Search Results (7,384)

This study presents a systematic analysis of the stability and roll characteristics of an Oscillating Water Column (OWC) wave energy buoy. By integrating theoretical derivation and AQWA simulation, the research identifies thirteen possible heeling states of OWC buoy, focusing on five representative states applicable to the current design. A novel segmented-integration model is proposed to compute the centre of buoyancy and righting moment for the hollow-annular OWC buoy, accurately capturing the evolution of static and dynamic stability across heel angles from 0° to 90°. Results show that the buoy has an initial metacentric height of 0.33 m, a maximum righting arm of 0.713 m, a limiting static heel angle of 77°, and a minimum capsizing moment of 22,887 N·m—all significantly exceeding regulatory requirements. The roll natural period ranges from 5.8 to 7.7 s, with a tuning factor above 1.3, effectively avoiding resonance with typical wave periods in the target sea area. The buoy demonstrates excellent dynamic stability and capsize resistance. This study fills a gap in OWC buoy stability analysis and provides a practical guidance for the safe design of wave energy devices. Full article

The transition to sustainable energy systems increasingly relies on advanced optimization methods to address the challenges of designing and operating them efficiently. Seasonal storage systems play a pivotal role in aligning renewable energy generation with fluctuating energy demand, with ice storage emerging as a promising solution for seasonal energy storage. This paper presents a novel optimization framework for the operation of seasonal ice-storage systems, leveraging Mixed-Integer Linear Programming (MILP) with time-series aggregation (TSA) techniques. The proposed model accurately captures the physical behavior of ice storage, incorporating both latent and sensible heat storage phases, discrete temperature levels, and charging/discharging efficiency curves. A key feature of this framework is its ability to address computational challenges in large-scale optimization, while maintaining high detail. Using a business park in Germany as a case study, the results demonstrate a significant reduction in computational time of up to 80% for 110 typical periods, with only a 2.5% deviation in the objective value and 9% in the Seasonal Energy Efficiency Ratio (SEER), although this efficiency gain depends on the number of typical periods used. This work addresses key gaps in seasonal ice-storage optimization models and provides a robust tool for designing and optimizing sustainable energy systems. Full article

The Internet of Things (IoT) has become deeply embedded in modern society, enabling applications across smart homes, healthcare, industrial automation, and environmental monitoring. However, as billions of interconnected devices continuously collect and exchange sensitive data, privacy and security concerns have escalated. This survey systematically reviews the state-of-the-art privacy-preserving techniques in IoT systems, emphasizing approaches that protect user data during collection, transmission, and storage. Peer-reviewed studies from 2016 to 2025 and technical reports were analyzed to examine applied mechanisms, datasets, and analytical models. Our analysis shows that blockchain and federated learning are the most prevalent decentralized privacy-preserving methods, while homomorphic encryption and differential privacy have recently gained traction for lightweight and edge-based IoT implementations. Despite these advancements, challenges persist, including computational overhead, limited scalability, and real-time performance constraints in resource-constrained devices. Furthermore, gaps remain in cross-domain interoperability, energy-efficient cryptographic designs, and privacy solutions for Unmanned Aerial Vehicle (UAV) and vehicular IoT systems. This survey offers a comprehensive overview of current research trends, identifies critical limitations, and outlines promising future directions to guide the design of secure and privacy-aware IoT architectures. Full article

Reliable water vapor permeability (WVP) testing is crucial for sustainable construction, enabling accurate assessment of bio-mineral materials like hemp concrete, which reduce the environmental impact through renewable sourcing and improved energy efficiency. However, most studies testing or conditioning porous building materials in environmental chambers overlook the influence of chamber occupancy on airflow and humidity evacuation. While the usual practice is to collect anemometric velocity results in selected locations, few investigations apply computational fluid dynamics (CFD) to analyze the entire flow field, and humidity-field assessment is practically absent. This study addresses this gap by using CFD to examine how sample arrangement affects airflow and relative humidity (RH) in a climatic chamber containing sixteen hemp concrete specimens in dry- and wet-cup setups, aiding the reliable characterization of hygroscopic eco-composites. Three arrangements were modeled in ANSYS Fluent (2024 R1) using turbulence and species transport. Results show that unoptimized wet-cup placements cause RH deviations exceeding ISO’s ±5% tolerance, potentially biasing permeability data and undermining comparability across laboratories. A balanced wet–dry layout maintained RH within limits, improving testing reproducibility. Velocity maps reveal strong gradients above exposed sample surfaces, suggesting that standard anemometric protocols may require refinement. The presented approach highlights chamber loading as a hidden factor influencing WVP results and provides a transferable CFD-based framework to enhance testing accuracy, support sustainable material qualification, and accelerate the standardization of green-building methodologies. Full article

This paper addresses the issue that traditional magnetorheological (MR) dampers have limited improvements in magnetic field utilization and damping channel length in confined spaces. It proposes an annular MR damper with an annular cylinder for propeller shafting. The piston head forms damping gaps with the cylinder’s inner and outer walls. This doubles the damping channel length without increasing axial size. The paper explains its working principle, completes the magnetic circuit design and damping force modeling, and utilizes COMSOL 5.6 Multiphysics to construct a magneto-fluid coupling model for analysis. Results show that, under 10 mm amplitude, 1 Hz sinusoidal excitation, and 2.0 A current, the damper outputs a damping force of 67.65 kN, with a damping adjustable coefficient of 10.87. Its force-displacement curve has a full hysteresis loop, showing excellent energy dissipation. The study proves the annular structure boosts the damper’s performance, offering a new way to achieve high damping force and a wide dynamic range in a compact space. Full article

Electricity is currently essential for the operation of most modern devices, with significant electrification being observed in all areas. This development has led to an increased demand for solutions that enable energy storage appropriate for a given application, which is currently solved by installing batteries with adequate capacity. This article presents an approach utilizing composite materials that combine both structural and energy storage features. The most frequently discussed components of such materials in the literature are compared, divided into those that contribute to the structural functions of the composite and those that provide additional functionality. The methodology for developing our literature analysis and for comparing materials is given. The results of our publication analysis are then presented, based on the type of integration of multifunctional elements, structural materials, resins, electrolytes, and production methods. The influence of these parameters on the mechanical and electrochemical properties of multifunctional composites is examined. The different materials are compared, and the best ones selected based on appropriate criteria. The current state of knowledge regarding simulations of such materials is presented, and the potential applications of multifunctional composites are reviewed. Finally, key research gaps are identified, suggesting directions for future work. Full article

Wettability significantly influences fluid distribution and flow behavior in hydrocarbon reservoirs, yet traditional macroscopic measurements fail to capture the micro- and nanoscale interfacial interactions that govern these processes. This study addresses a critical knowledge gap by employing molecular dynamics simulations to systematically investigate how salinity and mineral composition control wettability at the atomic scale—insights that are experimentally inaccessible yet essential for optimizing enhanced oil recovery strategies. We examined five typical reservoir minerals—kaolinite, montmorillonite, chlorite, quartz, and calcite—along with graphene as a model organic surface. Our findings reveal that while all minerals exhibit hydrophilicity (contact angles below 75°), increasing salinity weakens water wettability, with Ca²⁺ ions exerting the strongest effect due to their high charge density, which enhances electrostatic attraction with negatively charged mineral surfaces and promotes specific adsorption at the mineral–water interface, thereby displacing water molecules and reducing surface hydrophilicity. In oil–water–mineral systems, we discovered that graphene displays exceptional oleophilicity, with hydrocarbon interaction energies reaching −7043.61 kcal/mol for C₁₈H₃₈, whereas calcite and quartz maintain strong hydrophilicity. Temperature and pressure conditions modulate interfacial behavior distinctly: elevated pressure enhances molecular aggregation, while higher temperature promotes diffusion. Notably, mixed alkane simulations reveal that heavy hydrocarbons preferentially adsorb on mineral surfaces and form highly ordered structures on graphene, with diffusion rates inversely correlating with molecular size. These atomic-scale insights into wettability mechanisms provide fundamental understanding for designing salinity management and wettability alteration strategies in enhanced oil recovery operations. Full article

This study explores the transformative role of Cyber-Physical Power System (CPPS) Digital Twins (DTs) in enhancing the operational resilience, flexibility, and intelligence of modern power grids. By integrating physical system models with real-time cyber elements, CPPS DTs provide a synchronized framework for real-time monitoring, predictive maintenance, energy management, and cybersecurity. A structured literature review was conducted using the ProKnow-C methodology, yielding a curated portfolio of 74 publications from 2017 to 2025. This corpus was analyzed to identify key application areas, enabling technologies, simulation methods, and conceptual maturity levels of CPPS DTs. The study highlights seven primary application domains, including real-time decision support and cybersecurity, while emphasizing essential enablers such as data acquisition systems, cloud/edge computing, and advanced simulation techniques like co-simulation and hardware-in-the-loop testing. Despite significant academic interest, real-world implementations remain limited due to interoperability and integration challenges. The paper identifies gaps in standard definitions, maturity models, and simulation frameworks, underscoring the need for scalable, secure, and interoperable architectures and highlighting key areas for scientific development and real-life application of CPPS DTs, such as grid predictive maintenance, forecasting, fault handling, and power system cybersecurity. Full article

Precision solder deposition for 3D or flexible substrates remains a persistent challenge in electronic packaging. This study introduces a hybrid process that integrates stencil printing with laser-induced forward transfer (LIFT), employing a customized line-scan trajectory to fabricate high-aspect-ratio solder deposits under large-gap, contactless conditions. Solder paste patterns were first printed on a glass carrier and subsequently transferred using pulsed laser scanning, with high-speed imaging employed to resolve the transfer dynamics. Three transfer regimes—stable, unstable, and no transfer—were identified, with the stable regime exhibiting sequential stages governed by vaporization-induced pressure and the viscoelastic response of the solder paste. The initial aspect ratio (AR) was found to critically influence separation behavior, with AR = 0.3 marking the transition between bridging and cantilevered morphologies. Transferred deposits consistently achieved final aspect ratios approaching 0.7; notably, low-AR (<0.15) patterns showed a 2.2-fold height increase. The process maintains a robust energy window (0.937–1.112 J/cm²), offering both mechanistic insight into transfer stability and practical guidance for optimizing solder paste deposition in advanced packaging applications. Full article

Installing energy storage devices can improve the frequency modulation ability of offshore wind farms to participate in the grid. The lifecycle and wear of energy storage devices are significantly influenced by ambient temperature, charge and discharge rates, cycle depths, and operating environments. To extend the lifecycle and reduce the replacement frequency of these devices, their operation mode must be carefully considered. However, existing energy storage optimization configurations rarely consider these factors, particularly when addressing the frequency stability of offshore wind power systems. To address this gap, an optimization method is proposed for energy storage allocation that incorporates system frequency support, energy storage cost, and the devices’ lifecycle and degradation. This approach ensures a reasonable and efficient allocation of energy storage. Full article

This study examines how tourism development in Tunisia responds to environmental degradation, renewable energy consumption, technological innovation, and foreign direct investment. Using annual data for 1990–2023, we apply the Autoregressive Distributed Lag (ARDL) bounds approach to identify long-run equilibria and short-run dynamics and validate the results with Fully Modified Ordinary Least Squares (FMOLS). The bounds tests confirm stable long-run relationships among tourism development and its structural determinants—environmental degradation, renewable energy, technological innovation, and foreign direct investment. The empirical results show that environmental degradation depresses tourism development in the long run, whereas renewable energy and technological innovation promote it. Foreign direct investment provides the strongest positive contribution. Complimentary Granger causality tests confirm unidirectional causality from environmental degradation, renewable energy, and technological innovation to tourism development, and bidirectional causality between tourism and foreign direct investment, validating the robustness and direction of influences among variables. Short-run effects appear weaker and occasionally mixed; however, the negative and highly significant error-correction term indicates convergence toward equilibrium. The FMOLS estimates closely match the ARDL results, providing further confidence in the results. Accordingly, policymakers should bolster environmental management, increase renewable energy as part of tourism infrastructure, advance digital and eco-innovation, and attract FDI in cleaner technologies and higher standards of services. This study fills conceptual and regional evidence gaps by integrating environmental, technological, and financial dimensions within a unified framework. It offers practical guidance consistent with the Sustainable Development Goals; specifically, Goals 7 (clean energy), 8 (sustainable growth and jobs), and 13 (climate action). Full article

Despite being crucial for sustainable pavement construction, the widespread application of hot recycled asphalt mixtures in high-grade surface courses is hindered by concerns over their long-term performance, particularly regarding cracking resistance and moisture stability. This study systematically evaluates the road performance of hot central plant recycling (HCPR with 30% RAP) and hot in-place recycling (HIPR with 80% RAP) mixtures, benchmarked against virgin hot mix asphalt (SMA-13), through comprehensive laboratory simulations. The enhancing effect of basalt fibers (BFs) was thoroughly investigated. Results revealed a significant performance trade-off; while the recycled mixtures exhibited superior high-temperature stability (e.g., an 80.7% increase in dynamic stability for HIPR), their cracking resistance substantially decreased with higher RAP content (e.g., reductions of 60.8% in low-temperature flexural strain and 22.1% in intermediate-temperature fracture energy for HIPR). Both recycled mixtures also showed susceptibility to moisture damage, evidenced by stripping in Hamburg wheel-tracking tests. The incorporation of BFs effectively mitigated these deficiencies. It comprehensively improved the performance, enabling the HCPR mixture to meet specifications for severely cold regions and elevating the HIPR mixture to compliance level for cold regions. Furthermore, BF significantly enhanced rutting resistance under coupled hydrothermal conditions. These findings demonstrate that basalt fiber reinforcement can bridge the performance gap of recycled mixtures, thereby expanding their application scope and providing a robust technical basis for selecting and optimizing recycling strategies in high-grade pavement engineering. Full article

Education can play a pivotal role in the eradication of energy poverty by facilitating the transfer of knowledge and skills to all interested stakeholders whilst also promoting the adoption of sustainable energy solutions. In the context of this paper, a comprehensive review of European master’s programs related to energy poverty is carried out, resulting in the identification of approximately of 100 programs across seven European countries that either explicitly or implicitly address the topic. In most cases, energy poverty is embedded in a broader academic discipline—such as energy systems, renewable energy, or sustainable development—rather than being treated as a standalone field. In Europe, the United Kingdom, France, Greece, and Romania were singled out as the leading contributors to energy poverty education. Within the framework of the EU-funded project “MSc in Energy Poverty Alleviation Technologies”, implemented in collaboration with South African universities, this study focuses on South Africa, which represents a characteristic example of a country facing high levels of energy poverty and significant inequalities in energy access. This work highlights the critical need for targeted academic curricula specifically designed to bridge the persistent gap between academic research and its real-world applications, particularly in regions of the world where such integration is most urgent. It also emphasizes the essential role of linking STEM education with the social and humanitarian sciences. Finally, this work underscores the need for interdisciplinary approaches that connect energy poverty alleviation and education by additionally expanding the research and documentation of relevant good initiatives from Asia (China). Full article

Scientific, accurate, and interpretable carbon price forecasts provide critical support for addressing climate change, achieving low-carbon goals, and informing policy-making and corporate decision-making in energy and environmental markets. However, the existing studies mainly focus on deterministic forecasting, with obvious limitations in data feature diversity, model interpretability, and uncertainty quantification. To fill these gaps, this study constructs an interpretable hybrid system for carbon market price prediction by combining feature screening algorithms, deep learning models, and interpretable explanatory analysis methods. Specifically, this study first screens important variables from twenty-one multi-source structured and unstructured influencing factor datasets on five dimensions affecting carbon price using the Boruta algorithm. Immediately after that, this study proposes a hybrid architecture of bidirectional temporal convolutional network and Informer models, where a bidirectional temporal convolutional network is used to extract local spatio-temporal dependent features, while Informer captures long sequences of global features through the connectivity mechanism, thus realizing staged feature extraction. Then, to improve the interpretability of the model and quantify the uncertainty, this study introduces Shapley additive explanations to analyze the feature contribution in the prediction process, and the Monte Carlo dropout method is used to achieve interval prediction. Finally, the empirical results in China’s Guangdong and Shanghai carbon markets show that the proposed model significantly outperforms benchmark models, and the coverage probability of the obtained prediction intervals significantly outperforms the confidence level. The Shapley additive explanation analysis reveals regional heterogeneity drivers. In addition, the proposed model is also intensively validated in the European carbon market and the U.S. natural gas market, which also demonstrate an excellent prediction performance, indicating that the model has good robustness and applicability. Full article

This study introduces an energy-aware hybrid security framework that safeguards embedded systems against code theft, closing a critical gap. The approach integrates bitstream encryption, dynamic key generation, and Dynamic Function eXchange (DFX)-based memory obfuscation, yielding a layered hardware–software countermeasure to Read-Only Memory (ROM) scraping, side-channel attacks, and Man-in-the-Middle (MITM) intrusions by eavesdropping on communications on pins, cables, or Printed Circuit Board (PCB) routes. Prototyped on a Xilinx Zynq-7020 System-on-Chip (SoC) and applicable to MicroBlaze-based designs, it derives a fresh Authenticated Encryption with Associated Data (AEAD) key for each record via an Ascon-eXtendable-Output Function (XOF)–based Key Derivation Function (KDF) bound to a device identifier and a rotating slice from a secret pool, while relocating both the pool and selected Block RAM (BRAM)-resident code pages via Dynamic Function eXchange (DFX). This moving-target strategy frustrates ROM scraping, probing, and communication-line eavesdropping, while cryptographic confidentiality and integrity are provided by a lightweight AEAD (Ascon). Hardware evaluation reports cycles/byte, end-to-end latency, and per-packet energy under identical conditions across lightweight AEAD baselines; the framework’s key-derivation and DFX layers are orthogonal to the chosen AEAD. The threat model, field layouts (Nonce/AAD), receiver-side acceptance checks, and quantitative bounds are specified to enable reproducibility. By avoiding online key exchange and keeping long-lived secrets off Programmable Logic (PL)-based external memories while continuously relocating their physical locus, the framework provides a deployable, energy-aware defense in depth against code-theft vectors in FPGA-based systems. Overall, the work provides an original and deployable solution for strengthening the security of commercial products against code theft in embedded environments. Full article