In recent years, the integration of science and art has continuously promoted the dynamic inheritance of traditional culture into a new stage. This article focuses on the mechanism optimization and adaptation decisions of the active inheritance of Hetu Luoshu under the background of AI empowerment, attempting to break the one-way link of display replication and explore the multi-layer feedback between symbol semantics, user behavior, and AI self-learning. The team relies on multi-source data collection and simulation experiments to conduct in-depth modeling of key variables such as symbol features and interaction strength, analyze the adaptation peaks, valleys, mismatch faults of AI decision models under different mechanisms, and identify real bottlenecks and innovative points in the process of active inheritance. The study simultaneously introduces dynamic intervention paths such as user stratification and expert feedback and constructs an adaptive decision-making system that is both closed-loop and behavior driven.
This study uses Sentinel satellite data to estimate forest quality over a large area, focusing on Beijing. By combining ground survey data with remote sensing, a random forest model predicts forest parameters. The results show a correlation coefficient of 0.60-0.76 and a relative root mean square error of 0.09-0.39. Average tree height and diameter at breast height (DBH) had the highest accuracy (75%-80%), followed by canopy density and plant number density (68%-75%). The spatial agreement between predicted and actual forest quality indicates the model's effectiveness.
This study uses Sentinel satellite data to estimate forest quality over a large area, focusing on Beijing. By combining ground survey data with remote sensing, a random forest model predicts forest parameters. The results show a correlation coefficient of 0.60-0.76 and a relative root mean square error of 0.09-0.39. Average tree height and diameter at breast height (DBH) had the highest accuracy (75%-80%), followed by canopy density and plant number density (68%-75%). The spatial agreement between predicted and actual forest quality indicates the model's effectiveness.
With the comprehensive and in-depth implementation of the “going global” strategy of Chinese culture, research on the overseas dissemination of Chinese literature has gradually become a prominent discipline. This article first defines the essence of overseas translation of Chinese literature, and based on Brado's 7W communication model, combined with the current situation of overseas dissemination of Chinese literature, analyzes the motivation of translation ecology research on overseas dissemination of Chinese literature. Through simulation experiments and actual data analysis, the differences in the accuracy of individual and joint regularization methods in small sample high-dimensional feature selection of deep learning models were compared. The research on the overseas dissemination of Chinese literature is still in its infancy, and conducting research and analysis on it is of great significance.
This study addresses the challenge of selecting optimal locations for urban sports facilities, leveraging the strengths of the ant colony optimization (ACO) algorithm. An enhanced ACO model is proposed, incorporating population density and distance to sports facilities as critical factors in the objective function. The model employs a unique pheromone updating strategy that reduces search time and improves solution quality. Two updates to the pheromone levels are performed, and the initial pheromone distribution is reset based on path distances. The effectiveness of the model is demonstrated through a case study in Yuhua District, Changsha City, where it successfully identifies prime locations for public sports facilities. This research contributes to the literature on facility siting and urban planning by offering a practical solution for optimizing the distribution of sports infrastructure within cities.
Coal is a prominent energy resource for several countries. Of late, exploring the automatic management and control of coal mining has been a significant task. This article presents a framework for a mine-wide integrated automation management and control platform with the goal of advancing coal mining through unified data, models, platforms, and plans. Utilizing cutting-edge technologies, the platform offers resource management, real-time monitoring, remote control, statistical analysis, and intelligent alarm systems. Data access design ensures standardized data collection and exchange, fostering interoperability. A big data storage center manages heterogeneous data sources. The platform interface design emphasizes flexibility and scalability through containerized applications and microservices frameworks, streamlining deployment. The functional design encompasses subsystem configuration access, real-time monitoring, remote access, etc. A detailed evaluation is presented to demonstrate the significance of the proposed platform in terms of functionality, performance, and scalability.
Drawing on the theoretical frameworks of structural functionalism and relational research perspectives, this study meticulously constructs and analyzes China's inbound tourism flow network spanning the years 2001 to 2023. Employing a diverse array of analytical tools such as the small-world model, rank-size model, structural equivalence model, core-periphery model, and various centrality metrics, the research delves into the structural characteristics and evolution patterns of China's inbound tourism flow network within a comprehensive spatial-temporal framework. Key findings include: 1) Small-world and scale-free properties with tourist flows concentrated in pivotal provinces. 2) Persistence of a U-shaped pattern in inbound tourism, with robust activity in eastern and western regions. 3) Presence of a core-periphery structure, with Beijing, Shanghai, and Guangdong as national hubs. This research enhances understanding of tourism industry spatial organization, offering insights for policymakers and stakeholders in tourism development.
The ongoing energy structure reform in our country has led to the emergence of distributed renewable energy as a primary source of energy development and utilization, primarily due to its utilization of local resources. However, challenges such as undefined objectives and ineffective planning have impeded its progress. This study specifically investigates distributed renewable energy power planning by enhancing a particle swarm algorithm with a strategy for updating local optimal solutions. The refined algorithm tackles issues related to renewable energy variability and economic efficiency, thereby optimizing the planning of distributed renewable energy power systems. The outcomes illustrate improvements in system operation, economic viability, and environmental sustainability. This research contributes to the progression of particle swarm algorithms for the planning of distributed renewable energy power systems.
Recent advancements in deep learning have popularized Generative Adversarial Networks for image generation. This study investigates integrating Generative Adversarial Networks technology into architectural design to empower architects in creating diverse, innovative, and practical designs. By analyzing architectural research, deep learning theory, and practical Generative Adversarial Networks applications, we substantiate the feasibility of using Generative Adversarial Networks for architectural design optimization. The generated architectural images exhibit significant diversity, innovation, and practicality, inspiring architects with numerous design possibilities. Overall, Generative Adversarial Networks technology not only expands design methodologies but also stimulates groundbreaking innovation in architectural practice. As technology progresses, Generative Adversarial Networks-based architectural design optimization shows promising potential for widespread adoption, heralding a new era of creativity and efficiency in architecture.
There is a rising need for sensors under an IoT network to identify and monitor the environment as more IoT devices and services are made accessible for use. This movement presents challenges such as the proliferation of data and the scarcity of energy. This research presents a strategy for enhancing the service provision capabilities of WSN-aided IoT applications by combining mobile edge computation with wireless signal and control transmission. In order to reduce overall system energy consumption while maintaining data transmission rate and power needs, a new optimization problem integrating power allocation, CPU frequency, offloading weight factor, and energy harvesting is devised. The non-convex nature of the problem necessitates the development of a novel ideal solution group iterative process optimization model that divides the original problem into multiple subproblems, with each subproblem being optimized in turn. According to the results of simulations with a numerical model, our proposed method consumes considerably less energy than just the two benchmark methodologies.
For patients with limb motor dysfunction, the effect of physical exercise is directly related to their future quality of life. This article combines the physical training plan of rehabilitation therapists with the training of rehabilitation robots, which can effectively improve the training performance of existing lower limb rehabilitation robots. Therefore, a teaching and training method and a wireless data acquisition system based on energy acquisition wireless network sensor are proposed. Based on wireless wearable technology, wireless network sensors, PCs and electronic devices are used to monitor the activity information of human walking and standing in real time, and the physical fitness is tested by means of mean, variance, and standard deviation. Through the analysis of rehabilitation health, this article consists of two parts: power module and physical exercise. Finally, experiments show that the accuracy of wireless network sensors based on SVM algorithm is the most accurate under physical training. It provides a good means for wireless body area network technology.
Coal is a prominent energy resource for several countries. Of late, exploring the automatic management and control of coal mining has been a significant task. This article presents a framework for a mine-wide integrated automation management and control platform with the goal of advancing coal mining through unified data, models, platforms, and plans. Utilizing cutting-edge technologies, the platform offers resource management, real-time monitoring, remote control, statistical analysis, and intelligent alarm systems. Data access design ensures standardized data collection and exchange, fostering interoperability. A big data storage center manages heterogeneous data sources. The platform interface design emphasizes flexibility and scalability through containerized applications and microservices frameworks, streamlining deployment. The functional design encompasses subsystem configuration access, real-time monitoring, remote access, etc. A detailed evaluation is presented to demonstrate the significance of the proposed platform in terms of functionality, performance, and scalability.
With rapid socio-economic growth and increased energy demand, exploration and exploitation of oil and gas resources have become crucial. Long-term exploitation leads to problems such as pressure drop and production reduction in oil fields, and water injection technology has become a common method to improve these problems. The traditional direct water injection for oil extraction has problems such as high injection cost and low oil recovery efficiency. Therefore, an intelligent control system for different oilfield reservoirs is needed. This study focuses on the layered water injection intelligent system based on advanced sensor technology, digital signal processing and intelligent algorithms. The article reviews the advantages of layered water injection system and the current research status, designs an intelligent control structure including hardware circuits and modular software processes, and adopts adaptive particle swarm optimization algorithm as the core of intelligent control.
In the domain of network communication, network intrusion detection systems (NIDS) play a crucial role in maintaining security by identifying potential threats. NIDS relies on packet inspection, often using rule-based databases to scan for malicious patterns. However, the expanding scale of internet connections hampers the rate of packet inspection. To address this, some systems employ GPU accelerated pattern matching algorithms. Yet, this approach is susceptible to denial of service (DOS) attacks, inducing hashing collisions and slowing inspection. This research introduces a GPU-optimized variation of the Rabin-Karp algorithm, achieving scalability on GPUs while resisting DOS attacks. Our open-source solution (https://github.com/AnasAbbas1/NIDS) combines six polynomial hashing functions, eliminating the need for false-positive validation. This leads to a substantial improvement in inspection speed and accuracy. The proposed system ensures minimal packet misclassification rates, solidifying its role as a robust tool for real-time network security.
Aiming at the problems of existing news recommendation methods, such as inadequate exploration of the semantic information of news, neglecting potential hotspot features of news, and challenging the balance between user preferences and hotspot features, a hotspot-aware personalized news recommendation model (DistilBERT-TC-MA) is suggested, which integrates the distilled version of BERT (DistilBERT), text convolutional neural network (TextCNN), and multilayer attention (MA). First, it takes full advantage of DistilBERT, TextCNN, and self-attention mechanism to achieve news encoding. Following this, representations of trending news are dynamically aggregated using the attention mechanism, while user preferences are mined utilizing user click history. Finally, in order to successfully accomplish the click prediction of candidate news, the hotspot features, user preferences, and candidate news are ultimately combined using a click predictor. The experimental results of the suggested DistilBERT-TC-MA model on MIND dataset are better than several other advanced methods.
As security is a serious concern nowadays, it becomes important to develop a product that deals with security issues without any human intervention. Hence, an automatic security system is a proposed device that ensures the security of the premises. Using both emerging technologies and specialized hardware, we can achieve safety goals and be able to develop the proposed device. It is an IoT-based approach that includes cloud computing, OpenCV, and web application for developing a security-based automatic system. Using raspberry pi and software, the authors design an automated security system where all the used electrical items are controlled. This system deals with the protection of possessions, minimizing break-ins, and avoiding any dangerous situations. The additional salient feature is that it also deals with the COVID-19 alerts, which are generated from the temperature sensor. Therefore, it protects the premises not only from any unauthorized access but also protects the premises from any infected person.
Ubiquitous environments are not fixed in time. Entities are constantly evolving; they are dynamic. Ubiquitous applications therefore have a strong need to adapt during their execution and react to the context changes, and developing ubiquitous applications is still complex. The use of the separation of needs and model-driven engineering present the promising solutions adopted in this approach to resolve this complexity. The authors thought that the best way to improve efficiency was to make these models intelligent. That's why they decided to propose an architecture combining machine learning with the domain of modeling. In this article, a novel tool is proposed for the design of ubiquitous applications, associated with a graphical modeling editor with a drag-drop palette, which will allow to instantiate in a graphical way in order to obtain platform independent model, which will be transformed into platform specific model using Acceleo language. The validity of the proposed framework has been demonstrated via a case study of COVID-19.
Blockchain technology has revolutionized various sectors such as trade finance, education, healthcare, the internet of things (IoT), and user identification with its groundbreaking potential. Its transformative influence on privacy, data integrity, and transactional reliability has significantly enhanced user authentication sharing across industries. Consequently, there is a pressing demand for a robust framework capable of providing seamless authentication between devices, cloud servers, and IoT base stations. This article presents into the critical need for such a framework, meticulously evaluating its feasibility in light of the scarce existing solutions that meet industry guidelines. The proposed framework reconciles two contrasting perspectives, thoroughly examining 11 distinct factors and highlighting key features uncovered through rigorous research. The findings have implications for the future of secure authentication.
The next generation parallel computers are the keys to achieve exascale performance, whereas sequential computers have already been saturated. In order to achieve this mighty target of exascale computing, one of the main challenges is the reduction of power consumption along with achieving suitable performance. Energy efficiency is a key feature to ensure the trade-off between the performances over the required power usages. Hence, to focus on those issues, the target of this article is to analyze the performance versus power usage trade-off for the conventional networks like- Mesh and Torus. High degree networks show much better performance than the low degree of networks. However, high degree networks require higher power usage for their high degree of interconnected links. This article showed that with zero load latency, the 3D Torus could show about 57.07% better performance than a 2D Torus. On the other hand, a 2D Mesh network requires about 24.22% less router power usage than the 3D Mesh, & 5D Torus requires about 66.8% higher router power usage than a 3D Torus network.
In this paper, the authors have explained a time limited travelling salesman problem (TSP) where a time limit is associated with each city. The traveller must reach each city on or before the predetermined time limit (that is fixed for each city) in his/her tour. Travel cost is also a parameter for the proposed model. This time limit indicates the maximum time unit by which the traveller must reach a particular city. Here, travel cost is the objective of the problem. Moreover, total travel time is also fixed for a complete tour. This research recently introduced this time limit for each of the cities. The proposed TSP is solved by using a genetic algorithm-based method. The cyclic crossover and special mutation operations have been adapted to GA for solving the proposed TSP. To show the effectiveness of proposed algorithm, the authors have considered some benchmark instances. Then the authors redefine a few benchmark instances for the proposed TSP. Computational results with different data sets are presented.
