Search Results (409)

This paper presents a robust and computationally efficient fault classification framework for wind energy conversion systems (WECS), built upon a Robust Random Vector Functional Link Network (Robust-RVFLN) and validated through real-time simulations on a Real-Time Digital Simulator (RTDS). Unlike existing studies that depend on high-dimensional feature extraction or purely data-driven deep learning models, our approach leverages a compact set of five statistically significant and physically interpretable features derived from rotor torque, phase current, DC-link voltage, and dq-axis current components. This reduced feature set ensures both high discriminative power and low computational overhead, enabling effective deployment in resource-constrained edge devices and large-scale wind farms. A synthesized dataset representing seven representative fault scenarios—including converter, generator, gearbox, and grid faults—was employed to evaluate the model. Comparative analysis shows that the Robust-RVFLN consistently outperforms conventional classifiers (SVM, ELM) and deep models (CNN, LSTM), delivering accuracy rates of up to 99.85% for grid-side line-to-ground faults and 99.81% for generator faults. Beyond accuracy, evaluation metrics such as precision, recall, and F1-score further validate its robustness under transient operating conditions. By uniting interpretability, scalability, and real-time performance, the proposed framework addresses critical challenges in condition monitoring and predictive maintenance, offering a practical and transferable solution for next-generation renewable energy infrastructures. Full article

Railway turnouts are highly susceptible to snow and ice accumulation during winter, which can cause malfunctions, resulting in train delays or, in extreme cases, derailments with potential casualties. To mitigate these risks, resistive heating (RH) systems using nichrome wires have traditionally been employed. However, these systems suffer from slow heat transfer and high power consumption. To address these limitations, this article proposes an induction heating (IH) system designed for rapid thermal response and improved electrical and thermal efficiency. The proposed system comprises a power conversion unit featuring a boost power factor correction (PFC) stage and a high-frequency resonant inverter, along with an improved IH coil. An experiment in real snowfall demonstrates the IH system’s fast heat-up capability, effective snow cover removal, and enhanced energy efficiency compared to conventional methods. Full article

This study presents a computational investigation into the design of triphenylamine-based donor chromophores incorporating 2-(1,1-dicyanomethylene)rhodanine as the acceptor unit. Three molecular architectures (System-1 to System-3) were developed by introducing distinct thiophene-derived $\pi$-bridges to modulate their electronic and optical characteristics for potential application in bulk heterojunction organic solar cells (OSCs). Geometrical optimizations were performed at the B3LYP/6-31+G(d,p) level, while excited-state and absorption properties were evaluated using TD-DFT with the CAM-B3LYP functional. Frontier orbital analysis revealed efficient charge transfer from donor to acceptor moieties, with System-3 showing the narrowest HOMO–LUMO gap (1.96 eV) and the lowest excitation energy (2.968 eV). Charge transport properties, estimated from reorganization energies, indicated that System-2 exhibited the most favorable balance for ambipolar transport, featuring the lowest electron reorganization energy (0.317 eV) and competitive hole mobility. Photovoltaic parameters calculated with PC₆₁BM as acceptor predicted superior $V_{oc}$, $J_{sc}$, and fill factor values for System-2, resulting in the highest theoretical power conversion efficiency (10.95%). These findings suggest that $\pi$-bridge engineering in triphenylamine-based systems can significantly enhance optoelectronic performance, offering promising donor materials for next-generation OSC devices. Full article

Background: This study used inertial measurement unit (IMU)-based posture angle estimates to define proxy risk labels and investigated whether these labels can be predicted from demographic, anthropometric, and lifestyle variables through machine learning analysis. Methods: Thirty healthy individuals aged 18–25 years were included. Demographic and anthropometric data and information on daily living activities were collected. The IMU sensors were placed at vertebral levels C1, C7, T5, T12, and L5. Participants were instructed to stand in an upright posture, followed by a relaxed daily posture. Anatomic postural changes between these positions were analyzed. Cervical lordosis, thoracic kyphosis, lumbar lordosis, and scoliosis risks were predicted using machine learning algorithms, including Random Forest (RF) and Artificial Neural Networks (ANN). Results: Incorrect postures during desk work and phone use were associated with an increased likelihood of posture-related deviations, such as cervical lordosis, thoracic kyphosis, and lumbar lordosis. Conversely, daily physical activity reduced these deviations. Using LOSO and stratified cross-validation with imbalance handling, balanced accuracies ranged between 0.55 and 0.82 across targets, with majority-class baselines between 0.53 and 0.87. For cervical lordosis risk, RF achieved a 0.82 balanced accuracy (95% CI: 0.74–0.97), while other categories showed a moderate but consistent performance. AUPRC values exceeded baseline levels across all models. Conclusions: IMU-based posture angle estimates can be used to identify posture-related risk categories. In this study, ML models have demonstrated predictive relationships with demographic, anthropometric, and lifestyle variables. These findings provide exploratory evidence based on IMU-derived proxy labels in a small cohort of healthy young adults. They represent exploratory indicators of postural deviation rather than clinical outcomes and may motivate future studies on preventive strategies. Importantly, the results remain underpowered relative to the a priori power targets and should be interpreted qualitatively. Full article

Adverse climate events have recently highlighted an increasing need to deploy sustainable energetic infrastructures. The existing electric conversion circuits for solar energy provide high efficiency; however, gaps in sustainability and robustness can be identified by considering their operation during intense perturbations, potentially occurring for interplanetary energy transfer. Additionally, charging characteristics for energy storage units influence differently the operation life of battery arrays, with increased stability providing favorable operating conditions. Therefore, the present study develops an alternative controller for managing solar energy as well as a prototype for tracking the maximum power point, both constrained by robustness and renewability studies. For the presented design, stability analyses and simulations validated the management of electric energy from solar panels and the developed configuration resulted in improving current peak integral transient characteristics by using an alternative control method, demonstrating stability for an indefinite number of energy storage units. Furthermore, the estimation for VLSI (Very-Large-Scale Integration) of this constrained design has been concluded to potentially provide a solution with adequate performance, comparable to state-of-the-art computational circuits. However, certain limitations could arise when substituting the main computation parts with analyzed solutions and proceeding with integration-based manufacturing. Full article

Language, a uniquely human cognitive faculty, is fundamentally characterized by its capacity for complex thoughts and structured expressions. This review examines two critical measures of linguistic performance: idea density (ID) and grammatical complexity (GC). ID quantifies the richness of information conveyed per unit of language, reflecting semantic efficiency and conceptual processing. GC, conversely, measures the structural sophistication of syntax, indicative of hierarchical organization and rule-based operations. We explore the neurobiological underpinnings of these measures, identifying key brain regions and white matter pathways involved in their generation and comprehension. This includes linking ID to a distributed network of semantic hubs, like the anterior temporal lobe and temporoparietal junction, and GC to a fronto-striatal procedural network encompassing Broca’s area and the basal ganglia. Moreover, a central theme is the integration of Chomsky’s theories of Universal Grammar (UG), which posits an innate human linguistic endowment, with their neurobiological correlates. This integration analysis bridges foundational models that first mapped syntax (Friederici’s work) to distinct neural pathways with contemporary network-based theories that view grammar as an emergent property of dynamic, inter-regional neural oscillations. Furthermore, we examine the genetic factors influencing ID and GC, including genes implicated in neurodevelopmental and neurodegenerative disorders. A comparative anatomical perspective across human and non-human primates illuminates the evolutionary trajectory of the language-ready brain. Also, we emphasize that, clinically, ID and GC serve as sensitive neurocognitive markers whose power lies in their often-dissociable profiles. For instance, the primary decline of ID in Alzheimer’s disease contrasts with the severe grammatical impairment in nonfluent aphasia, aiding in differential diagnosis. Importantly, as non-invasive and scalable metrics, ID and GC also provide a critical complement to gold-standard but costly biomarkers like CSF and PET. Finally, the review considers the emerging role of AI and Natural Language Processing (NLP) in automating these linguistic analyses, concluding with a necessary discussion of the critical challenges in validation, ethics, and implementation that must be addressed for these technologies to be responsibly integrated into clinical practice. Full article

To accommodate the integration of emerging energy sources such as wind and solar power, hydroelectric units are increasingly required to operate across a broader range of conditions. This operational expansion often leads to elevated pressure pulsations within turbines under non-design conditions, resulting in intensified hydraulic vibrations and, in some cases, structural damage and overall stability concerns. In this study, the Shear Stress Transport (SST) k-ω turbulence model is employed to perform unsteady numerical simulation calculation of a Francis-99 mixed-flow model turbine operating at a head of 400 m. Simulations are conducted for three operating regimes: low-flow and low-load conditions, optimal conditions, and high-flow and high-load conditions. Internal flow in the full flow channel of the turbine and pressure pulsation in the full flow channel components is systematically analyzed. The findings indicate that under low-flow and low-load conditions, the ability of the runner blades to constrain the water flow is significantly decreased. Across all three operational scenarios, the dominant pressure pulsation frequencies observed in both the stationary and guide vane are 30f_n, primarily influenced by dynamic and static disturbance caused by the rotation of the runner’s long and short blades. In low-flow and low-load conditions, a low-frequency component at 0.2f_n, due to the existence of vortices in the draft tube, exhibits the highest amplitude—up to 0.6%—in the straight cone section. Within the runner, pressure pulsation frequencies are predominantly associated with the rotation of the guide vane. Conversely, the draft tube region is characterized by frequency components related to both the runner’s dynamic-static interaction at 30f_n and vortex-induced pulsations at 0.2f_n. Full article

We present a single-slab metasurface that converts a normally incidental linearly polarized wave into either right- or left-handed circular polarization (RHCP/LHCP) through a simple ${90}^{\circ }$ mechanical rotation. Each unit cell comprises two L-shaped metallic resonators placed on the opposite faces of a low-permittivity substrate. Operating in transmission mode, the linear-to-circular (LTC) converter does not require any active electronic components. The geometry is optimized by using full-wave simulations to maximize the conversion up to 26% relative bandwidth with polarization conversion efficiency up to 65%, and insertion loss below 1.3 dB. Power balance analysis confirms low-loss, impedance-matched behavior. A scaled prototype fabricated from AWG-25 steel wires validates the model: experimental measurements closely reproduce the simulated bandwidth and demonstrate robust handedness switching. Because the resonance frequency depends primarily on resonator length and unit-cell pitch and thickness, the design can be retuned across the microwave spectrum through straightforward geometrical scaling. These results suggest that mechanical rotation could provide a simple and reliable alternative to electronic tuning in reconfigurable circular polarizers. Full article

This paper addresses the technical requirements and challenges encountered in the design and development of a customised power electronics board for a stratospheric balloon payload. This board includes power conversion and distribution to critical components (e.g., FPGAs and a ±4 kV power supply), as well as the pressurised enclosure designed to house these components along with other essential electronics. These systems were part of two scientific instruments onboard SUNRISE III, a high-altitude solar observatory launched in July 2024 from ESRANGE (Kiruna, Sweden), with a floating trajectory over the Arctic Circle. The SUNRISE III mission, based on a stratospheric balloon, was carried out by an international consortium of research institutions from Germany, Spain, Japan, and the United States, and in collaboration with NASA’s CSBF and the Swedish Space Corporation. Furthermore, this work presents telemetry data from the pressure sensing system of the electronic unit, as well as voltage and current measurements from the power electronics board outputs. These data were recorded during the floating phase of the mission, up to the balloon’s arrival in northern Canada after a successful week of scientific operations. Full article

Accelerated climate warming has led to the frequent occurrence of extreme weather events, resulting in high-frequency, large-scale, and highly destructive power outages and electricity shortages, which serve as a wake-up call for the safe and stable operation of the power system. To predict safety risks, this study constructs a baseline scenario and five power security scenarios based on the SWITCH-China model, systematically assessing the impact of external shocks on the power system’s evolution path and carbon reduction economics. The results indicate that external shocks are the key factors influencing the power system’s installed capacity structure and generation mix. The increase in demand forces the substitution of non-fossil energy. In the demand growth scenario, by 2060, wind and solar installed capacity will be 1.034 billion kilowatts higher than in the baseline scenario. Rising fuel costs will accelerate the exit of fossil fuel units. In the fuel cost increase scenario, 765 million kilowatts of coal power were reduced cumulatively across three time points. Wind and solar outages, along with transmission failures, lead to significant local economic investments while also causing inter-provincial carbon transfer. In the wind and solar outage scenario, provinces with a high proportion of wind and solar, such as Guangdong and Guizhou, see an increase in carbon emissions of 31 million tons and 8 million tons, respectively. Conversely, provinces with a lower proportion of wind and solar, such as Inner Mongolia and Xinjiang, reduce carbon emissions by 46 million tons and 39 million tons, respectively. Energy storage development supports the expansion of non-fossil energy in the power system. The study recommends accelerating wind and solar deployment, building a storage system at the scale of hundreds of billions of kilowatt-hours, and optimizing the inter-provincial transmission network to address the dual challenges of power security and carbon neutrality. Full article

Aiming at the demands for low-carbon transformation in multi-energy-coupled industrial parks, a model is proposed that incorporates a carbon trading system incorporating incentives and penalties. This model includes joint combined heat and power (CHP) units, carbon capture technologies, and power-to-gas (P2G) conversion equipment. Firstly, we develop a modeling framework for the joint operation of cogeneration units to establish a comprehensive energy system within the industrial park that integrates electricity, heat, gas, and cold energy sources. Subsequently, we introduce a reward and punishment carbon trading mechanism into an industrial park to regulate carbon emissions effectively. With an optimization objective focused on minimizing the overall operating costs of the system while considering relevant constraints, we formulate an optimization model. The Gurobi solver is employed through the Yalmip toolkit to address this optimization problem. Finally, four operational scenarios are established to compare and validate the feasibility of our proposed optimization strategy. The results from our computational example demonstrate that integrating combined heat and power along with carbon capture and P2G technologies—coupled with a tiered reward and punishment carbon trading mechanism—can significantly enhance the energy consumption structure of the system. Under this model, the overall expenses are decreased by 12.36%, CO₂ emissions decrease by 33.37%, and renewable energy utilization increases by 36.7%. This approach has effectively improved both wind power consumption capacity and low-carbon economic benefits within the system while ensuring sustainable economic development in alignment with “dual carbon” goals. Full article

The development of hydrogen energy can significantly reduce the negative impact on the environment. For the successful implementation of hydrogen technologies, it is necessary to transform existing models of production, distribution, and consumption of both thermal and electrical energy. The processes of fuel conversion and combustion are complex and, in some cases, insufficiently studied. A complete replacement of natural gas with hydrogen requires an assessment of energy and environmental characteristics. This study aims to evaluate the operation of a gas turbine unit when transitioning to hydrogen fuel. The GE 6FA engine was chosen as the object of study. Mathematical modeling of this engine was conducted using the software complex “AS GRET” (Automated System for Gas Dynamic Calculations of Power Turbomachinery). Full article

Integrating renewable energy with biomass valorization offers a scalable pathway toward circular and climate-resilient campus operations. This study presents a replicable model implemented at Alanya Alaaddin Keykubat University (ALKU, Türkiye), where post-consumer food waste from 30 cafeteria menus is converted into pet food and compost using a 150 L ECOAIR-150 thermal drying and grinding unit powered entirely by a 1.7 MW rooftop photovoltaic (PV) system. The PV infrastructure, established under Türkiye’s first public-sector Energy Performance Contract (EPC), ensures zero-electricity-cost operation. On average, 260 kg of organic waste are processed monthly, yielding 180 kg of pet food and 50 kg of compost, with an energy demand of 1.6 kWh h⁻¹ and a conversion efficiency of 68.4%, resulting in approximately 17.5 t CO₂ emissions avoided annually. Economic analysis indicates a monthly revenue of USD 55–65 and a payback period of ~36 months. Sensitivity analysis highlights the influence of input quality, seasonal waste composition, PV output variability, and operational continuity during academic breaks. Compared with similar initiatives in the literature, this model uniquely integrates EPC financing, renewable energy generation, and waste-to-product transformation within an academic setting, contributing directly to SDGs 7, 12, and 13. Full article

This paper investigates technological pathways for the conversion of coal-fired power plants toward sustainable energy sources, using an integrated multi-criteria decision-making approach that combines Proknow-C, AHP, and PROMETHEE. Eight alternatives were identified: full conversion to natural gas, full conversion to biomass, coal and natural gas hybridization, coal and biomass hybridization, electricity and hydrogen cogeneration, coal and solar energy hybridization, post-combustion carbon capture systems, and decommissioning with subsequent reuse. The analysis combined bibliographic data (26 scientific articles and 13 patents) with surveys from 14 energy experts, using Total Decision version 1.2.1041.0 and Visual PROMETHEE version 1.1.0.0 software tools. Based on six criteria (environmental, structural, technical, technological, economic, and social), the most viable option was full conversion to natural gas (ϕ = +0.0368), followed by coal and natural gas hybridization (ϕ = +0.0257), and coal and solar hybridization (ϕ = +0.0124). These alternatives emerged as the most balanced in terms of emissions reduction, infrastructure reuse, and cost efficiency. In contrast, decommissioning (ϕ = −0.0578) and carbon capture systems (ϕ = −0.0196) were less favorable. This study proposes a structured framework for strategic energy planning that supports a just energy transition and contributes to the United Nations Sustainable Development Goals (SDGs) 7 and 13, highlighting the need for public policies that enhance the competitiveness and scalability of sustainable alternatives. Full article

Solid Oxide Fuel Cells (SOFCs) offer high-efficiency electrochemical conversion of fuels like natural gas, yet detailed modeling is crucial for optimization. This paper presents a simulation study of a natural gas-fueled SOFC system, developed using Aspen Plus with Fortran integration. Distinct from prevalent paradigms assuming rated power output, this work adopts rated current density as the primary input, enabling a more direct investigation of the cell’s electrochemical behavior. We conducted a comprehensive sensitivity analysis of key parameters, including fuel utilization, water-carbon ratio, and current density, and further investigated the impact of different interconnection configurations on overall module performance. Results demonstrate that a single unit operating at a current density of 180 mA/cm², a fuel utilization of 0.75, and a water-carbon ratio of 1.5 can achieve a maximum net stack-level electrical efficiency of 54%. Furthermore, optimizing the interconnection of a 400 kW module by combining series and parallel units boosts the overall net system-level electrical efficiency to 59%, a 5-percentage-point increase over traditional parallel setups. This is achieved by utilizing a bottoming cycle for exhaust heat recovery. This research validates the rated current density approach for SOFC modeling, offering novel insights into performance optimization and modular design for integrated energy systems. Full article