Search Results (4,401)

Digital twins (DTs) offer significant promise for condition-based maintenance of floating offshore wind turbines (FOWTs); however, existing solutions typically compromise either on physical rigor or real-time computational performance. This paper presents a real-time DT framework that resolves this trade-off by embedding a hydro-elastic reduced-order model (ROM) that accurately captures structural dynamics and fluid–structure interaction. Integrated in a cloud-ready Internet of Things architecture, the ROM reconstructs full-field displacements, von Mises stresses, and fatigue metrics with near real-time responsiveness. Validation on the 5 MW OC4-DeepCWind semi-submersible platform shows that the ROM reproduces finite-element (FEM) displacements and stresses with relative errors below 1%. A three-hour load case is solved in 0.69 min for displacements and 3.81 min for stresses on a consumer-grade NVIDIA RTX 4070 Ti GPU—over two orders of magnitude faster than the full FEM model—while one million fatigue stress histories (1000 hotspots × 1000 operating scenarios) are processed in 37 min. This efficiency enables continuous structural monitoring, rapid *what-if* assessments and timely decision-making for targeted inspections and adaptive control. By effectively combining physics-based reduced-order modeling with high-throughput computation, the proposed framework overcomes key barriers to DT deployment: computational overhead, physical fidelity and scalability. Although demonstrated on a steel platform, the approach is readily extensible to composite structures and multi-turbine arrays, providing a robust foundation for cost-effective and reliable deep-water wind-energy operations. Full article

This study introduces an innovative self-regulating intelligent optimal balancing control framework for inverted pendulum-type mechatronic platforms, designed to enhance reference tracking accuracy and improve disturbance rejection capability. The control procedure is synthesized by synergistically integrating a baseline Linear Quadratic Regulator (LQR) with a fuzzy controller via a customized linear decomposition function (LDF). The LDF dissociates and transforms the LQR control law into compounded state tracking error and tracking error derivative variables that are eventually used to drive the fuzzy controller. The principal contribution of this study lies in the adaptive modulation of these compounded variables using reconfigurable tangent hyperbolic functions driven by the cubic power of the error signals. This nonlinear preprocessing of the input variables selectively amplifies large errors while attenuating small ones, thereby improving robustness and reducing oscillations. Moreover, a model-free online self-tuning law dynamically adjusts the variation rates of the hyperbolic functions through dissipative and anti-dissipative terms of the state errors, enabling autonomous reconfiguration of the nonlinear preprocessing layer. This dual-level adaptation enhances the flexibility and resilience of the controller under perturbations. The robustness of the designed controller is substantiated via tailored experimental trials conducted on the Quanser rotary pendulum platform. Comparative results show that the prescribed scheme reduces pendulum angle variance by 41.8%, arm position variance by 34.6%, and average control energy by 28.3% relative to the baseline LQR, while outperforming conventional fuzzy-LQR by similar margins. These results show that the prescribed controller significantly enhances disturbance rejection and tracking accuracy, thereby offering a numerically superior control of inverted pendulum systems. Full article

Developing bifunctional electrocatalysts that simultaneously enable green hydrogen production and water purification is essential for advancing sustainable energy and environmental technologies. In this study, we present Cu@Pd core–shell nanostructures fabricated through template-assisted electrodeposition of Cu, followed by galvanic Pd modification on pyrolytic graphite electrodes (PGEs). The optimised catalyst exhibited superior hydrogen evolution reaction (HER) activity, with an onset potential of 70 mV, a low Tafel slope of 33 mV dec⁻¹ and excellent stability during prolonged HER operation. In addition to hydrogen evolution, Cu@Pd/PGE shows significantly enhanced nitrate reduction reaction (NRR) activity compared to Cu/PGE in both alkaline and neutral conditions. Under ideal conditions, the catalyst achieved 60% nitrate removal with high selectivity towards ammonia and minimal nitrite formation, emphasising its superior performance. This enhanced bifunctionality arises from the synergistic Cu–Pd interface, facilitating efficient nitrate adsorption and selective hydrogenation. Despite their high catalytic activity for both HER and NRR, the Cu@Pd nanostructures could often emerge as a versatile platform for integration into sustainable hydrogen production and an effective denitrification process. Full article

The electrical arcs generated by high-speed dynamic separation between pantograph and catenary systems pose a significant threat to the operational safety of high-speed railways. Environmental factors, particularly relative humidity and airflow, critically influence arc characteristics. This study establishes a two-dimensional pantograph–catenary arc model based on magnetohydrodynamic theory, validated through a self-developed experimental platform. Research findings demonstrate that as relative humidity increases from 25% to 100%, the core arc temperature decreases from 10,500 K to 9000 K due to enhanced heat dissipation in humid air and electron capture by water molecules; the peak arc voltage rises from 37.25 V to 48.17 V resulting from accelerated deionization processes under high humidity conditions; the average arc energy in polar regions increases from 2.5 × 10⁻⁴ J/m³ to 3.5 × 10⁻⁴ J/m³, exhibiting a saddle-shaped distribution; and the maximum arc pressure declines from 5.3 Pa to 3.7 Pa. Under airflow conditions of 10–30 m/s, synergistic effects between airflow and humidity further modify arc behavior. The most pronounced temperature fluctuations and most frequent arc root migration occur at 100% humidity with 30 m/s airflow, while the shortest travel distance and longest persistence are observed at 25% humidity with 10 m/s airflow, as airflow accelerates heat dissipation and promotes arc root alternation. Experimental measurements of arc radiation intensity and temperature distribution show excellent agreement with simulation results, verifying the model’s reliability. This study quantitatively elucidates the influence patterns of humidity and airflow on arc characteristics, providing a theoretical foundation for enhancing pantograph–catenary system reliability. Full article

Underwater exploration relies heavily on autonomous underwater vehicles and sensor platforms for sustained monitoring of marine environments, yet their operational duration is limited by energy constraints. To enhance energy efficiency, various control strategies have been proposed, including robust, optimal, and disturbance-aware approaches. Recent work introduced a variable structure controller (VSC) with a constant-amplitude control action for depth control of a platform equipped with a variable buoyancy module, achieving an average 22% reduction in energy use in comparison with conventional PID-based controllers. In a separate paper, the conditions for its closed-loop stability were proven. This study extends these works by proposing a controller with a variable-amplitude control action designed to minimize energy consumption. A formal proof of stability is provided to guarantee safe operation even under conservative assumptions. The controller is applied to a previously developed depth-regulated sensor platform using a validated physical model. Additionally, this study analyzes how the controller parameters and mission requirements affect stability regions, offering practical guidelines for parameter tuning. A method to estimate oscillation amplitude during hovering tasks is also introduced. Simulation trials validate the proposed approach, showing energy savings of up to 16% when compared to the controller using a constant-amplitude control action. Full article

Decentralized energy trading has been designed as a scalable substitute for traditional electricity markets. While blockchain technology facilitates efficient transparency and automation for peer-to-peer energy trading, the majority of current proposals lack real-time intelligence and adaptability concerning pricing strategies. This paper presents an innovative machine learning-driven solar energy trading platform on the Ethereum blockchain that uniquely integrates Bayesian-optimized XGBoost models with dynamic pricing mechanisms inherently incorporated within smart contracts. The principal innovation resides in the real-time amalgamation of meteorological data via Chainlink oracles with machine learning-enhanced price optimization, thereby establishing an adaptive system that autonomously responds to fluctuations in supply and demand. In contrast to existing static pricing methodologies, our framework introduces a multi-faceted dynamic pricing model that encompasses peak-hour adjustments, prediction confidence weighting, and weather-influenced corrections. The system dynamically establishes energy prices predicated on real-time supply–demand forecasts through the implementation of role-based access control, cryptographic hash functions, and ongoing integration of meteorological and machine learning data. Utilizing real-world meteorological data from La Trobe University’s UNISOLAR dataset, the Bayesian-optimized XGBoost model attains a remarkable prediction accuracy of 97.45% while facilitating low-latency price updates at 30 min intervals. The proposed system delivers robust transaction validation, secure offer creation, and scalable dynamic pricing through the seamless amalgamation of off-chain machine learning inference with on-chain smart contract execution, thereby providing a validated platform for trustless, real-time, and intelligent decentralized energy markets that effectively address the disparity between theoretical blockchain energy trading and practical implementation needs. Full article

Unmanned aerial vehicles (UAVs) are redefining both civilian and defense operations, with swarm-based architectures unlocking unprecedented scalability and autonomy. However, these advancements introduce critical security challenges, particularly in location verification and authentication. This review provides a comprehensive synthesis of hardware security primitives (HSPs)—including Physical Unclonable Functions (PUFs), Trusted Platform Modules (TPMs), and blockchain-integrated frameworks—as foundational enablers of trust in UAV ecosystems. We systematically analyze communication architectures, cybersecurity vulnerabilities, and deployment constraints, followed by a comparative evaluation of HSP-based techniques in terms of energy efficiency, scalability, and operational resilience. The review further identifies unresolved research gaps and highlights transformative trends such as AI-augmented environmental PUFs, post-quantum secure primitives, and RISC-V-based secure control systems. By bridging current limitations with emerging innovations, this work underscores the pivotal role of hardware-rooted security in shaping the next generation of autonomous aerial networks. Full article

Achieving optimal daylighting in buildings necessitates complex and expensive control systems. This research addresses this challenge by proposing a simple and more practical solution: a parametric louver system based on rotating slats controlled by stepper motors, powered by an Integrated Circuit platform (Arduino board), which can translate the digital figures (the rotation angles) to a physical action. The system automatically adjusts the slats in accordance with solar altitudes and reflects them to specific targets over the ceiling. This ensures a uniform and comfortable distribution of daylight throughout a room. This system was developed using Grasshopper as the parametric software, with future control planned via a user-friendly mobile app through a preliminary prototype. This daylighting system prioritises human visual comfort while targeting a significant 53% reduction in electrical lighting energy consumption. The system aims to enhance occupant well-being to significantly increase energy savings, making it a compelling solution for sustainable building design. Full article

As a key piece of equipment in badminton, the surface treatment technology of rackets has garnered significant attention in the fields of material science and sports engineering. This study is the first to systematically review research on racket coatings, integrating interdisciplinary knowledge on the classification of functional coatings, their performance-enhancing principles, and their relationship with competitive levels, thereby addressing a gap in theoretical research in this field. This study focuses on four major functional coating systems: superhydrophobic coatings (to improve environmental adaptability and reduce air resistance), anti-scratch coatings (to prolong the life of the equipment), vibration-damping coatings (to optimise vibration damping performance), and strength-enhancing coatings (to safeguard structural stability). In badminton, differences in player skill levels and usage scenarios lead to variations in racket materials, which, in turn, result in different preparation processes and performance effects. The use of vibration-damping materials alleviates the impact force on the wrist, effectively preventing sports injuries caused by prolonged training; leveraging the aerodynamic properties of superhydrophobic technology enhances racket swing speed, thereby improving hitting power and accuracy. From the perspective of performance optimization, coating technology improves athletic performance in three ways: nanocomposite coatings enhance the fatigue resistance of the racket frame; customized damping layers reduce muscle activation delays; and surface energy regulation technology improves grip stability. Challenges remain in the industrial application of environmentally friendly water-based coatings and the evaluation system for coating lifespan under multi-field coupling conditions. Future research should integrate intelligent algorithms to construct a tripartite optimization system of “racket-coating-user” and utilize digital sports platforms to analyze its mechanism of influence on professional athletes’ tactical choices, providing a theoretical paradigm and technical roadmap for the targeted development of next-generation smart badminton rackets. Full article

Non-photochemical quenching (NPQ) is a key photoprotective mechanism in Symbiodiniaceae, enabling photosystem II (PSII) to dissipate excess excitation energy under stress. The balance between regulated (Φ_NPQ) and unregulated (Φ_NO) energy dissipation influences thermal tolerance, yet the temperature thresholds at which this balance shifts remain poorly defined. Here, we used the Phenoplate, a high-throughput fluorometric platform integrating rapid light curves with controlled temperature ramping, to examine short-term thermal responses in Breviolum minutum across 6–71 °C. We identified a sharp transition at 41 °C where Φ_NPQ collapsed and was replaced by Φ_NO, indicating loss of regulated photoprotection. This switch coincided with a pronounced drop in PSII effective quantum yield (Φ_II) and substantial reductions in cell density, marking a thermal Achilles’ heel in the photoprotective capacity of this species. Despite this regulatory breakdown, a fraction of cells persisted for at least three days post-exposure. These results demonstrate that B. minutum maintains regulated photoprotection up to a discrete threshold, beyond which unregulated becomes the dominant pathway and survival is compromised. Identifying such thermal inflection points in coral symbionts provides mechanistic insight into their vulnerability under acute heat stress and may inform early-warning indicators for coral bleaching susceptibility. Full article

Lipids obtained from microalgae have recently received significant attention from the energy and food industries. Microalgae are promising alternatives and are more sustainable sources of lipids for the food industry, which faces a growing demand for food and increased environmental awareness among consumers. This study provides a bibliometric review of research articles published between 2019 and 2024 with the aim of understanding the future trends and tendencies of the applications of microalgal lipids in the food industry. A thorough assessment of 255 articles retrieved from the Scopus database showed an apparent decrease in the number of publications per year within the analyzed timeframe. The predominant focus has been basic research conducted on a lab-scale using chlorophytes (green algae) to optimize lipid production by modulating physicochemical cultivation parameters (i.e., nutrient availability, temperature, light, and pH). Lipids were mainly extracted using the Bligh and Dyer or Folch methods, quantified gravimetrically, and characterized using gas chromatography coupled to mass spectrometry. Publications referring to polyunsaturated fatty acids, such as omega-3 and omega-6, were the most abundant. The results emphasized the significance of microalgae as a promising biotechnological platform for the production of lipids within the food industry. Full article

Integration of renewable energy into modern power grids remains limited by intermittency and the need for reliable energy storage. Redox flow batteries (RFBs) are promising for large-scale energy storage, yet their widespread adoption is hindered by the high cost. In this study, we investigate isopropanol as a redox-active species with Pt-Cu alloy electrocatalysts for aqueous-organic RFBs. A series of Pt_xCu catalysts with varying Pt:Cu ratios were synthesized and studied for isopropanol electro-oxidation reaction (IPAOR) performance. Among them, PtCu demonstrated the best performance, achieving a low activation energy of 14.4 kJ/mol at 0.45 V vs. RHE and excellent stability at 1 M isopropanol (IPA) concentration. Kinetic analysis and in situ attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy revealed significantly reduced acetone accumulation on PtCu compared to pure Pt, indicating enhanced resistance to catalyst poisoning. Density functional theory (DFT) calculations further identified the first proton-coupled electron transfer (PCET) as the rate-determining step (RDS) with C-H bond scission as the preferred pathway on PtCu. A proof-of-concept PtCu-catalyzed H-cell demonstrated stable cycling over 200 cycles, validating the feasibility of IPA as a low-cost, regenerable redox couple. These findings highlight PtCu-catalyzed IPA/acetone(ACE) chemistry as a promising platform for next-generation aqueous-organic RFBs. Full article

Most biomarkers exhibit abnormal expression in more than one disease, making conventional single-biomarker detection strategies prone to false-negative results. Detecting multiple biomarkers associated with a single disease can therefore substantially improve diagnostic accuracy. Accordingly, recent research has focused on precise multiplex detection, leading to the development of sensors employing various readout methods, including electrochemical, fluorescence, Raman, and colorimetric approaches. This review focuses on optical sensing applications, such as fluorescence, Raman spectroscopy, and colorimetry, which offer rapid and straightforward detection and are well suited for point-of-care testing (POCT). These optical sensors exploit nanoscale phenomena derived from the intrinsic properties of nanomaterials, including metal-enhanced fluorescence (MEF), Förster resonance energy transfer (FRET), and surface-enhanced Raman scattering (SERS), which can be tailored through modifications in material type and structure. We summarize the types and properties of commonly used nanomaterials, including plasmonic and carbon-based nanoparticles, and provide a comprehensive overview of recent advances in multiplex biomarker detection. Furthermore, we address the potential of these nanosensors for clinical translation and POCT applications, highlighting their relevance for next-generation disease diagnostic platforms. Full article

This paper explores how digital crowdsourcing platforms communicate sustainability-oriented innovation and mobilise stakeholder engagement. Through a directed content analysis of three platforms (OpenIDEO, San Francisco, CA, USA; Enel Innovation Hub, Rome, Italy; and InnoCentive, Waltham, MA, USA). The study examines communication strategies, participation models, and alignment with the United Nations Sustainable Development Goals (SDGs). Results show that communication is not neutral but functions as a governance mechanism shaping who participates, how innovation is framed, and what outcomes emerge. OpenIDEO fosters inclusive co-creation and SDG alignment, Enel Innovation Hub highlights technical readiness and energy transition, and InnoCentive relies on rewards and competition. Word-frequency analysis confirms these emphases, while interpretation through Motivation Crowding Theory, Social Exchange Theory, and Transaction Cost Theory explains how motivational framing, legitimacy signals, and participation structures affect engagement. The study contributes to research on open innovation and platform studies by demonstrating the constitutive role of communication in enabling or constraining sustainable collective action. Practical implications are outlined for platform designers, marketers, and policymakers seeking to align digital infrastructures with systemic sustainability goals. Full article

Accurate demand forecasting contributes to improved energy efficiency and the development of short-term strategies. Predictive management of energy storage using redox flow batteries is presented as a robust solution for optimizing the operation of microgrids from the demand side. This study proposes an intelligent architecture that integrates demand forecasting models based on artificial neural networks and active management strategies based on the instantaneous production of renewable sources within the microgrid. The solution is supported by a real-time monitoring platform capable of analyzing data streams using continuous evaluation algorithms, enabling dynamic operational adjustments and active methods for predicting the storage system’s state of charge. The model’s effectiveness is validated using performance indicators such as RMSE, MAPE, and MSE, applied to experimental data obtained in a specialized microgrid laboratory. The results also demonstrate substantial improvements in energy planning and system operational efficiency, positioning this proposal as a viable strategy for distributed and sustainable environments in modern electricity systems. Full article