Search Results (209)

Irregular multistorey buildings are prone to seismic forces due to torsional effects resulting from the eccentricity between the mass and stiffness centres. Shear walls are essential in multistorey buildings for improving structural behaviour when subjected to earthquake loads. The seismic response of buildings is highly sensitive to the placement and configuration of shear walls within the building infrastructure. This research focuses on optimising the location of shear walls in a T-shaped irregular reinforced concrete structure for better seismic resilience. The structural analysis is carried out, and the building is evaluated via the response spectrum as per the provisions of IS 1893:2016. This study examines various shear wall configurations to achieve optimised modal mass participation, thereby reducing dynamic irregularities and enhancing overall seismic performance. The impact of these optimised locations is assessed across various seismic zones in India, with a focus on critical response parameters, including lateral displacement, interstorey drift, storey shear, and base shear. The results reveal that strategically optimised shear wall placement significantly enhances seismic performance by reducing lateral drift and torsional effects. In this study, the shear wall configurations that resulted in higher modal participation factors and lower eccentricities between the centre of mass and the centre of stiffness demonstrated a superior seismic performance across all considered seismic zones. Full article

The Vehicular Internet of Things (V-IoT) has emerged as a cornerstone of next-generation intelligent transportation systems (ITSs), enabling applications ranging from safety-critical collision avoidance and cooperative awareness to infotainment and fleet management. These heterogeneous services impose stringent quality-of-service (QoS) demands for latency, reliability, and fairness while competing for limited and dynamically varying spectrum resources. Conventional schedulers, such as round-robin or static priority queues, lack adaptability, whereas deep reinforcement learning (DRL) solutions, though powerful, remain computationally intensive and unsuitable for real-time roadside unit (RSU) deployment. This paper proposes a lightweight and interpretable reinforcement learning (RL)-based spectrum management framework for Vehicular Internet of Things (V-IoT) networks. Two enhanced Q-Learning variants are introduced: a Value-Prioritized Action Double Q-Learning with Constraints (VPADQ-C) algorithm that enforces reliability and blocking constraints through a Constrained Markov Decision Process (CMDP) with online primal–dual optimization, and a contextual Q-Learning with Upper Confidence Bound (Q-UCB) method that integrates uncertainty-aware exploration and a Success-Rate Prior (SRP) to accelerate convergence. A Risk-Aware Heuristic baseline is also designed as a transparent, low-complexity benchmark to illustrate the interpretability–performance trade-off between rule-based and learning-driven approaches. A comprehensive simulation framework incorporating heterogeneous traffic classes, physical-layer fading, and energy-consumption dynamics is developed to evaluate throughput, delay, blocking probability, fairness, and energy efficiency. The results demonstrate that the proposed methods consistently outperform conventional Q-Learning and Double Q-Learning methods. VPADQ-C achieves the highest energy efficiency (≈$8.425\times {10}^7$ bits/J) and reduces interruption probability by over $60\%$, while Q-UCB achieves the fastest convergence (within ≈190 episodes), lowest blocking probability (≈0.0135), and lowest mean delay (≈0.351 ms). Both schemes maintain fairness near $0.364$, preserve throughput around 28 Mbps, and exhibit sublinear training-time scaling with $\mathcal{O}\left(1\right)$ per-update complexity and $\mathcal{O}\left(N^2\right)$ overall runtime growth. Scalability analysis confirms that the proposed frameworks sustain URLLC-grade latency (<0.2 ms) and reliability under dense vehicular loads, validating their suitability for real-time, large-scale V-IoT deployments. Full article

Load spectrum enhancement is a pivotal accelerated fatigue testing methodology employed to substantially reduce test duration and associated costs. This technique operates by strategically elevating load amplitudes while ensuring the preservation of the original failure mechanism. In this study, a novel fatigue life prediction model for variable amplitude loading is developed by integrating the theories of Equivalent Initial Flaw Size (EIFS) and the Equivalent Plastic Zone (EPZ). This integrated approach explicitly accounts for both the small crack effect and load interaction effects, which are critical yet often oversimplified aspects of fatigue damage accumulation. The model is subsequently applied to quantitatively establish the relationship between the Load Enhancement Factor (LEF) and the test time or compression ratio. Finally, fatigue tests on typical 2A14 aluminum alloy structures under variable amplitude loading are conducted to validate the proposed model. The results demonstrate a significant life reduction with increasing LEF, achieving a remarkable test time reduction of over 50% at an LEF of 1.2. All experimental data fall within a scatter band of three, relative to the model prediction. Additionally, the predicted mean compression ratio exhibits approximate agreement with the experimental data, with errors within an acceptable range. This work provides a physically grounded and practically validated framework for implementing efficient and reliable load spectrum enhancement. Full article

This study numerically investigates and designs, through electromagnetic and ray-tracing simulations, two types of double-sided metasurface thermal insulation glazing to maintain visible light (VIS) transmittance while effectively suppressing near-infrared (NIR) transmission, with a partial reduction in deep-blue (DB) transmission, thus reducing air-conditioning load and lighting energy consumption and contributing to overall building energy efficiency. Both designs were optimized and analyzed entirely through simulations, using structural parameter sweeps and AM 1.5 solar spectrum weighting. Design I is composed of two all-dielectric metasurfaces, aiming to maximize VIS transmittance while partially suppressing DB and reducing NIR transmission. Design II integrates a metallic layer with dielectric structures on the front side and employs an all-dielectric metasurface on the back side to enhance NIR blocking and maintain low transmittance under oblique incidence. Simulation results show that Design II outperforms Design I in NIR suppression, exhibiting lower and more stable transmittance across incident angles, while Design I achieves higher VIS transmittance. These findings present a promising pathway for developing high-performance, lightweight glazing for sustainable buildings, improving energy efficiency by balancing solar heat control and daylight utilization. Full article

With the development of offshore wind power towards deep-sea areas, the offshore valve tower, as a key facility of offshore wind farms, plays a vital role in ensuring the stable operation of the system. To investigate its dynamic response characteristics under seismic loading, a 1:25 physical test model of the valve tower was constructed based on the gravity–elasticity similarity principle. Acceleration responses at the first deck of a 1:65 scale offshore converter platform model were obtained through shaking-table tests and applied as base excitation to the valve tower model. The experimental results reveal that the frequency domain response of the valve tower transitions from high-frequency dominance at the base to low-frequency dominance at the top, with the structural weak link located at the mid-connection between the front and rear sub-towers. The fundamental frequency of the valve tower is 3.92 Hz, and the average damping ratio is 3.21%. The shake table test of the converter valve tower was verified using the gravity–elasticity similarity law, effectively reproducing the seismic response characteristics of the prototype. This provides crucial data for seismic response spectrum analysis, identifies structural weaknesses, and offers guidance for the design of more earthquake-resistant offshore valve towers, thus enhancing the safety of deep-sea wind farms. Full article

The antibacterial properties of Ag(I) coordination compounds are well documented; however, their effectiveness is highly dependent on the choice of appropriate ligands, and it is frequently hindered by their low water solubility and limited bioavailability. Herein, six new Ag(I) complexes incorporating the quinazolinone thioamide mqztH (=2-mercapto-4(3H)-quinazolinone) and phosphine co-ligands were synthesized and investigated for their antibacterial activity. In vitro activity assays against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) bacterial strains revealed that all complexes selectively inhibited S. aureus bacterial growth. Structure–activity relationship analysis showed that monodentate PPh₃ co-ligands play a key role in enhancing the antibacterial efficacy of their complexes. Notably, complex [AgCl(mqztH)(PPh₃)₂] (1) exhibited broad-spectrum activity, with IC₅₀ values of 4.2 ± 1.4 μg mL⁻¹ (4.9 μΜ) for S. aureus and 63 ± 1.9 μg mL⁻¹ (75 μΜ) for E. coli bacteria. To improve solubility and antibacterial activity, complex 1 was encapsulated in barium alginate (BaAlg) matrices to form hydrogel-based drug delivery formulations [1]@BaAlg. The synthesized formulations retained the bactericidal effect of the complex, achieving comparable activity at concentrations lower by an order of magnitude compared to complex 1 in free form. Combined with the demonstrated high biocompatibility of complex 1 toward L929 normal eukaryotic cells, as well as the biocompatible nature of the alginate matrix, these findings underscore the strong potential of the complex 1-loaded hydrogel formulations for further investigation and development as effective antibacterial drug platforms. Mechanistic studies confirmed the redox-active nature of complex 1 and its potential to inhibit the function of glutathione reductase (GR) and thioredoxin reductase (TrxR) at low concentrations, suggesting the interference with bacterial redox homeostasis as a relevant mechanism of bioactivity. Full article

In this paper, we propose a load-aware, load-balancing procedure for fifth-generation (5G) New Radio-Unlicensed (NR-U) networks in order to address performance degradation and resource inefficiencies caused by load imbalance. Load imbalances frequently occur in NR-U networks due to factors such as the dynamic spectrum, user mobility, and varying traffic demand. To tackle these challenges, a load-aware, load-balancing procedure utilizing game theoretic reinforcement learning (GT-RL) is introduced. For load awareness, an extended System Information Block (SIB) is incorporated within the framework of 5G wireless networks. The load-balancing problem is addressed as a game theoretic cost-minimization task combining conditional offloading with reinforcement learning traffic-steering to dynamically distribute loads. Reinforcement learning applies a game theoretic policy to move users from overloaded cells to less congested cells that best serve their needs. Analytically, the proposed method is proven to spread the network load toward equilibrium. The proposed method is validated through simulations that show the effectiveness of its load balancing. The proposed method achieved better performance than previous work by attaining lower load variances while achieving higher throughput and greater quality of service satisfaction. Especially under high-load dynamics, the proposed method achieved an 8% gain in UE satisfaction with QoS and a 7.61% gain in network throughput compared to existing RL-based approach, whereas compared to the non-AI approaches, UE QoS satisfaction and the network throughput were enhanced by more than 15%. Full article

Chronic wounds, such as diabetic ulcers, remain a significant clinical challenge due to high infection risk and delayed healing. This study presents a comprehensive evaluation of a novel wound dressing incorporating curcumin-functionalized silver–zinc oxide (Ag-ZnO) nanoparticles. The formulation was rationally designed based on molecular docking simulations that identified curcumin as a high-affinity ligand for Staphylococcus aureus Protein A. The synthesized nanoparticles demonstrated potent, broad-spectrum antibacterial activity, achieving complete inhibition of multidrug-resistant pathogens, including MRSA, within 60 s. A critical comparative assessment, incorporating an unloaded Ag-ZnO nanoparticle control group, was conducted in both a rabbit wound model and a randomized clinical trial (n = 75 patients). This design confirmed that the enhanced wound-healing efficacy is specifically attributable to the synergistic effect of curcumin combined with the nanoparticles. The curcumin-loaded Ag-ZnO treatment group showed a statistically significant reduction in healing time compared to both standard care and unloaded nanoparticle controls (e.g., medium wounds: 19.6 days vs. 90.6, p < 0.001). These findings demonstrate that curcumin-functionalized Ag-ZnO nanoparticles offer a safe and highly effective therapeutic strategy, providing robust antibacterial action and significantly accelerated wound healing. Full article

Brushless DC (BLDC) motors are commonly used in electric vehicles (EVs) because of their efficiency, small size and great torque-speed performance. These motors have a few benefits such as low maintenance, increased reliability and power density. Nevertheless, BLDC motors are highly nonlinear and their dynamics are very complicated, in particular, under changing load and supply conditions. The above features require the design of strong and adaptable control methods that can ensure performance over a broad spectrum of disturbances and uncertainties. In order to overcome these issues, this paper uses a Fractional-Order Proportional-Integral-Derivative (FOPID) controller that offers better control precision, better frequency response, and an extra degree of freedom in tuning by using non-integer order terms. Although it has the benefits, there are three primary drawbacks: (i) it is not real-time adaptable, (ii) it is hard to choose appropriate initial gain values, and (iii) it is sensitive to big disturbances and parameter changes. A new control framework is suggested to address these problems. First, a Reinforcement Learning (RL) approach based on Deep Deterministic Policy Gradient (DDPG) is presented to optimize the FOPID gains online so that the controller can adjust itself continuously to the variations in the system. Second, Snake Optimization (SO) algorithm is used in fine-tuning of the FOPID parameters at the initial stages to guarantee stable convergence. Lastly, cascade control structure is adopted, where FOPID controllers are used in the inner (current) and outer (speed) loops. This construction adds robustness to the system as a whole and minimizes the effect of disturbances on the performance. In addition, the cascade design also allows more coordinated and smooth control actions thus reducing stress on the power electronic switches, which reduces switching losses and the overall efficiency of the drive system. The suggested RL-enhanced cascade FOPID controller is verified by Hardware-in-the-Loop (HIL) testing, which shows better performance in the aspects of speed regulation, robustness, and adaptability to realistic conditions of operation in EV applications. Full article

Background: Skin mycoses affect approximately 10% of the global population, and the range of effective topical antifungal agents remains limited. Voriconazole (VRC) is a broad-spectrum triazole with proven efficacy against drug-resistant fungal infections. This study aimed to develop and optimize VRC-loaded microemulsion (ME) polymer gels (Carbopol^®-based) for cutaneous delivery. Selected formulations also contained menthol (2%) as a penetration enhancer and potential synergistic antifungal agent. Methods: A comprehensive screening was performed using pseudoternary phase diagrams to identify stable oil/surfactant/co-surfactant/water systems. Selected MEs were prepared with triacetin, Etocas™ 35, and Transcutol^®, then gelled with Carbopol^®. Formulations were characterized for pH, droplet size, polydispersity index (PDI), and viscosity. In vitro VRC release was assessed using diffusion cells, while ex vivo permeation and skin deposition studies were conducted on full-thickness human skin. Rheological behavior (flow curves, yield stress) and texture (spreadability) were evaluated. Antifungal activity was tested against standard strain of Candida albicans and clinical isolates including a fluconazole-resistant strain. Results: The optimized ME (pH ≈ 5.2; droplet size ≈ 2.8 nm) was clear and stable with both VRC and menthol. Gelation produced non-Newtonian, shear-thinning hydrogels with low thixotropy, favorable for topical application. In ex vivo studies, performed with human skin, both VRC-loaded gels deposited the drug in the epidermis and dermis, with no detectable amounts in the receptor phase after 24 h, indicating retention within the skin. Menthol increased VRC deposition. Antifungal testing showed that VRC-containing gels produced large inhibition zones against C. albicans, including the resistant isolate. The VRC–menthol gel exhibited significantly greater inhibition zones than the VRC-only gel, confirming synergistic activity. Conclusions: ME-based hydrogels effectively delivered VRC into the skin. Menthol enhanced drug deposition and demonstrated synergistic antifungal activity with voriconazole. Full article

Ice loads continue to pose challenges to the structural safety of offshore wind turbines (OWTs), while the rapid development of offshore wind power in cold regions is enabling the deployment of OWTs in deeper waters. To accurately simulate the dynamic response of an OWT under combined ice–wind loading, this paper proposes a Discrete Element Method–Wind Turbine Integrated Analysis (DEM-WTIA) framework. The framework can synchronously simulate discontinuous ice-crushing processes and aeroelastic–structural dynamic responses through a holistic turbine model that incorporates rotor dynamics and control systems. To address the issue of insufficient prediction accuracy for dynamic responses, we introduced a multivariate time series forecasting method that integrates a secondary decomposition strategy with a hybrid prediction model. First, we developed a parallel signal processing mechanism, termed Adaptive Complete Ensemble Empirical Mode Decomposition with Improved Singular Spectrum Analysis (CEEMDAN-ISSA), which achieves adaptive denoising via permutation entropy-driven dynamic window optimization and multi-feature fusion-based anomaly detection, yielding a noise suppression rate of 76.4%. Furthermore, we propose the F-Transformer prediction model, which incorporates a Frequency-Enhanced Channel Attention Mechanism (FECAM). By integrating the Discrete Cosine Transform (DCT) into the Transformer architecture, the F-Transformer mines hidden features in the frequency domain, capturing potential periodicities in discontinuous data. Experimental results demonstrate that signals processed by ISSA exhibit increased signal-to-noise ratios and enhanced fidelity. The F-Transformer achieves a maximum reduction of 31.86% in mean squared error compared to the standard Transformer and maintains a coefficient of determination (R²) above 0.91 under multi-condition coupled testing. By combining adaptive decomposition and frequency-domain enhancement techniques, this framework provides a precise and highly adaptable ultra-short-term response forecasting tool for the safe operation and maintenance of offshore wind power in cold regions. Full article

Accurately modelling and simulating the stiffness modulus of asphalt mixtures is essential for reliable pavement design and performance prediction under varying environmental and loading conditions. The preceding is commonly achieved through master curves, which relate stiffness to loading frequency at a reference temperature. However, conventional master curves face two primary limitations. Firstly, temperature is not treated as a state variable; instead, its effect is indirectly considered through shift factors, which can introduce inaccuracies due to their lack of thermodynamic consistency across the entire range of possible temperatures. Secondly, conventional master curves often encounter convergence difficulties when calibrated with experimental data constrained to a narrow frequency spectrum. In order to address these shortcomings, this investigation proposes a novel formulation known as the Thermo-Stiffness Integration (TSI) model, which explicitly incorporates both temperature and frequency as state variables to predict the stiffness modulus directly, without relying on supplementary expressions such as shift factors. The TSI model is built on thermodynamics-based principles (such as Eyring’s rate theory and activation free energy) and leverages the time–temperature superposition principle to create a physically consistent representation of the mechanical behaviour of asphalt mixtures. This manuscript presents the development of the TSI model along with its application in a case study involving eight asphalt mixtures, including four hot-mix asphalts and four warm-mix asphalts. Each type of mixture contains recycled concrete aggregates at replacement levels of 0%, 15%, 30%, and 45% as partial substitutes for coarse natural aggregates. This diverse set of materials enables a robust evaluation of the model’s performance, even under non-traditional mixture designs. For this case study, the TSI model enhances computational stability by approximately 4 to 45 times compared to conventional master curves. Thus, the main contribution of this research lies in establishing a valuable mathematical tool for both scientists and practitioners aiming to improve the design and performance assessment of asphalt mixtures in a more physically realistic and computationally stable approach. Full article

The reliability of the EDS (Electric Drive System) in electric vehicles is crucial to overall vehicle performance. This study addresses the challenge of acquiring high-fidelity internal load data in the early development phase due to the absence of prototypes, overcoming the limitations of traditional road tests, which are costly, time-consuming, and unable to measure gear meshing forces. A method based on a VPG (Virtual Proving Ground) is proposed to acquire internal loads of a dual-motor EDS, analyze the impact of typical virtual fatigue durability road conditions on critical components, and generate load spectra for test bench experiments. Through point cloud data-based road modeling and rigid-flexible coupled simulation, dynamic loads are accurately extracted, with pseudo-damage contributions from eight intensified road conditions quantified using pseudo-damage calculations, and equivalent sinusoidal load spectra generated using the rainflow counting method and linear cumulative damage theory. Compared to the limitations of existing VPG methods that rely on simplified models, this study enhances the accuracy of internal load extraction, providing technical support for EDS durability testing. Building on existing research, it focuses on high-fidelity acquisition of EDS loads and load spectrum generation, improving applicability and addressing deficiencies in simulation accuracy. This study represents a novel application of VPG technology in electric drive system development, resolving the issue of insufficient early-stage load spectra. It provides data support for durability optimization and bench testing, with future validation planned using real vehicle data. Full article

Series arc faults are a major cause of electrical fires, posing significant risks to life and property. Their negative-resistance characteristics make fault features difficult to detect, and the existing methods often suffer from high false-alarm rates, poor adaptability, and reliance on high sampling rates and long sampling windows. To enhance the accuracy and efficiency of series AC arc fault detection, this paper proposes a rapid identification method based on singular spectrum statistical features and a differential evolution-optimized XGBoost classifier. The approach first constructs the singular spectrum of current waveforms via a Hankel matrix singular value decomposition and extracts nine statistical features. It then optimizes seven XGBoost hyperparameters using differential evolution to build an efficient classification model. The experiments on 18,240 current samples covering 16 load conditions (including eight arc fault types) show that the method achieves an average identification accuracy of 98.90% using only three nominal cycles (60 ms) of current waveform. Even with a training set ratio as low as 5%, it maintains 97.11% accuracy, outperforming Back-propagation Neural Network, Support Vector Machine, and Recurrent Neural Network methods by up to three percentage points. The method avoids the need for high sampling rates or complex time–frequency transformations, making it suitable for resource-constrained embedded platforms and offering a generalizable solution for series arc fault detection. Full article

Bacterial diseases are a major constraint to aquaculture productivity, driving extensive antibiotic use and raising concerns over antimicrobial resistance, environmental contamination, and food safety. Curcumin, a polyphenolic compound from Curcuma longa, exhibits broad-spectrum antimicrobial and immunomodulatory activities but is limited by poor water solubility, instability, and low bioavailability. This review was conducted through a literature search of Scopus, PubMed, Web of Science, and Google Scholar using targeted keywords, including curcumin nanoparticles, antibacterial, aquatic pathogens, nanotechnology, synthesis, and disease control. Titles and abstracts were screened for relevance, followed by full-text evaluation of selected studies. Key findings were critically analyzed and incorporated into the review. Findings from the literature indicate that curcumin nanoparticles, synthesized via milling, anti-solvent precipitation, ionic gelation, emulsification, spray drying, and metal/polymer nanocomposite formation, exhibit enhanced antibacterial activity against aquatic pathogens, including Aeromonas hydrophila, Vibrio parahaemolyticus, Escherichia coli, and Staphylococcus aureus. Optimally engineered curcumin nanoparticles (<100 nm, being mostly spherical, highly negatively charged) can penetrate bacterial membranes, disrupt biofilms, lower minimum inhibitory concentrations, and improve in vivo fish survival. Practical applications include dietary supplementation to boost fish immunity and growth, water disinfection to reduce pathogen loads, immersion therapy for external infections, and antimicrobial coatings for aquaculture equipment and surfaces, resulting in reduced infections and outbreaks, reduced mortality, improved water quality, and decreased antibiotic dependence. In conclusion, curcumin nanoparticles and curcumin-based nanocomposites present a versatile, eco-friendly approach to sustainable aquaculture disease management. However, further field-scale validation, safety assessment, and cost-effective production methods are necessary to enable commercial adoption. Full article