Search Results (2,101)

Virtual power plants (VPPs) coordinate distributed energy resources (DERs) to collectively meet grid dispatch instructions. When a dispatch command is issued to a VPP, it must be disaggregated optimally among the individual DERs to minimize overall operational costs. However, existing methods for VPP dispatch instruction disaggregation often require solving complex optimization problems for each instruction, posing challenges for real-time applications. To address this issue, we propose a multi-parametric programming-based method that yields an explicit mapping from any given dispatch instruction to an optimal DER-level deployment strategy. In our approach, a parametric optimization model is formulated to minimize the dispatch cost subject to DER operational constraints. By applying Karush–Kuhn–Tucker (KKT) conditions and recursively partitioning the DERs’ adjustable capacity space into critical regions, we derive analytical expressions that directly map dispatch instructions to their corresponding resource allocation strategies and optimal scheduling costs. This explicit solution eliminates the need to repeatedly solve the optimization problem for each new instruction, enabling fast real-time dispatch decisions. Case study results verify that the proposed method effectively achieves the cost-efficient and computationally efficient disaggregation of dispatch signals in a VPP, thereby improving its operational performance. Full article

Graphene, a remarkable two-dimensional material, enhances the mechanical properties of high-entropy alloys as a reinforcing phase. This study investigated the influence of vacancy defects in graphene on the strengthening effect of FeNiCrCoCu high-entropy alloy through molecular dynamics simulations. The findings reveal that vacancy defects diminish graphene’s strength, resulting in its premature failure. In tensile tests, graphene with defects lowers the yield stress of the composite, yet it retains the ability to impede dislocations. Conversely, graphene exhibits a more pronounced strengthening effect during compression. Specifically, when the deletion of C atoms is less than 1%, the impact is negligible; between 1% and 6%, the strengthening effect diminishes; and when it surpasses 6%, the strengthening effect virtually ceases to exist. This research offers a theoretical foundation for optimizing graphene-reinforced composites. Full article

The incorporation of digital health into the medical curricula is becoming more important to better prepare doctors in the future. Digital health comprises a wide range of tools such as electronic health records, health information technology, telemedicine, telehealth, mobile health applications, wearable devices, artificial intelligence, and virtual reality. The present study aimed to explore the medical and biomedical students’ perspectives on the integration of digital health in medical curricula. A cross-sectional study was conducted on the medical and biomedical undergraduate students at the College of Medicine and Health Sciences at Sultan Qaboos University. Data was collected using a self-administered questionnaire. The response rate was 37%. The majority of respondents were in the MD (Doctor of Medicine) program (84.4%), while 29 students (15.6%) were from the BMS (Biomedical Sciences) program. A total of 55.38% agreed that they were familiar with the term ‘e-Health’. Additionally, 143 individuals (76.88%) reported being aware of the definition of e-Health. Specifically, 69 individuals (37.10%) utilize e-Health technologies every other week, 20 individuals (10.75%) reported using them daily, while 44 individuals (23.66%) indicated that they never used such technologies. Despite having several benefits, challenges exist in integrating digital health into the medical curriculum. There is a need to overcome the lack of infrastructure, existing educational materials, and digital health topics. In conclusion, embedding digital health into medical curricula is certainly beneficial for creating a digitally competent healthcare workforce that could help in better data storage, help in diagnosis, aid in patient consultation from a distance, and advise on medications, thereby leading to improved patient care which is a key public health priority. Full article

Background/Objectives: In the last few decades, the dengue virus, a prevalent flavivirus, has demonstrated various epidemiological, economic, and health impacts around the world. Dengue virus serotype 2 (DENV2) plays a vital role in dengue-associated mortality. The RNA-dependent RNA polymerase (RdRp) of DENV2 is a charming druggable target owing to its crucial function in viral reproduction. In recent years, streptomycetes natural products (NPs) have attracted considerable attention as a potential source of antiviral drugs. Methods: Seeking prospective inhibitors that inhibit the DENV2 RdRp allosteric site, in silico mining of the Streptome database was executed. AutoDock4.2.6 software performance in predicting docking poses of the inspected inhibitors was initially conducted according to existing experimental data. Upon the assessed docking parameters, the Streptome database was virtually screened against DENV2 RdRp allosteric site. The streptomycetes NPs with docking scores less than the positive control (68T; calc. −35.6 kJ.mol⁻¹) were advanced for molecular dynamics simulations (MDS), and their binding affinities were computed by employing the MM/GBSA approach. Results: SDB9818 and SDB4806 unveiled superior inhibitor activities against DENV2 RdRp upon MM/GBSA//300 ns MDS than 68T with ΔG_binding values of −246.4, −242.3, and −150.6 kJ.mol⁻¹, respectively. A great consistency was found in both the energetic and structural analyses of the identified inhibitors within the DENV2 RdRp allosteric site. Furthermore, the physicochemical characteristics of the identified inhibitors demonstrated good oral bioavailability. Eventually, quantum mechanical computations were carried out to evaluate the chemical reactivity of the identified inhibitors. Conclusions: As determined by in silico computations, the identified streptomycetes NPs may act as DENV2 RdRp allosteric inhibitors and mandate further experimental assays. Full article

The increasing complexity of modern automotive lighting systems requires advanced validation strategies that ensure both functional performance and regulatory compliance. This study presents a structured integration of Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) testing within a digital twin (DT) framework for validating headlamp systems. A gated validation process (G10–G120) is proposed, aligning each development phase with corresponding simulation stages from early requirements and concept validation to real-world scenario testing and continuous integration. A key principle of this approach is the adoption of a framework built upon the V-Cycle, adapted to integrate DT technology with SiL and HiL workflows. This architectural configuration ensures a continuous data flow between the physical system, the digital twin, and embedded software components, enabling real-time feedback, iterative model refinement, and traceable system verification throughout the development lifecycle. The paper also explores strategies for effective DT integration, such as digital twin-as-a-service, which combines virtual testing with physical validation to support earlier fault detection, streamlined simulation workflows, and reduced dependency on physical prototypes during lighting system development. Unlike the existing literature, which often treats SiL, HiL, and DTs in isolation, this work proposes a unified, domain-specific validation framework. The methodology addresses a critical gap by aligning simulation-based testing with development milestones and regulatory standards, offering a foundation for industrial adoption. Full article

Blended and virtual learning have become an integral part in international higher education, especially in the wake of the COVID-19 pandemic and the European Union’s Digital Education Action Plan. These modalities have enabled more inclusive, flexible, and sustainable forms of international collaboration, such as Collaborative Online International Learning (COIL) and Blended Intensive Programs (BIPs), reshaping the landscape of global academic mobility. This paper introduces the INVITE Learning Design Framework (LDF), developed to support higher education instructors in designing high-quality, internationalized blended and virtual learning experiences. The framework addresses the growing need for structured, theory-informed approaches to course design that foster student engagement, intercultural competence, and motivation in non-face-to-face settings. The INVITE LDF was developed through a rigorous scoping review of existing models and frameworks, complemented by needs-identification analysis and desk research. It integrates Self-Determination Theory, Active Learning principles, and the ADDIE instructional design model to provide a comprehensive, adaptable structure for course development. The framework was successfully implemented in a large-scale online training module for over 1000 educators across Europe. Results indicate that the INVITE LDF enhances educators’ ability to create engaging, inclusive, and pedagogically sound international learning environments. Its application supports institutional goals of internationalization by making global learning experiences more accessible and scalable. The findings suggest that the INVITE LDF can serve as a valuable tool for higher education institutions worldwide, offering a replicable model for fostering intercultural collaboration and innovation in digital education. This contributes to the broader transformation of international higher education, promoting equity, sustainability, and global citizenship through digital pedagogies. Full article

Swarm robots often need to cover the designated area to complete specific tasks. While robots possess local perception and limited communication capabilities, they struggle to handle coverage issues in dynamic environments. This paper proposes a self-organizing algorithm for swarm robots based on Dynamic Virtual Force (DVF) to cover dynamic areas. Robots in the swarm can locally perceive their surrounding robots and dynamically select adjacent ones to generate virtual repulsion, thereby controlling their movement. The algorithm enables swarm robots to be rapidly and evenly deployed in unknown areas, adapt to dynamic area changes, and solve the problem of symmetrical robot distribution during coverage. It also allows for adaptive coverage of different density areas, divided as needed. Experimental validation across 20 benchmark scenarios (including obstacles, dynamic boundaries, and multi-density zones) demonstrates that the DVF method outperforms existing approaches in coverage rate, total robot movement distance, and coverage uniformity. The results validate its effectiveness and superiority in addressing area coverage problems. By addressing these challenges, the DVF algorithm can be widely applied to forest firefighting, oil spill cleanup in the ocean, and other swarm robot tasks. Full article

This paper addresses the challenge of fostering cooperation among virtual power plant (VPP) operators in competitive electricity markets, focusing on the application of evolutionary game theory (EGT) and static reward–punishment mechanisms. This investigation resolves four critical questions: the minimum reward–punishment thresholds triggering stable cooperation, the influence of initial market composition on equilibrium selection, the sufficiency of static versus dynamic mechanisms, and the quantitative mapping between regulatory parameters and market outcomes. The study establishes the mathematical conditions under which static reward–punishment mechanisms transform competitive VPP markets into stable cooperative systems, quantifying efficiency improvements of 15–23% and renewable integration gains of 18–31%. Through rigorous evolutionary game-theoretic analysis, we identify critical parameter thresholds that guarantee cooperation emergence, resolving longstanding market coordination failures documented across multiple jurisdictions. Numerical simulations and sensitivity analysis demonstrate that static reward–punishment systems enhance cooperation, optimize resources, and increase renewable energy utilization. Key findings include: (1) Reward–punishment mechanisms effectively promote cooperation and system performance; (2) A critical region exists where cooperation dominates, enhancing market outcomes; and (3) Parameter adjustments significantly impact VPP performance and market behavior. The theoretical contributions of this research address documented market failures observed across operational VPP implementations. Our findings provide quantitative foundations for regulatory frameworks currently under development in seven national energy markets, including the European Union’s proposed Digital Single Market for Energy and Japan’s emerging VPP aggregation standards. The model’s predictions align with successful cooperation rates achieved by established VPP operators, suggesting practical applicability for scaled implementations. Overall, through evolutionary game-theoretic analysis of 156 VPP implementations, we establish precise conditions under which static mechanisms achieve 85%+ cooperation rates. Based on this, future work could explore dynamic adjustments, uncertainty modeling, and technologies like blockchain to further improve VPP resilience. Full article

The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. Full article

Deep learning-based super-resolution (SR) frameworks are widely used in remote sensing applications. However, existing SR models still face limitations, particularly in recovering contours, fine features, and textures, as well as in effectively integrating channel information. To address these challenges, this study introduces a novel residual model named OARN (Optimized Attention Residual Network) specifically designed to enhance the visual quality of low-resolution images. The network operates on the Y channel of the YCbCr color space and integrates LKA (Large Kernel Attention) and OCM (Optimized Convolutional Module) blocks. These components can restore large-scale spatial relationships and refine textures and contours, improving feature reconstruction without significantly increasing computational complexity. The performance of OARN was evaluated using satellite images from WorldView-2, GaoFen-2, and Microsoft Virtual Earth. Evaluation was conducted using objective quality metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Edge Preservation Index (EPI), and Perceptual Image Patch Similarity (LPIPS), demonstrating superior results compared to state-of-the-art methods in both objective measurements and subjective visual perception. Moreover, OARN achieves this performance while maintaining computational efficiency, offering a balanced trade-off between processing time and reconstruction quality. Full article

Successful dental implant therapy relies on accurate planning and placement, e.g., through static, computer-aided implant surgery using CAD/CAM-fabricated surgical guides. This study examined production methods’ influence on surgical guide sleeve housing geometry. A model with two edentulous spaces was digitized using intraoral scanning and CBCT, and two virtually positioned implants were planned. Ten guides per group were produced using milling (MCX5), DLP printing (ASIGA and SHERA), and SLA printing (FORM), printing with 50 µm and 100 µm layers each. Each guide (n = 70) was then digitized using an industrial scanner before and after sterilization. Superimposition of the actual guide data with the reference data allowed for evaluation of deviations at the drill sleeve housing along the x-, y-, z-, and dxyz-axes. Descriptive and statistical evaluation was performed (significance level: p ≤ 0.0125). Significant differences existed among the production methods: Milling and SLA showed higher deviations than the DLP group (p < 0.001). Milled guides post-sterilization showed the highest deviations (0.352 ± 0.08 mm), while one DLP printer at 50 μm layer thickness showed lowest deviations (0.091 ± 0.04 mm). The layer thickness was insignificant, whereas sterilization increased deviation (p < 0.001). DLP produced the most precise implant surgical guides. All 3D printers were suitable for fabricating clinically acceptable surgical guides. Full article

Network security courses rely on hands-on labs where students configure virtual Linux networks to practice attack and defense. Automated feedback is scarce because no standard exists for exchanging detailed configurations—interfaces, bridging, routing tables, iptables policies—between exercise software and large language models (LLMs) that could serve as tutors. We address this interoperability gap with an exercise-oriented YANG profile that augments the Internet Engineering Task Force (IETF) ietf-network module with a new network-devices module. The profile expresses Linux interface settings, routing, and firewall rules, and tags each node with roles such as linux-server or linux-firewall. Integrated into our LiNeS Cloud platform, it enables LLMs to both parse and generate machine-readable network states. We evaluated the profile on four topologies—from a simple client–server pair to multi-subnet scenarios with dedicated security devices—using ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash. Across 1050 evaluation tasks covering profile understanding (n = 180), instance analysis (n = 750), and instance generation (n = 120), the three LLMs answered correctly in 1028 cases, yielding an overall accuracy of 97.9%. Even with only minimal follow-up cues (≦3 turns) —rather than handcrafted prompt chains— analysis tasks reached 98.1% accuracy and generation tasks 93.3%. To our knowledge, this is the first exercise-focused YANG profile that simultaneously captures Linux/iptables semantics and is empirically validated across three proprietary LLMs, attaining 97.9% overall task accuracy. These results lay a practical foundation for artificial intelligence (AI)-assisted security labs where real-time feedback and scenario generation must scale beyond human instructor capacity. Full article

Virtual power plants (VPPs) enable the efficient participation of distributed renewable energy resources in electricity markets by aggregating them. However, the profitability of VPPs is challenged by market volatility and regulatory constraints, such as price caps and imbalance penalties. This study examines the joint impact of varying price cap levels and imbalance penalty structures on the bidding strategies and revenues of VPPs. A stochastic optimization model was developed, where a three-stage scenario tree was utilized to capture the uncertainty in electricity prices and renewable generation output. Simulations were performed under various market conditions using real-world price and generation data from the Korean electricity market. The analysis reveals that higher price cap coefficients lead to greater revenue and more segmented bidding strategies, especially under asymmetric penalty structures. Segment-wise analysis of bid price–quantity pairs shows that over-bidding is preferred under upward-only penalty schemes, while under-bidding is preferred under downward-only ones. Notably, revenue improvement tapers off beyond a price cap coefficient of 0.8, which indicates that there exists an optimal threshold for regulatory design. The findings of this study suggest the need for coordination between price caps and imbalance penalties to maintain market efficiency while supporting renewable energy integration. The proposed framework also offers practical insights for market operators and policymakers seeking to balance profitability, adaptability, and stability in VPP-integrated electricity markets. Full article

Lidar technology is a key sensor for autonomous driving due to its precise environmental perception. However, adverse weather and atmospheric conditions involving fog, rain, snow, dust, and smog can impair lidar performance, leading to potential safety risks. This paper introduces a comprehensive methodology to simulate lidar systems under such conditions and validate the results against real-world experiments. Existing empirical models for the extinction and backscattering of laser beams are analyzed, and new models are proposed for dust storms and smog, derived using Mie theory. These models are implemented in the CARLA simulator and evaluated using Robot Operating System 2 (ROS 2). The simulation methodology introduced allowed the authors to set up test experiments replicating real-world conditions, to validate the models against real-world data available in the literature, and to predict the performance of the lidar system in all weather conditions. This approach enables the development of virtual test scenarios for corner cases representing rare weather conditions to improve robustness and safety in autonomous systems. Full article

With the continuous development of the Internet, people have put forward higher requirements for the stability and availability of the network. Although we constantly strive to take measures to avoid network failures, it is undeniable that network failures are unavoidable. Therefore, in this situation, enhancing the stability and reliability of the network to cope with possible network failures has become particularly crucial. Therefore, researching and developing high fault protection rate intra-domain routing protection schemes has become an important topic and is the subject of this study. This study aims to enhance the resilience and service continuity of networks in the event of failures by proposing innovative routing protection strategies. The existing methods, such as Loop Free Alternative (LFA) and Equal Cost Multiple Paths (ECMP), have some shortcomings in terms of fast fault detection, fault response, and fault recovery processes, such as long fault recovery time, limitations of routing protection strategies, and requirements for network topology. In response to these issues, this article proposes a new routing protection scheme, which is an intra-domain routing protection scheme based on the minimum cross-degree backup path. The core idea of this plan is to find the backup path with the minimum degree of intersection with the optimal path, in order to avoid potential fault areas and minimize the impact of faults on other parts of the network. Through comparative analysis and performance evaluation, this scheme can provide a higher fault protection rate and more reliable routing protection in the network. Especially in complex networks, this scheme has more performance and protection advantages than traditional routing protection methods. The proposed scheme in this paper exhibits a high rate of fault protection across multiple topologies, demonstrating a fault protection rate of 1 in the context of real topology. It performs commendably in terms of path stretch, evidenced by a figure of 1.06 in the case of real topology Ans, suggesting robust path length control capabilities. The mean intersection value is 0 in the majority of the topologies, implying virtually no common edge between the backup and optimal paths. This effectively mitigates the risk of single-point failure. Full article