Search Results (2,430)

This study systematically investigates the effects of trap concentration on self-heating and terahertz (THz) responses in GaN HEMTs using Sentaurus TCAD. Traps, inherently unavoidable in semiconductors, can be strategically introduced to engineer specific energy levels that establish competitive dynamics between the electron momentum relaxation time and the carrier lifetime. A simulation-based exploration of this mechanism provides significant scientific value for enhancing device performance through self-heating mitigation and THz response optimization. An AlGaN/GaN heterojunction HEMT model was established, with trap concentrations ranging from 0 to $5\times {10}^{17} {\mathrm{c}\mathrm{m}}^{-3}$. The analysis reveals that traps significantly enhance channel current (achieving 3× gain at $1\times 10^{17} {\mathrm{c}\mathrm{m}}^{-3}$) via new energy levels that prolong carrier lifetime. However, elevated trap concentrations (>$1\times {10}^{16} {\mathrm{c}\mathrm{m}}^{-3}$) exacerbate self-heating-induced current collapse, reducing the min-to-max current ratio to 0.9158. In THz response characterization, devices exhibit a distinct DC component (${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$) under non-resonant detection ($\mathsf{\omega }\mathsf{\tau }\ll 1$). At a trap concentration of $1\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ peaks at $0.12 \mathrm{V}$ when ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-7.8 \mathrm{V}$. Compared to trap-free devices, a maximum response attenuation of 64.89% occurs at ${\mathrm{V}}_{\mathrm{g}}\mathrm{D}\mathrm{C}=-4.9 \mathrm{V}$. Furthermore, ${\mathrm{U}}_{\mathrm{d}\mathrm{c}}$ demonstrates non-monotonic behavior with concentration, showing local maxima at $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ and $7\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$, attributed to plasma wave damping and temperature-gradient-induced electric field variations. This research establishes trap engineering guidelines for GaN HEMTs: a concentration of $4\times {10}^{15} {\mathrm{c}\mathrm{m}}^{-3}$ optimally enhances conductivity while minimizing adverse impacts on both self-heating and the THz response, making it particularly suitable for high-sensitivity terahertz detectors. Full article

The ability to predict capacity retention is critical for ensuring the long-term reliability of supercapacitors in energy storage systems. This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural features to forecast the degradation behavior of commercial supercapacitors. A total of seven supercapacitor samples were tested under various current and voltage conditions, resulting in over 70,000 charge–discharge cycles across three case studies. In addition to electrical measurements, detailed physical and material characterizations were performed, including electrode dimension analysis, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. Results show that incorporating material features consistently improved prediction accuracy across all models. The MLP model exhibited the highest performance, achieving an R² of 0.976 on the training set and 0.941 on unseen data. Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. This study demonstrates that combining electrical and material data offers a more holistic and physically informed approach to supercapacitor health prediction. The framework developed here provides a practical foundation for accurate and robust lifetime forecasting of commercial energy storage devices, highlighting the critical role of material-level insights in enhancing model generalization and reliability. Full article

In the context of building a new type of power system, the optimal operation of high-proportion new-energy distribution networks (HNEDNs) is a current hot topic. In this paper, a stochastic distribution robust optimization method for HNEDNs that considers energy-storage refinement modeling is proposed. First, an energy-storage lifetime loss model based on the rainfall-counting method is constructed, and then an optimal operation model of an HNEDN considering energy storage refinement modeling is constructed, aiming to minimize the total operation cost while taking into account the energy cost and the penalty cost of abandoning wind and solar power. Then, a source-load uncertainty model of HNEDN is constructed based on the Wasserstein distance and conditional value at risk (CvaR) theory, and the HNEDN optimization model is reconstructed based on the stochastic distribution robust optimization method; based on this, the multiple linearization technique is introduced to approximate the reconstructed model, which aims to both reduce the difficulty in solving the model and ensure the quality of the solution. Finally, the modified IEEE 33-bus power distribution system is used as an example for case analysis, and the simulation results show that the method presented in this paper, through reducing the loss of life in the battery storage device, can reduce the average daily energy storage depreciation cost compared to an HNEDN optimization method that does not take the energy storage life loss into account; this, in turn, reduces the total operating cost of the system. In addition, the stochastic distribution robust optimization method used in this paper can adaptively adjust the economy and robustness of the HNEDN operation strategy according to the confidence level and the available historical sample data on new energy-output prediction errors to obtain the optimal HNEDN operation strategy when compared with other uncertainty treatment methods. Full article

This paper introduces a novel and flexible class of continuous probability distributions, termed the Alpha Power Survival Ratio-X (APSR-X) family. Unlike many existing transformation-based families, the APSR-X class integrates an alpha power transformation with a survival ratio structure, offering a new mechanism for enhancing shape flexibility while maintaining mathematical tractability. This construction enables fine control over both the tail behavior and the symmetry properties, distinguishing it from traditional alpha power or survival-based extensions. We focus on a key member of this family, the two-parameter Alpha Power Survival Ratio Exponential (APSR-Exp) distribution, deriving essential mathematical properties including moments, quantile functions and hazard rate structures. We estimate the model parameters using eight frequentist methods: the maximum likelihood (MLE), maximum product of spacings (MPSE), least squares (LSE), weighted least squares (WLSE), Anderson–Darling (ADE), right-tailed Anderson–Darling (RADE), Cramér–von Mises (CVME) and percentile (PCE) estimation. Through comprehensive Monte Carlo simulations, we evaluate the estimator performance using bias, mean squared error and mean relative error metrics. The proposed APSR-X framework uniquely enables preservation or controlled modification of the symmetry in probability density and hazard rate functions via its shape parameter. This capability is particularly valuable in reliability and survival analyses, where symmetric patterns represent balanced risk profiles while asymmetric shapes capture skewed failure behaviors. We demonstrate the practical utility of the APSR-Exp model through three real-world applications: economic (tax revenue durations), engineering (mechanical repair times) and medical (infection durations) datasets. In all cases, the proposed model achieves a superior fit over that of the conventional alternatives, supported by goodness-of-fit statistics and visual diagnostics. These findings establish the APSR-X family as a unique, symmetry-aware modeling framework for complex lifetime data. Full article

Minimizing energy consumption remains a critical challenge in wireless sensor networks (WSNs) because of their reliance on nonrechargeable batteries. Clustering-based hierarchical communication has been widely adopted to address this issue by improving residual energy and balancing the network load. In this architecture, cluster heads (CHs) are responsible for data collection, aggregation, and forwarding, making their optimal selection essential for prolonging network lifetime. The effectiveness of CH selection is highly dependent on the choice of metaheuristic optimization method and the design of the fitness function. Although numerous studies have applied metaheuristic algorithms with suitably designed fitness functions to tackle the CH selection problem, many existing approaches fail to fully capture both the spatial distribution of nodes and dynamic energy conditions. To address these limitations, we propose the binary secretary bird optimization clustering (BSBOC) method. BSBOC introduces a binary variant of the secretary bird optimization algorithm (SBOA) to handle the discrete nature of CH selection. Additionally, it defines a novel multiobjective fitness function that, for the first time, considers the Voronoi diagram of CHs as an optimization objective, besides other well-known objectives. BSBOC was thoroughly assessed via comprehensive simulation experiments, benchmarked against two advanced methods (MOBGWO and WAOA), under both homogeneous and heterogeneous network models across two deployment scenarios. Findings from these simulations demonstrated that BSBOC notably decreased energy usage and prolonged network lifetime, highlighting its effectiveness as a reliable method for energy-aware clustering in WSNs. Full article

The dynamic control of vibrations in skyscrapers is a critical consideration in sustainable building design, particularly in response to environmental excitations such as wind impact or seismic activity. Effective vibration neutralisation plays a crucial role in providing the safety of high-rise buildings. This research introduces an innovative concept for an active vibration damper that operates based on fluid dynamic transport to adaptively alter a skyscraper’s natural frequency, thereby counteracting resonant vibrations. A distinctive feature of this system is an adjustable-length cable mechanism, allowing for the dynamic modification of the pendulum’s effective length in real time. The structure, based on cable length adjustment, enables the PTMD to precisely tune its natural frequency to variable excitation conditions, thereby improving damping during transient or resonance phenomena of the building’s dynamic behaviour. A comprehensive mathematical model based on Lagrangian mechanics outlines the governing equations for this system, capturing the interactions between pendulum motion, fluid flow, and the damping forces necessary to maintain stability. Simulation analyses examine the role of initial excitation frequency and variable damping coefficients, revealing critical insights into optimal damper performance under varied structural conditions. The findings indicate that the proposed pendulum damper effectively mitigates resonance risks, paving the way for sustainable skyscraper design through enhanced structural adaptability and resilience. This adaptive PTMD, featuring an adjustable-length cable, provides a solution for creating safe and energy-efficient skyscraper designs, aligning with sustainable architectural practices and advancing future trends in vibration management technology. The study presented in this article supports the development of modern skyscraper design, with a focus on dynamic vibration control for sustainability and structural safety. It combines advanced numerical modelling, data-driven control algorithms, and experimental validation. From a sustainability perspective, the proposed PTMD system reduces the need for oversized structural components by providing adaptive, efficient damping, thereby lowering material consumption and embedded carbon. Through dynamically retuning structural stiffness and mass, the proposed PTMD enhances resilience and energy efficiency in skyscrapers, lowers lifetime energy use associated with passive damping devices, and enhances occupant comfort. This aligns with global sustainability objectives and new-generation building standards. Full article

Highly scattering media have garnered significant interest in recent years, ranging from potential applications in solar cells, photocatalysis, and other novel photonic devices to research on fundamental topics such as topological photonics, enhanced light–matter coupling and light confinement. Here, we report measurements of spectrally and time-resolved delayed luminescence (DL) in highly scattering rutile TiO₂ films. The complex emission kinetics manifests in the non-exponential decay of photon density and the temporal evolution of the spectral composition. We found that while the energy levels of TiO₂ nanoparticles broadly set the spectral regions of excitation and emission, our results demonstrate that the DL intensity and duration are strongly influenced by the inherent multiple elastic and inelastic processes determined by the mesoscale inhomogeneous structure of random media. We show that the lifetime of DL increases up to 6 s for the largest redshift detected, which is associated with multiple reabsorption processes. We outline a simple model for spectrally resolved DL emission from dense scattering media that can guide the design and characterization of composite materials with specific spectral and temporal properties. Full article

This study presents the design and techno-economic comparison of two standalone photovoltaic (PV) systems, each supplying a 1 kW critical load with 100% reliability under Cairo’s climatic conditions. These systems are modeled for both the constant and the night load scenarios, accounting for the worst-case weather conditions involving 3.5 consecutive cloudy days. The primary comparison focuses on traditional lead-acid battery storage versus green hydrogen storage via electrolysis, compression, and fuel cell reconversion. Both the configurations are simulated using a Python-based tool that calculates hourly energy balance, component sizing, and economic performance over a 21-year project lifetime. The results show that the PV/H₂ system significantly outperforms the PV/lead-acid battery system in both the cost and the reliability. For the constant load, the Levelized Cost of Electricity (LCOE) drops from 0.52 USD/kWh to 0.23 USD/kWh (a 56% reduction), and the payback period is shortened from 16 to 7 years. For the night load, the LCOE improves from 0.67 to 0.36 USD/kWh (a 46% reduction). A supplementary cost analysis using lithium-ion batteries was also conducted. While Li-ion improves the economics compared to lead-acid (LCOE of 0.41 USD/kWh for the constant load and 0.49 USD/kWh for the night load), this represents a 21% and a 27% reduction, respectively. However, the green hydrogen system remains the most cost-effective and scalable storage solution for achieving 100% reliability in critical off-grid applications. These findings highlight the potential of green hydrogen as a sustainable and economically viable energy storage pathway, capable of reducing energy costs while ensuring long-term resilience. Full article

The Internet of Things (IoT) is a fast-growing internet technology and has been incorporated into a wide range of fields. The optimal design of IoT systems has several challenges. The energy consumption of the devices is one of these IoT challenges, particularly for open-air IoT applications. The major energy consumption takes place due to inefficient routing, which can be addressed by the energy-efficient clustering method. In addition, the energy-harvesting method can also play a significant role in increasing the overall lifetime of the network. Therefore, in the proposed work, a novel energy-efficient dual energy management and saving model is proposed to manage the energy consumption of IoT networks. This model uniquely integrates energy-efficient clustering with solar energy harvesting (SEH) to address IoT energy challenges. The dual elbow method is utilized for efficient clustering to ensure guaranteed quality of service (QoS), while SEH enhances energy sustainability. The proposed method is implemented for high-density sensor network applications. Simulation results demonstrate a 25% reduction in overall energy consumption and a 20% increase in network lifetime compared to existing methods. Our model will be able to manage energy consumption and increase the IoT network’s overall lifetime by optimizing IoT devices’ energy consumption. Full article

Laboratory data from in-cell tests at and near open circuit potentials (OCV) and ex-situ H₂O₂ vapor exposure tests are used to develop a fluoride emission rate (FER) model for a state-of-the-art 12-µm thin, low equivalent weight, long-chain perfluorosulfonic acid (PFSA) ionomer membrane that is mechanically reinforced with expanded PTFE and chemically stabilized with 2 mol% cerium as an anti-oxidant. The anode FER at OCV linearly correlates with O₂ crossover from the cathode and the high yield of H₂O₂ at anode potentials, as observed in rotating ring disk electrode (RRDE) studies. The cathode FER may be linked to the energetic formation of reactive hydroxyl radicals (·OH) from the decomposition of H₂O₂ produced as an intermediate in the two-electron ORR pathway at high cathode potentials. Both anode and cathode FERs are significantly enhanced at low relative humidity and high temperatures. The modeled FER is strongly influenced by the gradients in water activity and cerium concentration that develops in operating fuel cells. Membrane stability maps are constructed to illustrate the relationship between the cell voltage, temperature, and relative humidity for FER thresholds that define H₂ crossover failure by chemical degradation over a specified lifetime. Full article

The risk of developing symptomatic knee osteoarthritis (KOA) during a lifetime, i.e., pain, aching, or stiffness in a joint associated with radiographic KOA, was estimated in 2008 to be around 40% in men and 47% in women. The clinical and scientific communities lack an efficient diagnostic method to effectively monitor, evaluate, and predict the evolution of KOA before and during the therapeutic protocol. In this review, we summarize the main methods that are used or seem promising for the diagnosis of osteoarthritis, with a specific focus on non- or low-invasive methods. As standard diagnostic tools, arthroscopy, magnetic resonance imaging (MRI), and X-ray radiography provide spatial and direct visualization of the joint. However, discrepancies between findings and patient feelings often occur, indicating a lack of correlation between current imaging methods and clinical symptoms. Alternative strategies are in development, including the analysis of biochemical markers or acoustic emission recordings. These methods have undergone deep development and propose, with non- or minimally invasive procedures, to obtain data on tissue condition. However, they present some drawbacks, such as possible interference or the lack of direct visualization of the tissue. Other original methods show strong potential in the field of KOA monitoring, such as electrical bioimpedance or near-infrared spectrometry. These methods could permit us to obtain cheap, portable, and non-invasive data on joint tissue health, while they still need strong implementation to be validated. Also, the use of Artificial Intelligence (AI) in the diagnosis seems essential to effectively develop and validate predictive models for KOA evolution, provided that a large and robust database is available. This would offer a powerful tool for researchers and clinicians to improve therapeutic strategies while permitting an anticipated adaptation of the clinical protocols, moving toward reliable and personalized medicine. Full article

The increasing share of electric vehicles (EVs) offers many advantages, including a reduced CO₂ footprint over the vehicles’ lifetime and improved resource efficiency through the recycling of high-voltage batteries. At the same time, the growing EV share presents challenges, such as ensuring sufficient power supply for the simultaneous charging of EVs within existing distribution grids. The scientific community has conducted numerous studies on the interaction between EVs and distribution grids, employing increasingly complex modeling techniques. However, the benefits of more complex modeling are rarely quantified. This study aims to address this gap by evaluating the impact of modeling complexity on transformer peak loads and busbar voltage for three communities with real-world distribution grid data. Since numerous stochastic factors influence EV charging patterns, this paper introduces a modular framework that accounts for the interconnection of these factors through microsimulation. The framework models charging events of battery electric vehicles (BEVs) and comprises modules for synthetic population generation, weekly mobility pattern assignment, and energy demand modeling based on vehicle class and ambient conditions. The findings reveal that cost-optimized charging strategies and seasonal factors, such as cold weather, have a significantly greater impact on the distribution grid than the detailed modeling of sociodemographic mobility patterns or detailed modeling of a diversified vehicle fleet. Full article

Background: Physical activity (PA) has been shown to prevent Alzheimer’s disease (AD) by reducing amyloid accumulation, lowering inflammatory factors, and increasing hippocampal grey matter. While high lifetime PA engagement is associated with a reduced risk of AD, the relationship between specific types of PA and early-stage AD remains unclear. As AD primarily affects cognitive function before physical capabilities, PA engagement—an important factor in PA—needs further investigation. Objectives: This study explores the potential association between current participation in open-skill sports (OSSs) versus closed-skill sports (CSSs) and early-stage AD. Methods: The sample (N = 128) included a cognitively healthy (HC, n = 78) group and an Alzheimer’s disease (AD) group, combining amnestic mild cognitive impairment due to AD patients (n = 22) and early-stage Alzheimer’s dementia patients (n = 28), reflecting the continuum of progression from aMCI to dAD (n = 50). PA was assessed with the Physical Activity Scale for the Elderly questionnaire, specifically focusing on PA within the last seven days. The statistical analyses included Mann–Whitney U tests and backwards stepwise logistic regression models. Results: Key predictors of group classification (AD vs. NC) included sex, high frequency of PA, and high duration of PA, each for the last seven days. Participation in OSS was significantly associated with medium PA frequency, high PA duration, both within the last seven days, and age, but not with diagnostic status. No statistically significant differences in PA levels (OSSs or CSSs) executed within the last seven days were observed between the AD and HC groups. Conclusions: Participation in OSSs or CSSs within the last seven days was only a marginally significant predictor of AD vs. HC status, and a diagnosis of AD was not predictive of OSS participation within the last seven days. Given the protective role of PA in AD, future research should aim to identify specific PA types that effectively support cognitive health in older adults with early cognitive decline. Full article

Modelling aging in the second life of lithium-ion batteries (LiBs) is challenging due to the complexity of degradation mechanisms that lead to capacity loss and internal resistance increase, as well as uncertainty and variability in the operational and environmental conditions to which the batteries are exposed. In this work, we propose a similarity-based approach for diagnosing the aging of LiBs in their second life, which combines time series analysis and machine learning to help identify trends and patterns in the aging process. This approach overcomes the intrinsic nonlinearity nature of the LiB aging trajectory in the second life while adapting to varying operational and environmental conditions. Knees or inflection points defining the first, second, and non-usable lives of the batteries are also identified, offering insights into degradation mechanisms and thus supporting thermal management and optimal user-pattern tasks to extend the LiBs’ lifetime. Full article

This study presents the development of a textile-based cascade filter for the removal of microplastics from an industrial laundry effluent. The cascade microfilter consists of three stages of 3D textile sandwich composite filter media, which have successively finer pores and are aimed at filtering microplastic particles down to 1.5 µm. Polypropylene fabrics with pore sizes of 100, 50 and 20 µm and 3D warp-knitted fabrics with high porosity (96%) were used. Filtration tests were carried out with polyethylene model microplastic particles at a concentration of 167 mg/L. To regenerate the filter and restore its filtration performance, backwashing with filtered water and compressed air was applied. Field trials at an industrial laundry facility and a municipal wastewater treatment plant confirmed high removal efficiencies. The 3D textile sandwich structure promotes filter cake formation, allowing extended backwash intervals and the effective recovery of filtration capacity between 89.7% and 98.5%. The innovative use of 3D textile composites enables a high level of microplastic removal while extending the filter media lifetime. This makes a significant contribution to the reduction in microplastic emissions in the aquatic environment. The system is scalable, space and cost efficient and adaptable to various industrial applications and is thus a promising solution for advanced wastewater treatment. Full article