Search Results (8,255)

With the deep integration of Internet of Vehicles (IoV) and edge computing technologies, the spatiotemporal dynamics, burstiness, and load fluctuations of user requests pose severe challenges to microservices auto-scaling. Existing static or periodic resource adjustment strategies struggle to adapt to IoV edge environments and often neglect service dependencies and multi-objective optimization synergy, failing to fully utilize implicit regularities like the symmetry in spatiotemporal patterns. This paper proposes a dual-phase dynamic scaling mechanism: for long-term scaling, the Spatio-Temporal Graph Transformer (STGT) is employed to predict traffic flow by capturing correlations in spatial–temporal distributions of vehicle movements, and the improved Multi-objective Graph-based Proximal Policy Optimization (MGPPO) algorithm is applied for proactive resource optimization, balancing trade-offs among conflicting objectives. For short-term bursts, the Fast Load-Aware Auto-Scaling algorithm (FLA) enables rapid instance adjustment based on the M/M/S queuing model, maintaining balanced load distribution across edge nodes—a feature that aligns with the principle of symmetry in system design. The model comprehensively considers request latency, resource consumption, and load balancing, using a multi-objective reward function to guide optimal strategies. Experiments show that STGT significantly improves prediction accuracy, while the combination of MGPPO and FLA reduces request latency and enhances resource utilization stability, validating its effectiveness in dynamic IoV environments. Full article

Compared to rigidly-backed absorbers, the selection of appropriate optimization techniques for the optimal design of broadband acoustic mufflers remains under-investigated. This study determines the most effective optimization strategy for maximizing the total dissipation of rainbow-trapping silencers (RTSs), composed of graded side-branch cavities that enable broadband dissipation of sound through visco-thermal effects. Model-based and data-driven optimization strategies are compared, particularly in high-dimensional design spaces with flat cost function landscapes where gradient-based approaches are inadequate. It is found that model-based particle swarm optimization (PSO) outperforms simulated annealing, genetic algorithm, and surrogate method in maximizing RTS total dissipation, especially in high-dimensional designs. PSO uniquely handles flat or valleyed cost landscapes through efficient exploration–exploitation trade-offs. Data-driven approaches using Bayesian regularization neural networks (BRNNs) drastically reduce computational cost in high-dimensional spaces, though they require large datasets to avoid over-smoothing. In low dimensions, direct optimization on BRNN outputs suffices, making global search unnecessary. Both model-based and BRNN methods show robustness to input errors, but data-driven approaches handle output noise better. These findings, validated using transfer matrix models, offer strategic guidance for selecting optimization methods, especially when using computationally expensive visco-thermal finite element simulations. Full article

Gotland has been designated by the Swedish government as a pilot region for the transition to a sustainable, fossil-free energy system by 2030. This transformation emphasizes local renewable energy production and system independence. Within this context, this study investigates the role of industrial waste heat as a resource to improve energy efficiency and support sector integration between electricity, heating, and industry. A mixed-methods approach was used, combining techno-economic energy system modeling, life cycle assessment, spatial GIS data, and stakeholder input. The study develops and analyzes future carbon-neutral energy scenarios for Gotland’s energy system. Industrial waste heat can significantly reduce primary energy demand, particularly in scenarios with expanded industry, carbon capture, and increased sector integration—such as through district heating. In such cases, up to 3000–4000 GWh/year of low-temperature industrial residual heat becomes available, offering substantial potential to improve overall energy efficiency. The scenarios highlight synergies and trade-offs across environmental, economic, and social dimensions, emphasizing the importance of coordinated planning. Scenarios with offshore wind enable energy exports and industrial growth but raise challenges related to emissions and public acceptance, while scenarios without cement production reduce environmental impact but weaken local economic resilience. Limitations of the study include the exclusion of global supply chain impacts and assumptions about future technological costs. The study underscores the need for integrated planning, regulatory innovation, and stakeholder collaboration to ensure a just and resilient transition for Gotland. Full article

Intermediate-temperature solid oxide fuel cells (IT-SOFCs) have attracted attention due to their potential to overcome the trade-off between the performance and lifetime of SOFC devices. However, the guiding principle for effective material design, which can reduce operating temperatures and overcome performance decreases caused by excessive overpotential on the anode surface, has not been clearly established. In the present work, we studied the reported Schottky anomaly, which has been observed exclusively in yttria-stabilized zirconia (YSZ). To investigate this phenomenon, a small amount (less than 1200 ppm) of trivalent cations (Rh³⁺ or Fe³⁺), chemically similar to Y³⁺ in Y₂O₃, was doped onto the YSZ surface in the anode layer. Then, the current density observed from the SOFC device at 973 K was found to be nine-times higher than the SOFC device with an undoped anode. The surface first-principles calculations in the present work indicate that this performance enhancement is caused by the delocalized electrons induced by trivalent cation doping in the vicinity of the three-phase boundary and the promotion of surface oxygen diffusion in YSZ. Based on all experimental data, the effective material design guiding principle was obtained for utilizing the unique physical property of YSZ for applications such as IT-SOFCs. Full article

Urban building energy retrofit (UBER) is a critical strategy for advancing the low-carbon and climate-resilience transformation of cities. The integration of machine learning (ML), data-driven clustering, and multi-objective optimization (MOO) is a key aspect of artificial intelligence (AI) that is transforming the process of retrofit decision-making. This integration enables the development of scalable, cost-effective, and robust solutions on an urban scale. This systematic review synthesizes recent advances in AI-driven MOO frameworks for UBER, focusing on how state-of-the-art methods can help to identify and prioritize retrofit targets, balance energy, cost, and environmental objectives, and develop transparent, stakeholder-oriented decision-making processes. Key advances highlighted in this review include the following: (1) the application of ML-based surrogate models for efficient evaluation of retrofit design alternatives; (2) data-driven clustering and classification to identify high-impact interventions across complex urban fabrics; (3) MOO algorithms that support trade-off analysis under real-world constraints; and (4) the emerging integration of explainable AI (XAI) for enhanced transparency and stakeholder engagement in retrofit planning. Representative case studies demonstrate the practical impact of these approaches in optimizing envelope upgrades, active system retrofits, and prioritization schemes. Notwithstanding these advancements, considerable challenges persist, encompassing data heterogeneity, the transferability of models across disparate urban contexts, fragmented digital toolchains, and the paucity of real-world validation of AI-based solutions. The subsequent discussion encompasses prospective research directions, with particular emphasis on the potential of deep learning (DL), spatiotemporal forecasting, generative models, and digital twins to further advance scalable and adaptive urban retrofit. Full article

This paper presents a fast and robust approach to evaluate the singular values of small (e.g., $4\times 4$, $5\times 5$) matrices on single- and multi-Graphics Processing Unit (GPU) systems, enabling the modulation of the accuracy–speed trade-off. Targeting applications that require only computations of the SVs in electromagnetics (e.g., Multiple Input Multiple Output—MIMO link capacity optimization) and emerging deep-learning kernels, our method contrasts with existing GPU singular value decomposition (SVD) routines by computing singular values only, thereby reducing overhead compared to full-SVD libraries such as cuSOLVER’s gesvd and MKL’s desvg. The method uses four steps: interlaced storage of the matrices in GPU global memory, bidiagonalization via Householder transformations, symmetric tridiagonalization, and root finding by bisection using Sturm sequences. We implemented the algorithm in CUDA and evaluated it on different single- and multi-GPU systems. The approach is particularly suited for the analysis and design of multiple-input/multiple-output (MIMO) communication links, where thousands of tiny SVDs must be computed rapidly. As an example of the satisfactory performance of our approach, the speed-up reached for large matrix batches against cuSOLVER’s gesvd has been around 20 for $4\times 4$ matrices. Furthermore, near-linear scaling across multi-GPUs systems has been reached, while maintaining root mean square errors below $2.3\times {10}^{-7}$ in single precision and below $2.3\times {10}^{-13}$ in double precision. Tightening the tolerance from $\delta ={10}^{-7}$ to $\delta ={10}^{-9}$ increased the total runtime by only about 10%. Full article

Recent advances in vision–language models (VLMs) have transformed the field of robotics. Researchers are combining the reasoning capabilities of large language models (LLMs) with the visual information processing capabilities of VLMs in various domains. However, most efforts have focused on terrestrial robots and are limited in their applicability to volatile environments such as ocean surfaces and underwater environments, where real-time judgment is required. We propose a system integrating the cognition, decision making, path planning, and control of autonomous marine surface vehicles in the ROS2–Gazebo simulation environment using a multimodal vision–LLM system with zero-shot prompting for real-time adaptability. In 30 experiments, adding the path plan mode feature increased the success rate from 23% to 73%. The average distance increased from 39 m to 45 m, and the time required to complete the task increased from 483 s to 672 s. These results demonstrate the trade-off between improved reliability and reduced efficiency. Experiments were conducted to verify the effectiveness of the proposed system and evaluate its performance with and without adding a path-planning step. The final algorithm with the path-planning sub-process yields a higher success rate, and better average path length and time. We achieve real-time environmental adaptability and performance improvement through prompt engineering and the addition of a path-planning sub-process in a limited structure, where the LLM state is initialized with every application programming interface call (zero-shot prompting). Additionally, the developed system is independent of the vision–LLM archetype, making it scalable and adaptable to future models. Full article

While electroencephalography is extremely useful for studying brain activity, EEG data is always contaminated by a wide range of artefacts. Many techniques exist to identify and remove such artefacts, primarily offline, with and without human supervision and intervention. This research presents a novel, fully automated online wavelet-based learning adaptive denoiser for artefact identification and mitigation in EEG signals. It contributes to knowledge by offering a framework that can be instantiated with artefact-specific and context-dependent parameters. In detail, this framework is instantiated for block online muscle artefact identification and mitigation. It is based on the discrete wavelet transformation (DWT) for time–frequency enrichment and the Isolation Forest algorithm for linearly learning data characteristics and identifying anomalous activity in a sliding moving buffer. It is built upon a denoising strategy that operates in the domain of DWT coefficients before reverting characteristics to the time domain. The findings demonstrate that such instantiation is promising in its goal of successfully identifying myogenic muscle movements and transforming them into cleaner EEG signals. They also emphasise the difficulties in tackling the known problem of the cone of influence associated with wavelet transformation and the tradeoff between the length of consecutive EEG windows and the problem’s real-time nature. Full article

Aquifer Thermal Energy Storage (ATES) is a promising technology for the seasonal storage of heat, thereby bridging the temporal gap between summer surpluses and peak winter demand. However, the efficiency of conventional ATES systems is severely compromised in aquifers with high groundwater flow velocities, as advective heat transport leads to significant storage losses. This study explores a novel three-well concept that implements an active hydraulic barrier, created by an additional extraction well upstream of the ATES doublet. This well effectively disrupts the regional groundwater flow, thereby creating a localized zone of stagnant or significantly reduced flow velocity, to protect the stored heat. A comprehensive parametric study was conducted using numerical simulations in FEFLOW. The experiment systematically varied three key parameters: groundwater flow velocity, the distance of the third well and its pumping rate. The performance of the system was evaluated based on its thermal recovery efficiency and a techno-economic analysis. The findings indicate that the hydraulic barrier effectively enhances heat recovery, surpassing twice the efficiency observed in a conventional two-well configuration (100 m/a). The analysis reveals a critical trade-off between hydraulic containment and thermal interference through hydraulic short-circuiting. The techno-economic assessment indicates that the three-well concept has the potential to generate significant cost and CO₂e savings. These savings greatly exceed the additional capital and operational costs in comparison to a traditional doublet system in the same conditions. In conclusion, the three-well ATES system can be considered a robust technical and economic solution for expanding HT-ATES to sites with high groundwater velocities; however, its success depends on careful, model-based design to optimize these competing effects. Full article

With the increasing deployment of Unmanned Aerial Vehicle (UAV) swarms in uncertain and dynamically changing environments, optimal cooperative control has become essential for ensuring robust and efficient system coordination. To this end, this paper designs a data-driven optimal bipartite containment tracking control scheme for multi-UAV systems under compound uncertainties. A novel Dynamic Iteration Regulation Strategy (DIRS) is proposed, which enables real-time adjustment of the learning iteration step according to the task-specific demands. Compared with conventional fixed-step data-driven algorithms, the DIRS provides greater flexibility and computational efficiency, allowing for better trade-offs between the performance and cost. First, the optimal bipartite containment tracking control problem is formulated, and the associated coupled Hamilton–Jacobi–Bellman (HJB) equations are established. Then, a data-driven iterative policy learning algorithm equipped with the DIRS is developed to solve the optimal control law online. The stability and convergence of the proposed control scheme are rigorously analyzed. Furthermore, the control law is approximated via the neural network framework without requiring full knowledge of the model. Finally, numerical simulations are provided to demonstrate the effectiveness and robustness of the proposed DIRS-based optimal containment tracking scheme for multi-UAV systems, which can reduce the number of iterations by 88.27% compared to that for the conventional algorithm. Full article

Space-borne antenna pointing is affected by uncertain disturbances like satellite attitude, structural flexibility, and manufacturing/installation errors. Understanding the effect of these uncertainties is crucial for antenna performance. The main contribution of this paper is the proposal of an uncertainty quantification (UQ) framework for antenna pointing performance that integrates the Global Sensitivity Analysis (GSA) method and Point Estimate Method (PEM), named the GSA-PEM Integrated Framework (GSA-PEM in short). This framework enables systematic analysis of how uncertain parameters (satellite attitude, manufacturing/installation errors, joint rotation, structural deformation, feed displacement, etc.) impact antenna pointing. It establishes a pointing model via coordinate transformation, utilizes the total-effect of the Sobol method to prioritize the key parameters for reliability analysis, and computes pointing performance statistics characteristic via PEM to evaluate pointing reliability. Two case studies are presented to validate the accuracy and efficiency of the proposed framework. Monte Carlo Simulation (MCS) and the Maximum Entropy method using the Fractional-order Moments (ME-FMs) are comparison methods. Results demonstrate that the proposed framework achieves a trade-off between accuracy and efficiency in assessing antenna pointing performance under parameter uncertainty. Full article

The integration of renewable energy resources (RES) into microgrids (MGs) poses significant challenges due to the intermittent nature of generation and the increasing complexity of multi-energy scheduling. To enhance operational flexibility and reliability, this paper proposes an intelligent energy management system (EMS) for MGs incorporating a hybrid hydrogen-battery energy storage system (HHB-ESS). The system model jointly considers the complementary characteristics of short-term and long-term storage technologies. Three conflicting objectives are defined: economic cost (EC), system response stability, and battery life loss (BLO). To address the challenges of multi-objective trade-offs and heterogeneous storage coordination, a novel deep-reinforcement-learning (DRL) algorithm, termed MOATD3, is developed based on a dynamic reward adjustment mechanism (DRAM). Simulation results under various operational scenarios demonstrate that the proposed method significantly outperforms baseline methods, achieving a maximum improvement of 31.4% in SRS and a reduction of 46.7% in BLO. Full article

The increasing integration and miniaturization of electronic devices place serious pressure on packaging technologies to ensure long-term reliability. Polymer underfill encapsulation is a key process for reducing thermomechanical stress in modern assemblies. A systematic analysis that frames its diverse methods as solutions to the fundamental trade-off between the final polymer composite’s thermomechanical performance and its liquid-state processability is lacking from the literature. The novelty of this review lies in establishing a decision-making framework that connects specific application requirements to the underlying material science and process limitations. This article analyzes and compares different underfill techniques through a systematic literature review, from conventional capillary flow to advanced wafer-level underfills. Our findings show that this core trade-off leads to three distinct strategies: (1) Maximum reliability: This is achieved with highly filled, post-applied composites, offering excellent thermomechanical properties at the cost of slow, viscosity-driven manufacturing speeds. (2) High productivity: This is realized through integrated, pre-applied processes that simplify manufacturing but impose significant constraints on the polymer chemistry and filler content. (3) Targeted reinforcement for board-level packages: At the localized positions applied, ductile polymers often enhance mechanical shock resistance. This review concludes that the optimal underfill choice is not universal but is a complex, application-driven decision balancing the cured material’s performance against the processing demands of the polymer system. Full article

Flexible hydrogel sensors demonstrate emerging applications, such as wearable electronics, soft robots, and humidity smart devices, but their further application is limited due to their single-responsive behavior and unstable, low-sensitivity signal output. This study develops a dual-responsive starch-based conductive hydrogel via a facile “one-pot” strategy, achieving mechanically robust pressure sensing and ultra-sensitive humidity detection. The starch-Poly (2,3-dihydrothieno-1,4-dioxin)-poly (styrenesulfonate) (PEDOT:PSS)-glutaraldehyde (SPG) hydrogel integrates physical entanglement and covalent crosslinking to form a porous dual-network architecture, exhibiting high compressive fracture stress (266 kPa), and stable electromechanical sensitivity (ΔI/I₀, ~2.3) with rapid response (0.1 s). In its dried state (D-SPG), the film leverages the starch’s hygroscopicity for humidity sensing, detecting minute moisture changes (ΔRH = 6.6%) within 120 ms and outputting 0.4~0.5 (ΔI/I₀) signal amplitudes. The distinct state-dependent responsiveness enables tailored applications: SPG monitors physiological motions (e.g., pulse waves and joint movements) via conformal skin attachment, while D-SPG integrated into masks quantifies respiratory intensity with 3× signal enhancement during exercise. This work pioneers a sustainable candidate for biodegradable flexible electronics, overcoming trade-off limitations between mechanical integrity, signal stability, and dual responsiveness in starch hydrogels through synergistic network design. Full article

With the rise in cloud computing and virtualisation, secure and efficient VPN solutions are essential for network connectivity. We present a systematic performance comparison of OpenVPN (v2.6.12) and WireGuard (v1.0.20210914) across Azure and VMware environments, evaluating throughput, latency, jitter, packet loss, and resource utilisation. Testing revealed that the protocol performance is highly context dependent. In VMware environments, WireGuard demonstrated a superior TCP throughput (210.64 Mbps vs. 110.34 Mbps) and lower packet loss (12.35% vs. 47.01%). In Azure environments, both protocols achieved a similar baseline throughput (~280–290 Mbps), though OpenVPN performed better under high-latency conditions (120 Mbps vs. 60 Mbps). Resource utilisation showed minimal differences, with WireGuard maintaining slightly better memory efficiency. Security Efficiency Index calculations revealed environment-specific trade-offs: WireGuard showed marginal advantages in Azure, while OpenVPN demonstrated better throughput efficiency in VMware, though WireGuard remained superior for latency-sensitive applications. Our findings indicate protocol selection should be guided by deployment environment and application requirements rather than general superiority claims. Full article