Search Results (527)

This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically. Full article

The energy transition toward greater electrification leads to incentives in public transportation fed by catenary-powered networks. In this context, emerging technological devices such as in-motion-charging vehicles and electric vehicle charging points are expected to be operated while connected to trolleybus networks as part of new electrification projects, resulting in a significant demand for power. To enable a significant increase in electric transportation without compromising technical compliance for voltage and current at grid systems, the implementation of stationary battery energy storage systems (BESSs) can be essential for new electrification projects. A key challenge for BESSs is the selection of the optimal converter topology for charging their batteries. Ideally, the chosen converter should offer the highest efficiency while minimizing size, weight, and cost. In this context, a modular dual-active-bridge converter, considering its operation as a full-power converter (FPC) and a partial-power converter (PPC) with module-shedding control, is analyzed in terms of operation efficiencies and thermal behavior. The goal is to clarify the advantages, disadvantages, challenges, and trade-offs of both power-processing techniques following future trends in the electric transportation sector. The results indicate that the PPC achieves an efficiency of 98.58% at the full load of 100 kW, which is 1.19% higher than that of FPC. Additionally, higher power density and cost effectiveness are confirmed for the PPC. Full article

Power hardware-in-the-loop (PHIL) creates a safe test environment to connect simulations with real hardware under test (HuT). Therefore, an interface algorithm (IA) must be chosen. The ideal transformer method (ITM) and the partial circuit duplication (PCD) are popular IAs, where a distinction is made between voltage- (V-) and current-type (C-) IAs. Depending on the sample time of the simulator and further delays, simulation accuracy is reduced and instability can occur due to negative feedback in the V-ITM and C-ITM control loops, which makes PHIL operation impossible. In the case of positive feedback, such as with the V-PCD and C-PCD, the delay causes destructive interference, which results in a phase shift and attenuation of the output signal. In this article, a novel damped Smith predictor (SP) for positive feedback PHIL IAs is presented, which significantly reduces destructive interference while allowing stable operation at low linking impedances at V-PCD and high linking impedances at C-PCD, thus reducing losses in the system. Experimental results show a reduction in phase shift by 21.17° and attenuation improvement of 24.3% for V-PCD at a sample time of 100 µs. The SP transfer functions are also derived and integrated into the listed negative feedback IAs, resulting in an increase in the gain margin (GM) from approximately one to three, which significantly enhances system stability. The proposed methods can improve stability and accuracy, which can be further improved by calculating the HuT impedance in real-time and dynamically adapting the SP model. Stable PHIL operation with SP is also possible with SP model errors or sudden HuT impedance changes, as long as deviations stay within the presented limits. Full article

Dual-motor electric vehicles enhance power performance and overall output capabilities by enabling the real-time control of the torque distribution between the front and rear wheels, thereby improving handling, stability, and safety. In addition to increased energy efficiency, a dual-motor system provides redundancy: if one motor fails, the other can still supply partial power, further enhancing driving safety. This study aimed to optimize the energy management strategies of the front- and rear-axis motors, examining the application effects of rule-based control (RBC), global grid search (GGS), and the whale optimization algorithm (WOA). A simulation platform based on MATLAB/Simulink^® (R2021b, MATLAB, Natick, MA, USA) was constructed and validated through hardware-in-the-loop (HIL) testing to ensure the authenticity and reliability of the simulation results. Detailed tests and analyses of the dual-motor system were conducted under FTP-75 driving cycles. Compared to the RBC strategy, GGS and WOA achieved energy efficiency improvements of 9.1% and 8.9%, respectively, in the pure simulation, and 4.2% and 3.8%, respectively, in the HIL simulation. Compared to the pure RBC strategy, the RBC and GGS strategies incorporating regenerative braking achieved energy efficiency improvements of 26.1% and 29.4%, respectively, in the HIL simulation. Overall, GGS and WOA each present distinct advantages, with WOA emerging as a highly promising alternative energy management strategy. Future research should further explore WOA applications to enhance energy savings in real-world vehicle operations. Full article

In an effort to restrict further increases in climate change, governments and companies are exploring ways to reduce greenhouse gas (GHG) emissions. In this context, the oil industry, which contributes to indirect GHG emissions, is seeking ways to develop solutions to this issue. One such approach focuses on the connection of offshore oil production platforms to the onshore power grid via high-voltage direct current (HVDC), enabling a total or partial reduction in the number of local generators, which are generally powered by gas turbines. Therefore, this work aims to determine the technical feasibility, based on transient and dynamic stability analyses, of electrifying a system composed of six floating production storage and offloading (FPSO) units connected to a hub, which is powered by the onshore grid through submarine cables using HVDC technology. The analysis includes significant contingencies that could lead the system to undesirable operating conditions, allowing for the identification of appropriate remedial control actions. The analysis, based on real data and parameters, was carried out using PSCAD software. The results show that the modeled system is technically viable and could be adopted by oil companies. In addition to aligning with global warming mitigation goals, the proposal includes a complex system modeling approach, with the aim of enabling further study. Full article

The inhibition of steroid 5-alpha reductase (S5AR), a key mechanism for managing dihydrotestosterone-dependent conditions, has been demonstrated in teak (Tectona grandis L.f.) leaf extracts. Our recent clinical study confirmed the effectiveness of a hair growth formulation containing teak leaf extract in males with androgenic alopecia. However, significant variability in S5AR inhibitory activity among teak leaf samples from different regions underscores the need for quality control of raw materials. This study applied a metabolomics approach to investigate the influence of leaf age, harvesting period, and geographic origin on chemical composition and S5AR inhibitory activity, as well as to identify active S5AR inhibitors. Geographic origin emerged as the primary determinant of variations in chemical profiles and S5AR inhibitory activity. Using orthogonal partial least squares analysis, six diterpenoid S5AR inhibitors were identified, including four compounds reported for the first time as S5AR inhibitors: rhinocerotinoic acid, 7-oxo-8-labden-15-oic acid, 8-hydroxy-labd-13-en-15-oic acid, and a novel diterpene, 7-hydroxy-labd-8,13-dien-15-oic acid. These findings highlight the potential of metabolomics as a powerful tool for discovering bioactive compounds and optimizing raw material selection. By prioritizing proven geographic sources, consistent bioactivity can be achieved, supporting the therapeutic potential of teak leaves in managing S5AR-related conditions. Full article

This article presents experimental results related to the influence of bioethanol content in fuel blends on the performance and emissions of a spark-ignition engine. Tests were conducted for six ethanol–gasoline mixtures (ranging from 0% to 100% ethanol) under three engine control strategies: factory settings, a fuel dose increased by 10%, and a fuel dose increased by 20%—both with an ignition timing adjustment of +3°. Measurements included engine power and torque, as well as emissions of CO, CO₂, HC, O₂, and particulate matter, all performed under a full engine load. The results revealed the strong dependence of engine behavior on ethanol content. Increasing the ethanol concentration significantly reduced CO and HC emissions, as well as markedly lowering particulate emissions—particularly at 30% ethanol. Conversely, pure ethanol led to substantial reductions in power (up to 28%) and torque (up to 32%) compared to conventional gasoline. Adjustments to the fuel dose and ignition timing partially mitigated these losses. Emissions of CO₂ and oxygen content in exhaust gases varied depending on the blend, highlighting the complex nature of the combustion process. The findings contribute to the understanding of renewable fuel behavior in SI engines and underscore the influence of both fuel composition and control strategies on performance and emission characteristics. Full article

This paper presents an adaptive Maximum Power Point Tracking (MPPT) strategy for grid-connected photovoltaic (PV) systems that uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Particle Swarm Optimization (PSO) to enhance energy extraction efficiency under diverse environmental conditions. The proposed ANFIS-PSO-based MPPT controller performs dynamic adjustment Pulse Width Modulation (PWM) switching to minimize Total Harmonic Distortion (THD); this will ensure rapid convergence to the maximum power point (MPP). Unlike conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, which struggle with tracking delays and local maxima in partial shading scenarios, the proposed approach efficiently identifies the Global Maximum Power Point (GMPP), improving energy harvesting capabilities. Simulation results in MATLAB/Simulink R2023a demonstrate that under stable irradiance conditions (1000 W/m², 25 °C), the controller was able to achieve an MPPT efficiency of 99.2%, with THD reduced to 2.1%, ensuring grid compliance with IEEE 519 standards. In dynamic irradiance conditions, where sunlight varies linearly between 200 W/m² and 1000 W/m², the controller maintains an MPPT efficiency of 98.7%, with a response time of less than 200 ms, outperforming traditional MPPT algorithms. In the partial shading case, the proposed method effectively avoids local power maxima and successfully tracks the Global Maximum Power Point (GMPP), resulting in a power output of 138 W. In contrast, conventional techniques such as P&O and INC typically fail to escape local maxima under similar conditions, leading to significantly lower power output, often falling well below the true GMPP. This performance disparity underscores the superior tracking capability of the proposed ANFIS-PSO approach in complex irradiance scenarios, where traditional algorithms exhibit substantial energy loss due to their limited global search behavior. The novelty of this work lies in the integration of ANFIS with PSO optimization, enabling an intelligent self-adaptive MPPT strategy that enhances both tracking speed and accuracy while maintaining low computational complexity. This hybrid approach ensures real-time adaptation to environmental fluctuations, making it an optimal solution for grid-connected PV systems requiring high power quality and stability. The proposed controller significantly improves energy harvesting efficiency, minimizes grid disturbances, and enhances overall system robustness, demonstrating its potential for next-generation smart PV systems. Full article

Background: A key challenge in modern pharmaceutical research is developing predictive models for drug formulation behavior. Since permeability is closely linked to molecular properties, considering a broad of characteristics is essential for building reliable predictive tools. Near-infrared spectroscopy (NIR), a non-destructive, non-invasive, and chemically specific method, offers a powerful alternative to current gold-standard methods approved by regulatory agencies. Objectives: This study aims to apply a partial analytical quality by design (AQbD) approach to enhance the understanding and development of NIR and RP-HPLC methodologies. Methods: The employment of NIR with multivariate data analysis enabled the establishment of chemometric models for the classification and quantification of bifonazole (BFZ) in cream formulations. Results: An analytical target profile (ATP) was defined to guide the selection of critical method variables and support method design and development activities. Risk assessment was carried out using an Ishikawa diagram. For the RP-HPLC method, key performance parameters such as peak area, theoretical plates, tailing factor, and assay were evaluated, while NIR spectra and BFZ concentration were considered for method performance. The quantification models enabled the accurate determination of BFZ content, yielding results of 8.48 mg via NIR and 8.34 mg via RP-HPLC, with an RSD of 1.25%. Conclusions: These findings demonstrate the robustness and reliability of the models, making them suitable for routine quality control of BFZ formulations. Future research should aim to explore its use for monitoring permeation dynamics in real time and integrating it into regulatory frameworks to standardize its application in pharmaceutical quality control and formulation development. Full article

The paper focuses on linearization of split DC link voltage dynamics and balancing their respective average values in three-phase three-level AC/DC converters. It was recently demonstrated that both AC-side current magnitude and operating power factor impact the dynamics of partial DC link voltage difference, imposing the time-varying behavior of split DC link voltages when a typical linear time-invariant compensator, e.g., proportional or proportional–integrative, is utilized. Consequently, robust split DC link voltage balancing loops would be beneficial. The case of a bandwidth-restricted (DC in a steady state) zero-sequence component employed as a control signal to equalize average partial DC link voltages is considered in this work. It is proposed to nominalize the dynamics of partial DC link voltage difference by means of a linear disturbance observer based on a frequency-selective filter so that the modified dynamics become linear and nearly nominal from a compensator point of view. As a result, the closed-loop response becomes time-invariant—a desirable characteristic of any practical system. Simulations validate the proposed methodology applied to a 10 kVA T-type converter model. Full article

The starch modification of mango cotyledons with both single ultrasound (US) and dual (US followed by octenyl succinic anhydride, OSA) was optimized by response surface methodology (RSM). The mechanochemical effects of ultrasound on amylose content, particle size, and dual modification efficiency were assessed. In addition, the structural, thermal, morphological, and functional properties were evaluated. After optimization with single US (41 min and 91% sonication intensity), sonication induced starch granule fragmentation, altering amorphous and partially crystalline regions, which increased amylose content (34%), reduced particle size (Dx50 = 12 μm), and modified granule surface morphology. The dual modification (the subsequent OSA reaction lasted 4.6 h under the same conditions) reached a degree of substitution of 0.02 and 81% efficiency, imparting amphiphilic properties to the starch. OSA groups were mainly incorporated into amorphous and surface regions, which decreased crystallinity, gelatinization temperature, and enthalpy. The synergistic effect of the modification with US and OSA in the dual modification significantly improved the solubility and swelling power of starch, resulting in better dispersion, functionality in aqueous systems, and chemical reactivity. These findings highlight the potential of dual modification to transform mango cotyledon starch into a versatile ingredient in the food industry as a thickener, a stabilizer in soups and sauces, an emulsifier, a carrier of bioactive and edible films; in the cosmetic industry as a gelling and absorbent agent; and in the pharmaceutical industry for the controlled release of drugs. Furthermore, valorizing mango cotyledons supports circular economy principles, promoting sustainable and value-added food product development. Full article

Background: Cardiac autonomic neuropathy (CAN) is a common yet frequently underdiagnosed complication of diabetes. While our previous study demonstrated the utility of multiscale cross-approximate entropy (MS-CXApEn) in detecting early CAN, the present study further investigates the use of frequency-domain coherence analysis between systolic blood pressure (SBP) and R-R intervals (RRI) and evaluates the effects of insulin treatment on autonomic function in diabetic rats. Methods: At the onset of diabetes induced by streptozotocin (STZ), rats were assessed for cardiovascular autonomic function both before and after insulin treatment. Spectral and coherence analyses were performed to evaluate baroreflex function and autonomic regulation. Parameters assessed included low-frequency power (LFP) and high-frequency power (HFP) of heart rate variability, coherence between SBP and RRI at low and high-frequency bands (LF_Coh and HF_Coh), spontaneous and phenylephrine-induced baroreflex sensitivity (BRS_spn and BRS_phe), HRV components derived from fast Fourier transform, and MS-CXApEn at multiple scales. Results: Compared to normal controls (LF_Coh: 0.14 ± 0.07, HF_Coh: 0.19 ± 0.06), early diabetic rats exhibited a significant reduction in both LF_Coh (0.08 ± 0.04, p < 0.05) and HF_Coh (0.16 ± 0.10, p > 0.05), indicating impaired autonomic modulation. Insulin treatment led to a recovery of LF_Coh (0.11 ± 0.04) and HF_Coh (0.24 ± 0.12), though differences remained statistically insignificant (p > 0.05 vs. normal). Additionally, low-frequency LFP increased at the onset of diabetes and decreased after insulin therapy in most rats significantly, while MS-CXApEn at all scale levels increased in the early diabetic rats, and MS-CXApEn_large declined following hyperglycemia correction. The BRS_spn and BRS_phe showed no consistent trend. Conclusions: Coherence analysis provides valuable insights into autonomic dysfunction in early diabetes. The significant reduction in LF_Coh in early diabetes supports its role as a potential marker for CAN. Although insulin treatment partially improved coherence, the lack of full recovery suggests persistent autonomic impairment despite glycemic correction. These findings underscore the importance of early detection and long-term management strategies for diabetic CAN. Full article

Recently, artificial intelligence (AI) has become a promising solution to the optimization of the energy harvesting and performance of photovoltaic (PV) systems. Traditional maximum power point tracking (MPPT) algorithms have several drawbacks on tracking the global maximum power point (GMPP) under partial shading conditions (PSCs). To track the GMPP, AI enabled methods stand out over other traditional solutions in terms of faster tracking dynamics, lesser oscillation, higher efficiency. However, such AI-based MPPT methods differ significantly in various applications, and thus, a full picture of AI-based MPPT methods is of interest to further optimize the PV energy harvesting. In this paper, various AI-based global maximum power point tracking (GMPPT) techniques are then implemented and critically compared by highlighting the advantages and disadvantages of each technique under dynamic weather conditions. The comparison demonstrates that the hybrid AI techniques are more reliable, which offer higher efficiency and better dynamics to handle PSCs. According to the benchmarking, a modified particle swarm optimization (PSO) GMPPT algorithm is proposed, and the experimental results validate its ability to achieve GMPPT with faster dynamics and higher efficiency. This paper is intended to motivate engineers and researchers by offering valuable insights for the selection and implementation of GMPPT techniques and to explore the AI techniques to enhance the efficiency and reliability of PV systems by providing fresh perspectives on optimal AI-based GMPPT techniques. Full article

Nonlinear characteristics of solar photovoltaic (PV) and nonuniform surrounding conditions, including partial shading conditions (PSCs), are the major factors responsible for lower conversion efficiency in solar panels. One major condition is the cause of the multiple peaks and oscillation around the peak point leading to power losses. Therefore, this study proposes a novel hybrid framework based on an artificial neural network (ANN) and fractional order PID (FOPID) controller, where new algorithms are employed to train the ANN model and to tune the FOPID controller. The primary aim is to maintain the computed power close to its true peak power while mitigating persistent oscillations in the face of continuously varying surrounding conditions. Firstly, a modified shuffled frog leap algorithm (MSFLA) was employed to train the feed-forward ANN model using real-world solar PV data with the aim of generating a reference solar PV peak voltage. Subsequently, the parameters of the FOPID controller were tuned through the application of the Sanitized Teacher–Learning-Based Optimization (s-TLBO) algorithm, with a specific focus on achieving maximum power point tracking (MPPT). The robustness of the proposed hybrid framework was assessed using two different types (monocrystalline and polycrystalline) of solar panels exposed to varying levels of irradiance. Additionally, the framework’s performance was rigorously tested under cloudy conditions and in the presence of various partial shading scenarios. Furthermore, the adaptability of the proposed framework to different solar panel array configurations was evaluated. This work’s findings reveal that the proposed hybrid framework consistently achieves maximum power point with minimal oscillation, surpassing the performance of recently published works across various critical performance metrics, including the $MP{P}_{efficiency}$, relative error (RE), mean squared error (MSE), and tracking speed. Full article

In response to fire safety risks associated with photovoltaic (PV) systems and evolving rapid shutdown requirements, this paper proposes a cost-effective and reliable rapid shutdown solution integrated directly into the PV module junction box. The system employs analog circuitry triggered by an external pulse-width modulation (PWM) signal, with optocoupler isolation and a controlled short-circuit method to rapidly reduce the module output voltage. Simulation and experimental results confirm that the output voltage is reduced to approximately 2 V within 280 ms, satisfying the U.S. National Electrical Code (NEC) 690.12 requirements. This junction-box–integrated approach eliminates the complexity of conventional module-level power electronics (MLPE) systems and offers a highly practical alternative for building-integrated photovoltaic (BIPV) applications where partial shading is minimal. Full article