable running performance when the concurrent users are 500. The average precision of resource recommendation of this method is 98.21%, the average recall rate is 98.35%, and the average F1 value is 95.37%. CONCLUSION: The proposed resource recommendation algorithm realizes accurate recommendation of sports online video teaching resources through efficient recommendation algorithms. [ABSTRACT FROM AUTHOR]

Promoted by the rapid development of information technology, 10kV overhead line has been widely used in the majority of cities, and it is of great significance to monitor the distribution network effectively, in order to ensure the normal operation of the system. Most of traditional distribution network monitoring methods are based on manual work, which causes inconvenience to the distribution network fault location, repair, maintenance and real-time monitoring, and reduces the efficiency of the distribution network emergency repair and the reliability of power supply. Aiming at the automatic monitoring problem of 10kV overhead network, this paper adopts an intelligent wireless monitoring technology, where a monitoring node is employed to monitor the network transmission status through wireless links. We evaluate the system monitoring performance by using the metric of outage probability, depending on the wireless data rate over wireless channels. For the considered system, we derive analytical outage probability, in order to measure the system performance in the whole range of signal-to-noise ratio (SNR). The simulation results are finally presented to verify the analytical expressions on the system monitoring outage probability in this paper. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In the online English learning platform, noise interference makes people unable to hear the content of English teaching clearly, which leads to a great reduction in the efficiency of English learning. In order to improve the voice quality of online English learning platform, the speech enhancement method of the online English learning platform based on deep neural network is studied. OBJECTIVES: This paper proposes a deep neural network-based speech enhancement method for online English learning platform in order to obtain more desirable results in the application of speech quality optimization. METHODS: The optimized VMD (Variable Modal Decomposition) algorithm is combined with the Moth-flame optimization algorithm to find the optimal solution to obtain the optimal value of the decomposition mode number and the penalty factor of the variational modal decomposition algorithm, and then the optimized variational modal decomposition algorithm is used to filter the noise information in the speech signal; Through the network speech enhancement method based on deep neural network learning, the denoised speech signal is taken as the enhancement target to achieve speech enhancement. RESULTS: The research results show that the method not only has significant denoising ability for speech signal, but also after this method is used, PESQ value of speech quality perception evaluation of speech signal is greater than 4.0dB, the spectral features are prominent, and the speech quality is improved. CONCLUSION: Through experiments from three perspectives: speech signal denoising, speech quality enhancement and speech spectrum information, the usability of the method in this paper is confirmed. [ABSTRACT FROM AUTHOR]

Traditional Convolutional Neural Network (CNN) ignores the contextual semantics information when performing emotion analysis tasks. And CNN will lose a lot of feature information during the maximum pooling operation, which will limit the text classification performance. CNN cannot extract the emotion features of English text more comprehensively, and relies heavily on a large number of language knowledge and emotion resources. In this paper, we propose a hybrid neural network model based on multi-head attention for English text emotion analysis. Firstly, the new model uses multi-head attention to learn the dependence between words and capture the emotion words in the English text. Secondly, the improved bidirectional gated recurrent unit is used to extract different granularity emotion features of English text. According to each emotion category and attention mechanism, feature vectors are generated to construct the emotion feature vector set. Finally, the text emotion categories are judged according to the model attributes. The model is tested on MR, IMDB and SST-5 data sets, the results show that the proposed method has better classification effect compared with other models. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a distributed channel estimation (CE) algorithm for spatially non-stationary (SNS) channels in extremely large aperture array systems, addressing the issues of high communication cost and computational complexity associated with traditional centralized algorithms. However, SNS channels differ from conventional spatially stationary channels, presenting new challenges such as varying sparsity patterns for different antennas. To overcome these challenges, we propose a novel distributed CE algorithm accompanied by a simple-yet-effective hard thresholding scheme. The proposed algorithm is not only suitable for uniform antenna arrays but also for irregularly deployed antennas. Simulation results demonstrate the advantages of the the proposed algorithm in terms of estimation accuracy, communication cost and computational complexity. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The rapid change in artificial intelligence has evaluated ideological and political education ability in colleges and universities as a significant challenge. OBJECTIVES: To assess the level of competence of universities in ideological and political education to determine the effectiveness and efficacy of educational programs and to provide a basis for improving and upgrading academic competence. METHODS: Based on the CIPP model, the author constructed an index system and selected a suitable evaluation model to conduct a study on the evaluation of ideological and political competence of colleges and universities in the context of Artificial Intelligence, which helps to understand the background conditions, resource allocation, teaching activities and quality of teaching of educational programs, as well as the level of ideological and political literacy of the students and their achievements. RESULTS: The evaluation results show that this kind of evaluation research helps to improve and enhance the capacity of ideological and political education in colleges and universities, and at the same time, it can dig into the implementation effect of the educational program, find problems and shortcomings, and promote the continuous improvement of the educational program. CONCLUSION: Through evaluation, the quality and level of ideological and political education in colleges and universities can improve students' ideological and political literacy and sense of social responsibility. In addition, based on this, it makes the development of ideological and political ability in colleges and universities can be better adapted to the era of artificial intelligence. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In the realm of Wireless Sensor Networks (WSN), effective data dissemination is vital for applications like traffic alerts, necessitating innovative solutions to tackle challenges such as broadcast storms. OBJECTIVES: This paper proposes a pioneering framework that leverages probabilistic data aggregation to optimize communication efficiency and minimize redundancy. METHODS: The proposed adaptable system extracts valuable insights from the knowledge base, enabling dynamic route adjustments based on application-specific criteria. Through simulations addressing bandwidth limitations and local broadcast issues, we establish a robust WSN-based traffic information system. RESULTS: By employing primal-dual decomposition, the proposed approach identifies optimal packet aggregation probabilities and durations, resulting in reduced energy consumption while meeting latency requirements. CONCLUSION: The efficacy of proposed method is demonstrated across various traffic and topology scenarios, affirming that probabilistic data aggregation effectively mitigates the local broadcast problem, ultimately leading to decreased bandwidth demands. [ABSTRACT FROM AUTHOR]

The DevOps pipeline for infrastructure is a critical component in modern software development and operations practices. It involves automating the provisioning, configuration, and management of infrastructure resources, enabling organizations to achieve agility, scalability, and reliability. This paper presents a plagiarism-free analysis of the DevOps pipeline for infrastructure, conducted through comprehensive research, evaluation of industry best practices, and examination of case studies. The DevOps methodology would collapse without the use of a DevOps pipeline. The phrase is often used to discussions of the methods, procedures, and automation frameworks that go into the creation of software objects. Jenkins, an open-source Java program, is the most well-known DevOps pipeline and is often credited as the catalyst for the whole DevOps movement. Today, we have access to a plethora of DevOps pipeline technologies, such as Travis CI, GitHub Actions, and Argo. To keep up with the need for new and improved software systems, today's development organizations must overcome a number of obstacles. The research highlights key findings, including the importance of automation, infrastructure as code, continuous integration and delivery, security, and monitoring/logging capabilities. These practices have been shown to enhance efficiency, reduce errors, and accelerate deployment cycles. By evaluating tools and technologies, gathering user feedback, and analyzing performance metrics, organizations can identify gaps and develop a roadmap for pipeline improvement. To maintain academic integrity, this analysis adheres to proper citation and referencing practices. Paraphrasing and summarizing research findings and adding personal analysis and interpretations ensure the originality and authenticity of the analysis. Plagiarism detection tools are used to confirm the absence of unintentional similarities with existing content. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to monitor the status of electrical equipment using Internet of Things (IoT) and relaying-aided transmission technologies, where the data rate is used as the metric for evaluating the system monitoring performance. In this framework, relaying plays a pivotal role, enhancing the efficiency of data transmission in the monitoring process. We employ the optimal relay selection criterion to choose the most effective relay to assist the data transmission, thereby optimizing the communication link between the electrical equipment and the monitoring system. To provide a comprehensive understanding of the system performance, we delve into the analytical aspects by deriving an analytical expression for the data rate. This expression offers some insights into the theoretical performance limits and the factors influencing the efficiency of the system. The theoretical framework is further complemented by a series of simulations. These simulations validate the analytical expression developed in the study, and provide practical scenarios to demonstrate the real-world applicability and effectiveness of the proposed IoT and relaying-aided transmission technologies in monitoring electrical equipment. [ABSTRACT FROM AUTHOR]

Digitalization has brought ease in managing the operations of organizations in the world. However, education and digital literacy become hurdles for involving marginalized people. The participation of women is crucial for the overall development of humankind. "Gender equality and women's empowerment are essential for achieving sustainable development and creating a more just and equitable world"[1]. Indeed, greater women's participation can significantly boost qualitative and quantitative output in all fields. Promoting women's participation in rural areas can significantly impact agricultural economies, including increasing food production and dairy output. However, the participation of women in decision-making, particularly in rural areas, is often limited. This paper focuses on Identifying and analyzing the factors that prevent women from playing a more significant role in rural economies, which is an essential step towards promoting women's economic empowerment and achieving more equitable and sustainable development outcomes in the digital era. The study's finding suggests that education levels, lack of adequate training, and household work are key factors that limit the participation of women. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Tribal entrepreneurship can be understood as infusing the knowledge of commerce and trade into the tribal groups and thereby exploring their products and known for their economic betterment and social advancement. An accumulating body of research has demonstrated that artificial intelligence (AI) is an indistinguishable feature of the fourth industrial revolution. This study integrates the literature on AI and new technologies to examine the constraining and facilitating forces for developing and scaling-up AI-enabling technologies in Africa. This article proposes an integrated conceptual model to elucidate the range of external drivers encompassing global competitive drivers, and market and industry drivers. The internal drivers include the potential to enhance product development speed, improve quality, drive production cost down, and minimise errors and manual processes in organisations. OBJECTIVES: In this study, socio-economic status of tribal population has been taken as a reference in order to see whether there is any impact of economic wellbeing on their livelihood [1]. The study is an endeavour to examine the role of micro enterprises for social and economic empowerment of tribal community in Odisha and Andhra Pradesh and role of AI. RESULTS: The main results obtained in this paper are the following SEM identified that there is a lower degree of positive impact of "Tribal entrepreneurship" on "Economic Empowerment" and "women Empowerment" and lower degree of negative impact on "Social Empowerment". It is also concluded that Economic Empowerment of tribal entrepreneurs through micro-enterprises has reduced the dependency on private money lenders the most. Women Empowerment of tribal entrepreneurs has helped the women to achieve gender equality and the social Empowerment aids in the development of competency and technical skills through micro-enterprises. CONCLUSION: The tribal communities in Odisha are regarded as the most disadvantageous group of people in terms of their socio-economic status. The present research makes some pivotal contributions to the current AI literature. First, in spite of the growing recognition that development of new industries and new-business development is increasingly predicated on the adoption of new technologies (Krasniqi and Hajrizi Citation2016), there is a paucity of studies examining contemporary challenges faced by developing nations and their inability to capitalise on such ample and obvious opportunities. [ABSTRACT FROM AUTHOR]

More and more companies are using social media in their marketing. They're spending a lot of money to make sales and connect with customers quickly, which helps their brands do better and gets more people visiting their websites. This paper wants to look at how people's feelings and thoughts about a brand affect how they act on social media. Knowing how different people think is really important for managers who spend money on marketing. This study looks at how what people think about a brand relates to what they say about it on social media and if they support the brand there too. The research uses special ways to study how brands behave on social media. It focuses on how people feel about brands and how this affects what they do online. This study found that how people feel about a brand is connected to what they do to support it online. It also showed that different kinds of people act differently on social media, which is helpful for companies to know. Understanding how people feel about brands and act on social media is super important for companies. The study's results give useful ideas to make social media strategies better and to get more people involved with brands online. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Due to its potential to produce substantial returns and reduce risks, stock price prediction has garnered a lot of attention in the financial markets. OBJECTIVES: A comparison of neural network models for stock price prediction is presented in this research report. METHODS: Through this study, I aim to compare, on the basis of the precision and accuracy, the performance of different neural network models for stock price prediction. LSTM model along with RNN model accuracy in predicting the next day's stock price i.e., which model can predict closest to the actual value. RESULTS: It is found that LSTM works better than RNN in predicting a value closer to the actual open price stock value. CONCLUSION: A comparison between the models shows LSTM is the more accurate model. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an Internet of Things (IoT) network in severe fading environments, where the source sends some information to the destination. Due to the fading obstacle, the source cannot directly send the information to the destination. To tackle this issue, we consider the communication of IoT networks assisted by multi-hop relaying, where multiple relays can help assist the data transmission from the source to the destination. For the considered IoT networks assisted by multi-hop relaying, we evaluate the system performance by studying the transmission outage probability, where an analytical expression is derived to evaluate the IoT outage. We then evaluate the system performance by studying the transmission achievable data rate, where an analytical expression is provided to evaluate the IoT rate. Finally, we present some experimental results as well as the analytical results to verify the derived expressions for the IoT networks assisted by multi-hop relaying. In particular, the usage of multi-hop relaying can help extend the coverage area of IoT networks effectively. [ABSTRACT FROM AUTHOR]

With the increasing number or scale of large-scale assembly activities, emergency evacuation has become increasingly important. In order to understand the evacuation behavior of people, this paper uses European distance, simulation and other methods to optimize the research and design of evacuation in emergency. The main contents of this paper are as follows: First, based on the principle and update rules of cellular automata, determine the three key factors of gender, age and emotional intensity, and the evacuation model based on cellular automata, and then use MATLAB software to simulate the change of people flow under different anxiety conditions, and output relevant data and visual images. Finally, the differences of the experimental results caused by the key factors are discussed. Secondly, study the evacuation rate change curve, as well as the key factors and their effects. Based on the average speed and average flow model of the system, simulate and output the corresponding visual images. Thirdly, in order to study and determine the best simulated evacuation route and the dynamic process of personnel evacuation in the case of different door widths, based on the evacuation route selection model with the shortest time to reach the two doors, the best pedestrian evacuation route is selected according to the shortest time rule. Fourth, in order to study the impact of reduced visibility in the hall on the whole process. In this paper, the regional discretization method is used to establish the perception range model under visibility and analyze the factor changes in the whole process. [ABSTRACT FROM AUTHOR]

This paper represents the idea of implementing bio-inspired algorithms in designing an optimized PID controller to investigate the stability and improve the performance of the closed-loop CUK converter. Bio-inspired algorithms (BIA) such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) are stochastic optimization techniques and have been increasingly applied to attain optimal solution for designing and optimizing power converters in current times. The closed-loop transfer function of the CUK converter has been developed by the State Space Average (SSA) technique. This paper assesses both the cases of converter stability when it is integrated with the conventional PID controller and the BIA-PID controllers (FA-PID, PSO-PID, ABC-PID) and eventually compares the outcomes from all the controllers. For examining the stability of the system, three objective functions (IAE, ITAE, and ISE) and various performance specifications such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated. MATLAB and Simulink are used to carry out the simulations meticulously. Hence, a comparative analysis is illustrated in this paper to state a clear-sighted evaluation of the performances. [ABSTRACT FROM AUTHOR]

With the continuous advancement of robotics technology, the integration of robots into diverse human environments has become increasingly prevalent. However, the presence of robots in public spaces can often elicit discomfort or unease among individuals. To address this concern, the concept of concealing robots in various settings has emerged as an innovative approach to improve robot navigation and interaction while minimizing intrusion on human privacy. This paper explores the motivations, challenges, and potential benefits of hiding robots in different environments, particularly within the context of swarm robotics where multiple interconnected robots form a cohesive swarm. Equipped with onboard processing, communication, and sensing capabilities, these robots can autonomously interact with each other and adapt to the environment. The paper investigates the problem of maximizing the number of hidden orthogonal swarm robots, considering scenarios in which robots need to navigate and operate within polygonal environments. Specifically, it presents a 4-approximation algorithm for computing a maximum hidden robot set in such environments. The algorithm provides a practical solution for efficiently arranging robots, minimizing their visibility, and ensuring effective swarm operation. It also presents an O(n 2)-time 4-approximation algorithm to compute the maximum hidden robot set in simple polygons. The algorithm utilizes a working space of O(n), where n represents the complexity of the environment provided as input. Concealing robots in diverse environments offers several benefits. Firstly, it alleviates discomfort or unease among individuals, facilitating smoother integration of robots into public spaces. Additionally, concealing robots enhances their navigation capabilities by leveraging stealth techniques, allowing them to move seamlessly and unobtrusively within the environment. This approach also promotes improved human-robot interaction, as the reduced visibility of the robots can alleviate concerns and foster a more natural and comfortable interaction between humans and robots. Furthermore, it presents insights into future directions, including the development of more advanced stealth technologies, ethical frameworks for integrating hidden robots, and the potential impact on urban planning and infrastructure. In conclusion, hiding robots in diverse environments offers a promising approach to enhancing robot navigation, interaction, and privacy [ABSTRACT FROM AUTHOR]

This paper addresses the navigation of a team of k protector robots within pathways, focusing on minimizing the total number of turns. These robots utilize orthogonal routes known as watchman routes, which prioritize finding the shortest path while maintaining visibility of all points in the environment from at least one robot on its designated route. The main objective of this research is to optimize robot navigation by reducing the overall number of turns. The primary objective of this study is to develop a linear-time algorithm that efficiently processes and determines routes for k robots within a specified area. By minimizing the number of turns, this algorithm aims to enhance the navigation capabilities of watchman robots, enabling them to effectively traverse complex environments. This research employs techniques derived from computational geometry to investigate the navigation of protector robots. The focus is on developing an algorithm that can efficiently process and determine the optimal routes for the robots, considering factors such as visibility and shortest path length. The algorithm is designed to minimize the number of turns while ensuring efficient coverage of the environment. The main results of this paper include the development of a linear-time algorithm for determining routes for a team of k protector robots. The algorithm efficiently processes the input data and produces separate routes for each robot. By minimizing the number of turns, the algorithm improves the overall navigation efficiency of the robots. The results demonstrate the effectiveness of the algorithm in optimizing robot paths and reducing the complexity of navigation in real-world scenarios. In conclusion, this research contributes to the field of robotic systems by addressing the navigation challenges faced by a team of protector robots. The introduced linear-time algorithm optimizes the routes for k robots, aiming to minimize the total number of turns. The outcomes of this study have significant implications for the advancement of watchman robots, enhancing their coverage and surveillance capabilities in real-world applications. The algorithm's efficiency and effectiveness in minimizing turns open new opportunities for developing efficient navigation strategies in complex environments. [ABSTRACT FROM AUTHOR]

This paper "describes" the investigation of the stability of Degree of Freedom (DOF) flexible robotic arm by the diagrams shown below. The derived model is based on Euler-Lagrange approach. Exploration of a flexible robotic arm with using state-of-the-art controllers is essential for intelligent applications. These robot arms have joints that work independently of each other in order to create a smooth connection between joints. They still ensures the natural properties like a real human arm. The use of polarity assignment method "helps" the system to achieve desired output signals which has not been thoroughtly studied before for this system. The author can also compare the effectiveness of control methods for this system to find the most effective method for control strategies. In particular, ANN (artificial neural network) is the most modern technique currently applied to this system to investigate the security and stability of the system through this program. This is new and it has never been used before for a system of this type. Neural networks strategy has been implemented in this paper as an application of artificial intelligence. It has successfully performed a mission in resimulating functions of another control method: Polarity assignment method. Simulation results are done by Matlab. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Atherosclerosis is a chronic medical condition that can result in coronary artery disease, strokes, or even heart attacks. early detection can result in timely interventions and save lives. OBJECTIVES: In this work, a fully automatic transfer learning-based model was proposed for Atherosclerosis detection in coronary CT angiography (CCTA). The model's performance was improved by generating training data using a Generative Adversarial Network. METHODS: A first experiment was established on the original dataset with a Resnet network, reaching 95.2% accuracy, 60.8% sensitivity, 99.25% specificity and 90.48% PPV. A Generative Adversarial Network (GAN) was then used to generate a new set of images to balance the dataset, creating more positive images. Experiments were made adding from 100 to 1000 images to the dataset. RESULTS: adding 1000 images resulted in a small drop in accuracy to 93.2%, but an improvement in overall performance with 89.0% sensitivity, 97.37% specificity and 97.13% PPV. CONCLUSION: This paper was one of the early research projects investigating the efficiency of data augmentation using GANs for atherosclerosis, with results comparable to the state of the art. [ABSTRACT FROM AUTHOR]

Parkesin, the first thermoplastic ever created, was discovered and patented by Alexander Parkes in 1856. Plastic bags started to become extensively used in the US in 1966 as a result of his invention's ongoing growth. As an alternative to paper containers, the grocery sector adopted plastic shopping bags in 1977. Plastic bags are useful carriers or media for transporting and loading a lot of luggage. This condition causes issues because there are so many plastic bags in daily life, especially black plastic bags because they have more concentrated chemical residues and dyes than colored plastic bags.In order to create new forms of the plastic material, experimental and exploratory methods were applied to black plastic bags as part of the project-based learning approach. The end product is a plastic bag with particular qualities that may be mixed with imitation leather and taffeta fabric to create a semi-couture collection. It is slightly stiff but flexible like a cloth. [ABSTRACT FROM AUTHOR]

The purpose of this study was to find out how the development of citations, publication trends, a collaboration of authors, trends in title terms, and trends in keyword terms in Continuous Auditing articles (2013-2022). Data collection using publish or perish (PoP) software in bibliographic search as the initial database on Scopus. The results showed that from 200 scopus papers, the number of citations 2,5781 citations, with an average annual citation of 2915 citations, 14.58 citations per paper, and an average h-index of 26. The highest citations with authors from various countries; C. Sarkar (115 cites, rank 1). The most frequently used term in the title is continuous auditing with 27 items. It can be concluded that the trend of continuous auditing research is that various aspects are developed in preventing company failures and financial statement errors in the future. [ABSTRACT FROM AUTHOR]

Student behavior analysis can reflect students' learning situation in real time, which provides an important basis for optimizing classroom teaching strategies and improving teaching methods. It is an important task for smart classroom to explore how to use big data to detect and recognize students behavior. Traditional recognition methods have some defects, such as low efficiency, edge blur, time-consuming, etc. In this paper, we propose a new students behaviour recognition method based on spatio-temporal attention fusion model. It makes full use of key spatio-temporal information of video, the problem of spatio-temporal information redundancy is solved. Firstly, the channel attention mechanism is introduced into the spatio-temporal network, and the channel information is calibrated by modeling the dependency relationship between feature channels. It can improve the expression ability of features. Secondly, a time attention model based on convolutional neural network (CNN) is proposed, which uses fewer parameters to learn the attention score of each frame, focusing on the frames with obvious behaviour amplitude. Meanwhile, a multi-spatial attention model is presented to calculate the attention score of each position in each frame from different angles, extract several saliency areas of behaviour, and fuse the spatio-temporal features to further enhance the feature representation of video. Finally, the fused features are input into the classification network, and the behaviour recognition results are obtained by combining the two output streams according to different weights. Experiment results on HMDB51, UCF101 datasets and eight typical classroom behaviors of students show that the proposed method can effectively recognize the behaviours in videos. The accuracy of HMDB51 is higher than 90%, that of UCF101 and real data are higher than 90%. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The emerging industrialization of software promotes the continuous development of new evaluation models that adapt to the product's characteristics and the organizations' needs. OBJECTIVES: This article aims to identify the software quality models proposed between 2016 and 2020. METHODS: We conducted an exploratory systematic review in the SciELO, IEEE Xplore, and Scopus databases, resulting in 14 research papers proposing new models for assessing software quality. RESULTS: 79% of the research papers were extracted from IEEE Xplore, and 50% of them were authored by individuals affiliated with European institutions. We presented various software quality models focused on assessing both product quality and usability for specific purposes. Additionally, we reported that the proposed models are based on common software quality metrics standards such as CMMI, ISO/IEC 9126, and others. CONCLUSION: The review serves to update the state of the art regarding the software quality models proposed in recent years, which will be valuable for researchers and software developers when seeking evaluation models that align with their specific needs. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In the new era of information society, the knowledge reform of school music education is getting more and more attention. Traditional teaching methods need to make full use of information technology. They cannot meet the current learning needs because the learning of information technology teaching is an essential means to realize modern learning., therefore, the application of information technology in music education meets the requirements of the times. OBJECTIVES: To improve traditional teaching methods by utilizing modern teaching techniques so that students can gain more knowledge. With the help of modern technology and information technology, secondary music education can change the students' audiovisual experience, enrich their musical experience, and deepen their understanding and memorization of knowledge. METHODS: In this paper, use a neural network algorithm to explore the integration mechanism of music teaching design and education information technology, optimize the traditional information technology teaching platform, improve the stability of the music teaching platform, and further improve the level of network teaching from the feedback of students and teachers. RESULTS: This study's results found that this experiment's educational platform could motivate students to manage teaching and actively participate in music teaching and lessons. However, due to the real-time nature of information technology, it removes doubts between the parties and facilitates communication between teachers and students. The primary goal of music education is to perceive the attraction of music in terms of sight and sound, to relax the mind and body, and to develop a musical personality. From this perspective, it is also necessary to utilize modern information technology to change teachers' traditional teaching methods in classical performance and singing. CONCLUSION: With the help of modern technology and information technology, secondary music education can change the audiovisual experience of students, enrich their musical experience, and deepen their understanding and memorization of knowledge. The information system can motivate students to manage teaching and actively participate in music teaching. [ABSTRACT FROM AUTHOR]

In this paper we investigated about the potential problems occurring worldwide, regarding social networks with misleading advertisements where some authors applied some artificial intelligence techniques such as: Neural networks as mentioned by Guo, Z., et. al, (2021), sentiment analysis, Paschen (2020), Machine learning, Burkov (2019) cited in Kaufman (2020) and, to combat fake news in front of such publications by social networks in this study were able to identify if these techniques allow to solve the fear that people feel of being victims of misleading news or fake videos without checking concerning covid-19. In conclusion, it was possible to detail in this paper that the techniques applied with artificial intelligence used did not manage to identify misleading news in a deep way. These techniques used are not real-time applications, since each artificial intelligence technique is separately, extracting data from the information of social networks, generating diagnoses without real-time alerts. [ABSTRACT FROM AUTHOR]

INTRODUCTION: With the development of the information age and the application of cloud computing and big data technology, new changes have occurred in the field of education. Today, the form of learning is becoming more and more diversified, and cloud computing, big data and other information technologies can be more often applied in teaching and learning. The application of cloud computing to the creation and teaching of music can not only improve the interaction between teachers and students in the traditional classroom, but also cloud computing can better capture the problems of teaching in the classroom and provide adjustment suggestions through big data. OBJECTIVES: This study is to realize distance music teaching of SPOC through cloud computing, big data and other technologies. Further use of cloud computing technology is to improve the traditional music teaching classroom teaching and learning tools are not rich enough, the student perception is more single problem. At the same time, cloud computing technology is also utilized in order to explore the effect of distance music education. METHODS: Based on the management of learning theory, constructivism and communicative learning theory, this paper carries out the research on music creation and teaching through the methods of cloud computing and big data, and collects and summarizes the related literature at home and abroad, researches the development of big data and cloud computing in the field of music teaching and introduces the principle of SPOC teaching that supports students' independent learning through big data technology. RESULTS: Based on the training concept of MOOC and cloud computing, the teaching process encourages teachers and students to apply the pedagogical methods of cloud computing to improve the level of music nowadays teaching and learning at the same time, improve the overall quality of students. CONCLUSION: Music aesthetics education has diversity, SPOC learning process and cloud computing technology should be developed by developing an independent learning method. Therefore, cloud computing technology can help students learn music independently in a three-dimensional way, effectively develop the design stage of the learning process, use big data and cloud computing to better enrich the music teaching model, and implement the construction of independent knowledge. Future research can continue to develop towards empirical evidence and a large number of data experiments. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The cultivation of vocational literacy of higher vocational computer students is related to the employment prospects of graduates and has a significant impact on the computer talent market. The success or failure of curriculum reform will directly impact the effect of cultivating professionalism in higher vocational computer majors. OBJECTIVES: This paper introduces the source and function of the C4.5 model, establishes model evaluation indexes to assess the application effect of the C4.5 model; the number of iterations and prediction accuracy are important indexes of the model's functioning and puts forward five measures suggestions for the reform of vocational literacy course of higher vocational computer majors. METHODS: C4.5 model is applied to reform vocational literacy courses for higher vocational computer majors, and the effect of practical implementation is evaluated using a questionnaire. RESULTS: By applying the C4.5 model to reform the vocational literacy course for senior computer majors, the graduates' vocational literacy has been significantly improved. CONCLUSION: The research contributes to the realization of the goal of cultivating higher vocational computer talents and provides the market with computer talents with outstanding professionalism. [ABSTRACT FROM AUTHOR]

INTRODUCTION: To apply artificial intelligence technology to ideological and political education in colleges and universities, as well as to combine artificial intelligence technology with ideological and political education in colleges and universities, it is necessary that wireless networks have complete coverage. OBJECTIVES: How can ideological and political education in universities and colleges be merged with artificial intelligence? How can artificial intelligence be used to support progressive political education at the college level? Starting with these issues, this paper will focus on the development of ideological and political education in colleges and universities as its main research question and refer to artificial intelligence technology as a method of ideological and political education in colleges and universities supported by wireless networks. METHODS: By examining the characteristics of artificial intelligence and ideological education in colleges and universities, and combining the poor immediacy and weak timeliness of information dissemination, as well as the low degree of identification of student groups with the theoretical courses of ideological education in the field of ideological education under the view of artificial intelligence, to explore the path of innovation of ideological education. RESULTS: In order to adapt to the demands of AI technology and improve people's capacity to use AI learning software, we need start with educators and educated individuals. Additionally, the government should encourage the development of artificial intelligence technologies financially and technically. Finally, it examines how civic education in colleges and universities could be improved through the use of artificial intelligence. This would allow civic education to benefit from the advantages of AI technology. CONCLUSION: In order to support the positive and healthy development of political education courses in colleges and universities across the nation, this paper encourages the creation of concepts and methods for teaching politics in higher education institutions. [ABSTRACT FROM AUTHOR]

Reconfigurable Intelligent Surfaces (RIS) have been highlighted by the research community as a key enabling technology for the enhancement of next-generation wireless network performance, including energy efficiency, spectral efficiency, and network throughput. This paper investigates how RIS-assisted communication can effectively maximize the downlink throughput of a cellular network. Specifically, the paper considers a communication scenario where a single base station serves multiple ground users with the aid of an RIS placed on a building facade. For such a communication scenario, we considered an optimization problem aimed at maximizing the overall downlink throughput by jointly optimizing power allocation at the base station and phase shift of RIS reflecting elements, subject to power consumption and quality-of-service constraints. To address its non-convex nature, the original optimization problem has been divided into two subproblems. The first one, for power control with fixed phase shift values, is a convex problem that can be easily solved. Subsequently, a phase shift searching procedure to solve the non-convex problem of RIS phase shift optimization has been adopted. The results from numerical simulations show that the proposed method outperforms other conventional methods proposed in the literature. In addition, computational complexity analysis has been conducted to prove the low complexity of the proposed method. [ABSTRACT FROM AUTHOR]

Compared with traditional cellular network architecture, cloud access network has some significant advantages in spectrum utilization, energy consumption and network construction cost. However, a highquality forward link is required between the baseband processing unit pool and remote radio head (RRH) in the cloud access network, which results in limited RRH deployment and affects user access link transmission and coverage. To solve this issue, this paper introduces the intelligent reflector technology into the cloud access network as a solution with low energy consumption, low cost and easy deployment to deal with the existing bottlenecks. Firstly, an efficient channel information acquisition strategy based on federated learning is designed for the smart reflector to enhance the user access link, so as to achieve a compromise between the channel estimation accuracy and cost. On this basis, a robust beamforming design and optimization method of the compression mechanism of the forward link are proposed for the smart reflector to enhance the user access link and the wireless forward link, so as to improve the system transmission performance. Finally, we explore the joint resource allocation method of intelligent reflector assisted cloud access network, and improve the system energy efficiency through the collaborative configuration of intelligent reflector and cloud access network communication resources. The research of this paper will provide an important theoretical basis for the application of intelligent reflectors in cloud access networks, especially for the federated learning. [ABSTRACT FROM AUTHOR]

We provide (1) an overview of various present and future applications of smart contracts across various industries including real estate, finance, and healthcare and (2) an assessment of the efficacy of smart contracts as a means of replacing or supplementing traditional contracts. Disclosed in this paper are (1) present and future applications of smart contracts and potential risks and downsides, and (2) legal considerations when using smart contracts to replace or supplement traditional contracts. Aspects of blockchain technologies can be applied to traditional contracts, in part or in whole, to reduce common challenges associated with contracts. Specifically, smart contracts can be integrated with or replace traditional contracts with the benefit of ensuring reciprocal obligations are enforced and aid in ensuring mutual consent, offer and acceptance, consideration and legal purpose. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Knot theory has a long history, and as a branch of topology, it has received extensive attention. At present, the scientific analysis of data based on the similarity of Vassiliev invariants and knots under machine learning technology is the focus of the mathematical community. However, at present, there are some difficulties in the research work on the similarity of Vassiliev invariants and knots, including the low accuracy of the analysis of the characteristics of knots, the long analysis time and the high memory resources occupied in the analysis process. These difficulties not only delay the progress of Vassiliev invariants research, but also slow down the speed of knot similarity research. OBJECTIVES: However, with the acceleration of the intelligent process, various intelligent technologies have been applied in the research of mathematics, biology and physics, providing excellent help for the research of many disciplines. Therefore, machine learning technology could be used to carry out new research on Vassiliev invariants and knot similarity. METHODS: Traditional knot analysis technology was combined with machine learning technology to find a more efficient and stable way of exploring Vassiliev invariants and knot similarity. his paper proposed a research method of data scientific analysis based on Vassiliev invariants and knot similarity under machine learning technology. Its purpose was to combine traditional knot research methods with machine learning technology to improve the efficiency of knot research. The algorithm proposed in this paper was the knot Vassiliev invariant analysis algorithm based on machine learning, which could use the intelligent and efficient analysis algorithm of machine learning technology to process the data of complex knots. This algorithm has improved the accuracy of the analysis of knot characteristics and reduced the analysis time and the memory consumption at runtime. RESULTS: By testing the similarity between the Vassiliev invariant based on machine learning and the knot, the results showed that the analysis accuracy of the traditional Vassiliev invariant computing technology for the chiral characteristics, the number of intersections and the number of knots in the knot image was 84.25%, 83.27% and 85.56% respectively. The accuracy of knot Vassiliev invariant analysis algorithm based on machine learning for these indicators was 91.87%, 92.66% and 92.12% respectively. Obviously, the knot Vassiliev invariant analysis algorithm based on machine learning was superior to the traditional knot computing technology, and its analysis results were more excellent. CONCLUSION: In general, the research topic proposed in this paper has been proved to be of practical value. This research result proved that machine learning technology could play an excellent role in the current knot research, which correspondingly expanded the research direction of Vassiliev invariants and knot similarity. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor test effect and imperfect system function in the existing English course unit test system, an online English course unit test system based on SSH framework is designed to accurately recommend the required online English course resources for students and better assist students to complete the English course unit test. The SSH framework integrates the characteristics of the three mainstream open source frameworks of struts, spring and hibernate, and designs an online English course unit test system including presentation layer, business logic layer and data persistence layer. A variety of databases in the data persistence layer select MySQL database for data storage, The business logic layer uses the personalized English Course Resource Recommendation Model Based on three-dimensional feature collaborative control of the business server to meet the personalized needs of students' English courses, uses the application server to complete the front-end examination and back-end management of the system, and displays the system test results through the presentation layer. The experiment shows that the online English curriculum resources recommended by the system have obvious differences, strong adaptability and high matching degree; The CPU utilization rate of the system is always below 25%, the disk reading and writing speed is fast, and the fluctuation difference is not higher than 1000KB / s. The system has high storage performance. [ABSTRACT FROM AUTHOR]

The increasing demand for electricity and the impact of the consumption of fossil fuels leads to the need for approaching new alternatives of energy production, namely energy storage systems (ESS) and renewable energy sources (RES). For this purpose, this paper presents a single-phase current-source converter (CSC) with a hybrid dc-dc converter connected to the dc-link, which not only allows the interface of an electric vehicle (EV) and a RES, but also operates as shunt active power filter (SAPF). The operation as SAPF is performed through the CSC connected to the power grid, compensating some power quality problems that can occur in the electrical installation, namely current harmonics and low power factor. Moreover, the CSC can operate as a grid-tied inverter or as an active rectifier. Regarding the hybrid dc-dc converter, the main role of this power converter is to interface the CSC dc-link with the power converters for the EV and RES interfaces. As demonstrated along the paper, the CSC, combined with the hybrid dc-dc converter, allows the operation as SAPF, as well as the operation in bidirectional mode, specifically for the EV operation, and for injecting power from the RES. In this paper, the power electronics structure is described in detail, and the operating principle is introduced, supported by the description of the control algorithms. The validation results show the proper operation of the CSC as SAPF and the operation of the hybrid dc-dc converter as EV battery charger and discharger, as well as RES interface. [ABSTRACT FROM AUTHOR]

The Digital image plays an important role in today’s digital world. Storing and transmitting digital images efficiently is a challenging job. There are lots of techniques for reducing the size of digital pictures. This paper adapts the following method. The digital technique is separated into high and low resolutions. The low intensity and high intensity pixels single-handedly is dense and decompressed using three diverse algorithms to hit upon out the occurrence of low down intensity pixels in the picture. Totally six algorithms are experienced by means of benchmark images and the most excellent scheme is selected for concluding compression. A Comparison is made between the results obtained using these techniques and those obtained using JPEG 2000. [ABSTRACT FROM AUTHOR]

This paper presents a novel protective shell system designed to safeguard both the drone and its accompanying sensors. The objective of this project is to develop and test the shell-shaped container to check its capability of securely housing the drone. The system may incorporate a circuit board housing a processor that receives data from sensors and controls the opening and closing motions of the container. By analyzing the sensor information, the system may intelligently respond to adverse weather conditions by automatically closing the container to ensure the drone’s protection. This innovative solution addresses the challenges associated with deploying drones in outdoor environments and offers enhanced safety and durability for extended drone operations. [ABSTRACT FROM AUTHOR]

Deep clustering is an approach that uses deep learning to cluster data, since it involves training a neural network model to become familiar with a data representation that is suitable for clustering. Deep clustering has been applied to a wide range of data types, including images, texts, time series and has the advantage of being able to automatically learn features from the data, which can be more effective than using hand-crafted features. It is also able to handle high-dimensional data, such as time series with many variables, which can be challenging for traditional clustering techniques. In this paper, we introduce a novel deep neural network type to improve the performance of the auto-encoder part by ignoring the unnecessary extra-noises and labelling the input data. Our approach is helpful when just a limited amount of labelled data is available, but labelling a big amount of data would be costly or time-consuming. It also applies for the data in high-dimensional and difficult to define a good set of features for clustering. [ABSTRACT FROM AUTHOR]

The world has lived an exceptional time period caused by the Coronavirus pandemic. To limit Covid-19 propagation, governments required people to wear a facial mask outside. In facial data analysis, maskwearing on the human face creates predominant occlusion hiding the important oral region and causing more challenges for human face recognition and categorisation. The appropriation of existing solutions by taking into consideration the masked context is indispensable for researchers. In this paper, we propose an approach for mask-wearing prediction and adaptive facial human-gender classification. The proposed approach is based on convolutional neural networks (CNNs). Both mask-wearing and gender information are crucial for various possible applications. Experimentation shows that mask-wearing is very well detectable by using CNNs and justifies its use as a prepossessing step. It also shows that retraining with masked faces is indispensable to keep up gender classification performances. In addition, experimentation proclaims that in a controlled facepose with acceptable image quality' context, the gender attribute remains well detectable. Finally, we show empirically that the adaptive proposed approach improves global performance for gender prediction in a mixed context. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The quality assessment technology of distance education in colleges and universities, as the critical technology for identifying the quality of distance education in colleges and universities, is conducive to the improvement of the quality of distance teaching and the progress of the existing means and methods of distance education, which makes the means of distance teaching in colleges and universities rich in science. OBJECTIVES: Aiming at the evaluation methods of higher education institutions, there are problems such as insufficient objectivity and comprehensiveness of the evaluation system, single process, and inadequate quantitative analysis. METHODS:Proposes a decision tree and intelligent optimization algorithm for the college distance teaching quality assessment method. Firstly, the kernel principal component analysis method is used to carry out dimensionality reduction analysis on the index system of college distance teaching quality assessment; then, the decision tree parameters are optimized through the marine predator algorithm to construct a college distance teaching quality assessment model; finally, the robustness and efficiency of the proposed method are verified through simulation experimental analysis. RESULTS: The results show that the proposed method improves the accuracy of the assessment model. CONCLUSION: The problem of insufficient objective and scientific evaluation and low precision of distance teaching quality assessment methods in colleges and universities is solved. [ABSTRACT FROM AUTHOR]

INTRODUCTION: As the essential part of intelligent learning, innovative learning model construction is conducive to improving the quality of intelligent new teaching models, thus leading the deep integration of teaching and artificial intelligence and accelerating the change and development of teaching supported by artificial intelligence. OBJECTIVES: Aiming at the current intelligent teaching evaluation design method, there are problems such as more objectivity, poor precision, and a single method of evaluation indexes. METHODS: his paper proposes an intelligent learning construction method based on cluster analysis and deep learning algorithms. First of all, the intelligent learning model construction process is sorted out by clarifying the idea of clever learning model construction and extracting model elements; then, the intelligent learning model is constructed through a K-means clustering algorithm and deep compression sparse self-encoder; finally, the effectiveness and high efficiency of the proposed method is verified through simulation experiment analysis. RESULTS: Solved the problem that the intelligent learning model construction method is not objective enough, has poor accuracy and is not efficient enough. CONCLUSION: The results show that the proposed method improves the model's accuracy. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The construction of the wisdom teaching evaluation system, as the essential part of the institution's teaching reform, is conducive to developing the institution's disciplines, making the existing teaching more standardized, and making the means of teaching diversified, intelligent, and convenient. OBJECTIVES: Aiming at the current intelligent teaching evaluation design method, there are evaluation indexes that need to be more comprehensive, a single method, and system standard limitations. METHODS: Proposes an intelligent optimization algorithm for a multi-dimensional innovative teaching quality evaluation method. First of all, the multi-dimensional wisdom teaching evaluation system is constructed by analyzing the influencing factors of teaching quality evaluation; then, the parameters of the depth limit learning machine are optimized by the bird foraging search algorithm, and the multi-dimensional wisdom teaching evaluation model is constructed; finally, the validity and stability of the proposed method are verified by the analysis of simulation experiments. RESULTS: The results show that the proposed method improves the accuracy of the evaluation model. CONCLUSION: Solves the problem of low evaluation accuracy and incomplete system of teaching quality evaluation methods. [ABSTRACT FROM AUTHOR]

In athletics sports, accurate identification and correction of athlete's wrong posture can improve the quality of athlete's daily training. In the course of athletics sports, affine deformation of human body is easy to occur, which leads to the appearance of action feature points with low brightness and shading. However, the traditional method is to extract these feature points and compare them with the correct posture to realize the recognition and correction of posture, which leads to the failure of real-time detection and correction of athletes' wrong posture. Therefore, this paper proposes a method of posture recognition and correction for athletes with depth image bone tracking. The threshold method is used to preprocess the image, and the Kalman filter is used to filter the acquired image. The motion feature points are obtained from the filtered image by Gaussian distribution function. By improving SURF-RANSAC method, marginal points and action feature points with low brightness are screened out. Euclidean distance method is used to determine the distance between two adjacent feature points, and feedback monitoring principle is used to identify and correct the wrong posture. The simulation results show that the improved posture recognition and correction method of depth image bone tracking can realize tracking and monitoring of track and field athletes' movements, complete the detection and recognition of track and field sports posture with high accuracy and strong stability. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In this paper, a remote injury diagnosis system for Wushu competition based on wireless sensor network is designed to improve the safety of athletes in the process of Wushu competition. OBJECTIVES: Improve the safety of athletes during martial arts competitions. METHODS: On the basis of clarifying the overall architecture of the system, the hardware is equipped with pulse sensor, temperature sensor and ECG sensor as the terminal node of the injury diagnosis system of long-distance Wushu competition. The sensor is used to collect the physiological parameters of athletes in Wushu competition, and the collected parameters are transmitted to the wireless RF module. The radio frequency module uses the wireless sensor network to realize the wireless communication of various parameter data through the routing node and terminal node, and transmits the data to the remote diagnosis module. In the software part, the athletes' physiological parameters collected by the remote diagnosis module are used to realize the remote diagnosis of injury in remote Wushu competition through particle swarm optimization support vector machine diagnosis model. RESULTS: The experimental results show that the designed system can remotely collect the physiological parameters of athletes in Wushu competition, and remotely diagnose the injury of Wushu competition according to the collected data, and the diagnosis accuracy is as high as 99%. CONCLUSION: It has good safety performance and is of practical significance [ABSTRACT FROM AUTHOR]

The traditional ciphertext retrieval scheme has some problems, such as low retrieval performance, lack of single keyword retrieval mode and limitation of single machine resources in traditional single server architecture. At the same time, for searchable encryption, it needs to balance the data security and retrieval efficiency. In this paper, a multi-keyword parallel ciphertext retrieval system based on inverted index is proposed. The system adopts different index encryption methods to improve the performance of ciphertext retrieval. Through the segmentation of ciphertext inverted index, the block retrieval of inverted index is realized, which overcomes the limitation of single machine resources and improves the retrieval efficiency. By combining the characteristics of distribution, the traditional single-machine retrieval architecture is extended and multi-keyword parallel retrieval is realized. The experimental results show that compared with SSE-1 scheme, the proposed scheme can improve the efficiency of retrieval, update and other operations on the premise of ensuring the security of ciphertext data, achieve multi-keyword retrieval, and dynamically expand the distributed architecture of the system. Finally, it can improve the system load capacity. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In recent years, e-commerce has shown a booming trend. Influenced by e-commerce, people's logistics needs have also increased sharply in recent years. OBJECTIVES: Research on the node layout and optimization of e-commerce logistics is conducive to improving the scientificity and rationality of logistics node layout, improving logistics distribution efficiency, reducing logistics distribution costs, and better meeting consumers' logistics needs. However, due to the unreasonable layout of logistics nodes in some areas, it has brought huge logistics cost investment to e-commerce companies, and also laid hidden dangers for the long-term development of e-commerce companies. METHODS: Based on this, this paper studied the node layout and optimization of e-commerce logistics by using IoT big data and deep learning algorithms, and proposed an improved logistics node layout scheme based on IoT big data and deep learning algorithms. The experimental research was carried out from five aspects: the transportation cost of logistics, the efficiency of logistics distribution, the accuracy of logistics information transmission, the location and traffic conditions of logistics nodes, and the evaluation of the plan by e-commerce enterprises. RESULTS: The research results showed that the improved logistics node layout scheme can improve the efficiency of logistics distribution by 3.69% and the accuracy of logistics information transmission by 4.34%, and can reduce the logistics transportation cost of e-commerce enterprises. CONCLUSION: The node locations selected by the improved logistics node layout scheme are more reasonable, and ecommerce companies have higher evaluations of the improved logistics node layout scheme. [ABSTRACT FROM AUTHOR]

This paper aims to investigate an intrusion detection network for digital power grid networks, which consists of an edge server and two computational nodes that work collaboratively to detect any potential intrusion in the network. The primary objective of this study is to enhance the effectiveness of intrusion detection in the network. To achieve this objective, we first define the outage probability of the intrusion detection system under consideration. This is done to provide a measure of the probability that the system fails to detect an intrusion when it occurs. We then derive a closed-form expression for the outage probability to enable further analysis on the system behavior. Since the system resources, such as transmit power, are limited, we further design a transmit power allocation strategy to improve the system performance. This strategy seeks to optimize the allocation of transmit power across the different nodes of the intrusion detection network to maximize the likelihood of detecting intrusions while minimizing the resource usage. Finally, to evaluate the performance of the proposed system, we conduct simulations and provide results that demonstrate the accuracy of the closed-form expression and the effectiveness of the transmit power allocation strategy. These simulation results serve as evidence of the efficacy of the proposed approach in detecting intrusions in a resource-constrained network, especially for the digital power grid networks. [ABSTRACT FROM AUTHOR]

Free-space laser communication (lasercom), a great application of using free-space optics (FSO) for satellite communication, has been gaining significant attraction. However, in spite of great potential of lasercom, its performance is limited by the adverse effects of atmospheric turbulence and cloud attenuation, which directly affect the quality and availability of lasercom links. The paper, therefore, concentrates on evaluating the cloud attenuation in the FSO downlinks between satellite and ground stations in tropical regions. The meteorological ERA-Interim database provided by the European Center for Medium-Range Weather Forecast (ECMWF) from 2015 to 2020 is used to get the cloud database in several areas in tropical regions. This study proposed a novel probability density function of cloud attenuation, which is validated by using a well-known curve-fitting method. Moreover, we derive a closed-form of satellite-based FSO link availability by applying the site diversity technique to improve the system performance. Numerical results, which demonstrate the urgency of the paper, reveal that the impact of clouds on tropical regions is more severe than in temperate regions. [ABSTRACT FROM AUTHOR]

Numerous countries have been affected by natural disasters, the seriousness of which consequences is self-evident, such as power failure, building collapse and communication interruption. With the rapid development of science and technology, the Internet of Things (IoT) has become one of the most important part of current information technology. More and more countries and regions have realized the significance of IoT in dealing with natural disasters and are making efforts to put IoT into use. This paper summarizes the latest progress of IoT with fog computing, swarm robots and other advanced technologies in coping with natural disasters on four aspects. It focuses on how the IoT and other information technologies can solve problems encountered in natural disasters to a certain extent. [ABSTRACT FROM AUTHOR]