Search Results (4,632)

The dual-carbon objective aspires to enhance China’s medium- and long-term green power trading and facilitate the low-carbon economic operation of park microgrids from both medium- and long-term and spot market perspectives. First, the integration of medium- and long-term green power trading with spot trading was meticulously analyzed, leading to the formulation of a power purchase strategy for park microgrid operators. Subsequently, a sophisticated Bayesian fuzzy learning method was employed to simulate the interaction between supply and demand, enabling the prediction of the price for bilaterally negotiated green power trading. Finally, a comprehensive multi-objective optimization model was established for the synergistic operation of park microgrid in the medium- and long-term green power and spot markets. This model astutely considers factors such as green power trading, distributed photovoltaic generation, medium- and long-term thermal power decomposition, energy storage systems, and power market dynamics while evaluating both economic and environmental benefits. The Levy-based improved bird-flocking algorithm was utilized to address the multi-faceted problem. Through rigorous computational analysis and simulation of the park’s operational processes, the results demonstrate the potential to optimize user power consumption structures, reduce power purchase costs, and promote the green and low-carbon transformation of the park. Full article

Vehicular ad hoc networks (VANETs) enable communication among vehicles and between vehicles and infrastructure to provide safety and comfort to the users. Malicious nodes in VANETs may broadcast false information to create the impression of a fake event or road congestion. In addition, several malicious nodes may collude to collectively launch a false information attack to increase the credibility of the attack. Detection of these attacks is critical to mitigate the potential risks they bring to the safety of users. Existing techniques for detecting false information attacks in VANETs use different approaches such as machine learning, blockchain, trust scores, statistical methods, etc. These techniques rely on historical information about vehicles, artificial data used to train the technique, or coordination among vehicles. To address these limitations, we propose a false information attack detection technique for VANETs using an unsupervised anomaly detection approach. The objective of the proposed technique is to detect false information attacks based on only real-time characteristics of the network, achieving high accuracy and low processing delay. The performance evaluation results show that our proposed technique offers 30% lower data processing delay and a 17% lower false positive rate compared to existing approaches in scenarios with high proportions of malicious nodes. Full article

Low-Earth-Orbit (LEO) satellite networks face unique challenges in service function chain (SFC) orchestration due to their dynamic topology and resource constraints, making traditional terrestrial network solutions inadequate. This study addresses the challenge of maximizing service provider benefits through efficient SFC deployment and readjustment in LEO satellite networks. We propose PPOSFC, a novel dynamic SFC orchestration algorithm based on Proximal Policy Optimization (PPO), which incorporates future topology information into the decision-making process. The algorithm models the orchestration problem as a Markov decision process and employs a dual-objective optimization approach considering both deployment success rate and readjustment costs. Simulation results demonstrate that PPOSFC achieves a 13.07% increase in cumulative profit and improves deployment success rates by 6.78% compared to existing algorithms. The algorithm exhibits superior performance in both high and low user service request intensity, effectively balancing service quality and operational efficiency. Furthermore, our analysis reveals that incorporating predicted topology information significantly enhances orchestration performance. Full article

Fire detection and analysis have been a central focus of numerous studies due to their importance in potentially reducing fire’s harmful impact. Fire detection and classification using artificial intelligence (AI) methods have drawn significant attention in the literature. These methods often tackle certain aspects of fire, such as classifying fire versus non-fire images or detecting smoke or flames. However, these studies lack emphasis on integrating the capabilities of large language models for fire classification. This study explores the potential of large language models, especially ChatGPT-4, in fire classification tasks. In particular, we utilize ChatGPT-4 for the first time to develop a classification approach for fire incidents. We evaluate this approach using two benchmark datasets: the Forest Fire dataset and the DFAN dataset. The results indicate that ChatGPT has significant potential for timely fire classification, making it a promising tool to complement existing fire detection technologies. Furthermore, it has the capability to provide users with more thorough information about the type of burning objects and risk level. By integrating ChatGPT, detection systems can benefit from the rapid analysis capabilities of ChatGPT to enhance response times and improve accuracy. Additionally, its ability to provide context-rich information can support better decision-making during fire episodes, making the system more effective overall. The study also examines the limitations of using ChatGPT for classification tasks. Full article

Background/Objectives: The damage assessment of dental instruments, such as endodontic files, is crucial to ensure patient safety and treatment quality. Conventional scanning electron microscopy (SEM) has been the gold standard for this purpose; however, its limited accessibility and complex sample preparation protocols hinder its routine use in clinical settings. This study proposes a novel system that leverages digital photography and advanced image processing techniques as a viable alternative to SEM. Methods: Our system accurately detects early instrument damage by capitalizing on the high resolution of digital images. Its exceptionally user-friendly interface, portability, and key features make it highly suitable for daily clinical practice. Results: Our findings suggest that the proposed system provides image quality comparable to SEM. Conclusions: Image stacking provides a practical, efficient, and objective method for assessing endodontic instruments’ morphology. By detecting early damage, this system significantly improves the safety and quality of endodontic procedures, especially for reusable NiTi files, instilling confidence and security in its use. It offers a cost-effective and user-friendly alternative to traditional methods such as visual inspection and SEM, making it a comfortable and confident choice for both research and clinical settings. Full article

With the increasing number of satellites and rising user demands, the volume of satellite data transmissions is growing significantly. Existing scheduling systems suffer from unequal resource allocation and low transmission efficiency. Therefore, effectively addressing the large-scale multi-objective satellite data transmission scheduling problem (SDTSP) within a limited timeframe is crucial. Typically, swarm intelligence algorithms are used to address the SDTSP. While these methods perform well in simple task scenarios, they tend to become stuck in local optima when dealing with complex situations, failing to meet mission requirements. In this context, we propose an improved method based on the minimum angle particle swarm optimization (MAPSO) algorithm. The MAPSO algorithm is encoded as a discrete optimizer to solve discrete scheduling problems. The calculation equation of the sine function is improved according to the problem’s characteristics to deal with complex multi-objective problems. This algorithm employs a minimum angle strategy to select local and global optimal particles, enhancing solution efficiency and avoiding local optima. Additionally, the objective space and solution space exhibit symmetry, where the search within the solution space continuously improves the distribution of fitness values in the objective space. The evaluation of the objective space can guide the search within the solution space. This method can solve multi-objective SDTSPs, meeting the demands of complex scenarios, which our method significantly improves compared to the seven algorithms. Experimental results demonstrate that this algorithm effectively improves the allocation efficiency of satellite and ground station resources and shortens the transmission time of satellite data transmission tasks. Full article

Smart cities are complex urban environments that rely on advanced technology and data analytics to enhance city services’ quality of life, sustainability, and efficiency. As these cities continue to evolve, there is a growing need for a structured framework to evaluate and integrate products that align with smart city objectives. This paper introduces the Pentagon Framework, a comprehensive evaluation method designed to ensure that products and their materials meet the specific needs of smart cities. The framework focuses on five key features—smart, sustainable, sensing, social, and safe—collectively called the Penta-S concept. These features provide a structured approach to categorizing and assessing products, ensuring alignment with the city’s goals for efficiency, sustainability, and user experience. The Smart City Pentagon Framework Analyzer is also presented, a dedicated web application that facilitates interaction with the framework. It allows product data input, provides feedback on alignment with the Penta-S features, and suggests personality traits based on the OCEAN model. Complementing the web application, the Smart City Penta-S Compliance Assistant API, developed through ChatGPT, offers a more profound, personalized evaluation of products, including the life cycle phase recommendations using the IPPMD model. This paper contributes to the development of smart city solutions by providing a flexible framework that can be applied to any product type, optimizing its life cycle, and ensuring compliance with the Pentagon Framework. This approach improves product integration and fosters user satisfaction by tailoring products and their materials to meet specific user preferences and needs within the smart city environment. The proposed framework emphasizes citizen-centric design and highlights its advantages over conventional evaluation methods, ultimately enhancing urban planning and smart city development. Full article

Requirements elicitation is a fundamental process in software engineering, essential for aligning software products with user needs and project objectives. As software projects become more complex, effective elicitation methods are vital for capturing accurate and comprehensive requirements. Despite the variety of available elicitation methods, practitioners face persistent challenges such as capturing tacit knowledge, managing diverse stakeholder needs, and addressing ambiguities in requirements. Moreover, although elicitation is recognized as a core process for gathering and analyzing system objectives, there is a lack of a unified and systematic framework to guide practitioners—especially newcomers—through the activity. To address these challenges, we provide a comprehensive analysis of existing elicitation methods, aiming to contribute to better alignment between software products and project objectives, ultimately improving software engineering practices. We do so by performing a systematic literature review identifying crosscutting steps, common techniques, tools, and approaches that define the core activities of the elicitation process. We synthesize our findings into a metamodel that structures software elicitation processes. This review uncovers various elicitation methods—such as collaborative workshops, interviews, and prototyping—each demonstrating unique strengths in different project contexts. It also highlights significant limitations, including stakeholder misalignment and incomplete requirements capture, which continue to reduce the effectiveness of elicitation processes. Finally, our study seeks to contribute to understanding requirements elicitation methods by providing a comprehensive view of their current strengths and limitations through a metamodel enabling the structuring and optimization of elicitation processes. Full article

Human–Machine Interfaces (HMIs) in autonomous driving technology have recently gained significant research interest in public transportation. However, most of the studies are biased towards qualitative methods, while combining quantitative and qualitative approaches has yet to receive commensurate attention in measuring user acceptance of design outcome evaluation. To the best of our knowledge, no standardized test procedure that combines quantitative and qualitative methods has been formed to evaluate and compare the interrelationships between different designs of HMIs and their psychological effects on users. This paper proposes a practical and comprehensive protocol to guide assessments of user acceptance of HMI design solutions. We first defined user acceptance and analyzed the existing evaluation methods. Then, specific ergonomic factors and requirements that the designed output HMI should meet were identified. Based on this, we developed a protocol to evaluate a particular HMI solution from in- and out-of-vehicle perspectives. Our theoretical protocol combines objective and subjective measures to compare users’ behavior when interacting with Autonomous Vehicles (AVs) in a virtual experimental environment, especially in public transportation. Standardized testing procedures provide researchers and interaction designers with a practical framework and offer theoretical support for subsequent studies. Full article

Compared with point-to-point wireless optical communication systems, multi-faceted Optical Base Stations (OBSs) offer the advantages of supporting multi-user access and achieving omnidirectional dynamic communication. The emission structure of the multi-faceted OBSs, which offer wide beam coverage and ensure uniform distribution of optical signals, is essential for users to receive signals while in motion. This paper presents a modeling method for three-dimensional beam coverage based on the multi-faceted OBS architecture. Additionally, a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is used to optimize the configuration of the multi-faceted LED array by adjusting the deflection angles of the LED arrays and the emission half-angle of the LEDs at the OBS. These advancements aim to enhance beam coverage performance while also providing technical support for achieving omnidirectional communication in underwater optical wireless networks (UOWN). Full article

Background/Objectives Successful surgical outcomes in head and neck cancer depend on the accurate identification of resection margins. Effective communication between surgeons and pathologists is critical, but is often jeopardised by challenges in sampling and orienting anatomically complex specimens. This pilot study aims to evaluate the use of 3D scanning of surgical specimens as a tool to improve communication and optimise the pathology sampling process. Methods Two structured light 3D scanners, Cronos Dual and Optor Lab, were used to acquire 3D models of anatomical specimens in both preclinical (cadaver specimens) and clinical contexts (fresh surgical specimens). Surgical margins and critical points were annotated on the digital models. Acquisition quality, operating times and subjective feedback from surgeons and pathologists were evaluated. Results The Optor Lab scanner demonstrated superior image quality, shorter processing times and a more user-friendly interface than the Cronos Dual. Key challenges identified included specimen geometry, surface reflectivity and tissue stability. Feedback from both surgeons and pathologists was positive, highlighting the potential of 3D models to improve the surgical-pathology workflow. Conclusions 3D scanning of surgical specimens provides accurate, detailed digital models that can significantly enhance communication between surgeons and pathologists. This technology shows promise in improving pathological staging and clinical decision making, with further studies required to validate its integration into routine practice. Full article

(1) Background: Completion of the full oral health course of care (CoC) is essential to prevent further deterioration of oral and overall health. Understanding these patterns, particularly in public oral healthcare services, is crucial for improving access to and the delivery of care. This study aims to identify the socio-demographic and clinical characteristics of adult patients who did not complete required dental treatments within a 12-month period at Monash Health Dental Services (MHDS), Melbourne, Victoria. (2) Methods: Data were collected on patients’ course of care (CoC), socio-demographic characteristics, and clinical information from the MHDS Titanium electronic database. This study represents a secondary data analysis from adult patients who attended MHDS between November 2022 and October 2023, excluding emergency dental care visits. Logistic regression analyzed the socio-demographic and clinical variables affecting CoC. (3) Results: Our findings identified several significant predictors of incomplete CoC; being a non-priority group, mental health clients, refugees, and identifying as Aboriginal or Torres Strait Islanders (OR = 1.41; 95% CI: 1.08–1.84). Conversely, speaking a language other than English increased the odds of completing treatment (OR = 0.85; 95% CI: 0.74–0.98). By age, patients in the 36-to-55- or the 56-to-75-year-old age groups were more likely to be in the incomplete group (OR = 1.65; 95% CI: 1.37–1.98; and OR = 1.43; 95% CI: 1.22–1.66, respectively). (4) Conclusions: This study identified predictors of discontinued care, emphasizing accessibility and equitable outcomes for users of public oral healthcare. The findings indicate that the predictors of course of care (CoC) completion differ from barriers to accessing care. This highlights key objectives in public health dentistry, focusing on improving accessibility and promoting equitable oral health outcomes for vulnerable populations. Full article

Augmented reality (AR) offers several advantages in the commercialization of wood products, increasing both the efficiency and the attractiveness of the process of presenting and selling them. The digitization and virtualization of wood features/products for the purpose of their economic valorization represent a significant advance in technology and its application in traditional industries such as wood processing and trade. We present a concrete process of digitization and virtualization of wood features through AR for the purpose of its commercial valorization. Three methods of object scanning are tested: convergent photogrammetry, LiDAR scanning using an iPhone, and handheld scanners. Wood samples with different textures, shapes, and surface properties were used for the research, while each method was tested on a trio of models. The methods showed specific limitations: convergent photogrammetry is time-consuming and prone to human error, LiDAR iPhone scanning provides lower output quality and struggles with reflective surfaces, while handheld scanners are expensive and require additional tools for capturing color. Convergent photogrammetry was evaluated as the optimal and available method for the widest range of users. The 3D models were integrated into the Virtual Wood Market application, created in the Unreal Engine environment. The use of augmented reality in wood product commercialization offers significant benefits, including enhanced material efficiency, improved design and fabrication processes, better supply chain management, and increased customer engagement. These advantages can lead to more sustainable practices and higher customer satisfaction, ultimately driving the success of wood product commercialization. Full article

Simulating the experience of flight is a key objective of virtual reality (VR) technology. To enhance the sense of flying and immersion, we developed Drone Rider, a VR system that simulates free-flight atop a drone. In this study, we investigated whether delivering vibratory stimuli to the user’s feet could improve these sensations. While high-frequency drone propeller vibrations typically induce sensory numbness, alternative vibration patterns were explored. In Experiment 1, participants rated 13 different vibration patterns derived from various mechanical sounds, such as those from chainsaws, motorcycles, and washing machines. The motorcycle-based vibrations were most effective in enhancing both the sense of flight and immersion. In Experiment 2, we synthesized new vibration patterns by superimposing the highest-rated vibrations from Experiment 1, but no combination outperformed the original motorcycle vibration. These findings suggest that vibrations with multiple components below 100 Hz may reduce sensory adaptation and enhance the sense of flight and immersion in VR. This work provides valuable insights for developers aiming to optimize haptic feedback in VR flight simulators. Full article

With the improvement of people’s living standards, the issue of dietary health has received extensive attention. In order to simultaneously meet people’s demands for dietary preferences and nutritional balance, we have conducted research on the issue of personalized food recommendations. For this purpose, we have proposed a hybrid food recommendation model, which can provide users with scientific, reasonable, and personalized dietary advice. Firstly, the collaborative filtering (CF) algorithm is adopted to recommend foods to users; then, the improved Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) is used to adjust the nutritional balance and symmetry of the recommended foods. In view of the existing problems in the current nutritional balance algorithm, such as slow convergence speed and insufficient local search ability, the autonomous optimization (AO) adjustment strategy, the self-adaptive adjustment strategy, and the two-sided mirror principle to optimize boundary strategy are introduced in the MOEA/D. According to the characteristics of the food nutrition regulation problem, an adaptive food regulation (AFR) adjustment strategy is designed to achieve more accurate nutritional regulation. Based on the above improvements, a food nutritional recommendation algorithm based on MOEA/D (FNR-MOEA/D) is proposed. Experiments show that compared with MOPSO, MOABC, and RVEA, FNR-MOEA/D performs more superiorly in solving the problem of nutritional balance in food recommendation. Full article