Search Results (371)

This study aims to develop high-performance biocomposites for structural applications using kenaf, bagasse, hemp, and softwood fibers bonded with phenol-formaldehyde (PF) and phenol-urea-formaldehyde (PUF) adhesives, commonly used in particleboard manufacturing. A simple, low-cost fiber treatment was applied by adjusting the fiber pH to 11 and 13 using a 33% NaOH solution, following standard protocols to enhance fiber–adhesive interaction. The effects of alkaline treatment on the chemical structure of bagasse, kenaf, and hemp fibers were investigated using Fourier Transform Infrared Spectroscopy (FTIR) and correlated with composite mechanical performance. PF and PUF were applied at 13% (w/w), while polymeric diphenylmethane diisocyanate (pMDI) at 5% (w/w) served as a control for untreated fibers. The fabricated panels were evaluated for mechanical properties; modulus of elasticity (MOE), modulus of rupture (MOR), and internal bond strength (IB), and physical properties such as thickness swelling (TS) and water absorption (WA) after 24 h of immersion. FTIR analysis revealed that treatment at pH 11 increased the intensity of O–H, C–O–C, and C–O bands and led to the disappearance of the C=O band (~1700 cm⁻¹) in all fibers. Bagasse treated at pH 11 showed the most significant spectral changes and the highest IB values with both PF and PUF adhesives, followed by kenaf at pH 13, exceeding EN 312:6 (2010) standards for heavy-duty load-bearing panels in dry conditions. The highest MOE and MOR values were achieved with kenaf at pH 11, meeting EN 312:4 (2010) requirements, followed by bagasse, while softwood and hemp performed less favorably. In terms of thickness swelling, bagasse consistently outperformed all other fibers across pH levels and adhesives, followed by Kenaf and Hemp, surpassing even pMDI-based composites. These results suggest that high-pH treatment enhances the reactivity of PF and PUF adhesives by increasing the nucleophilic character of phenolic rings during polymerization. The performance differences among fibers are also attributed to variations in the aspect ratio and intrinsic structural properties influencing fiber–adhesive interactions under alkaline conditions. Overall, kenaf and bagasse fibers emerge as promising, sustainable alternatives to industrial softwood particles for structural particleboard production. PF and PUF adhesives offer cost-effective and less toxic options compared to pMDI, supporting their use in eco-friendly panel manufacturing. FTIR spectroscopy proved to be a powerful method for identifying structural changes caused by alkaline treatment and provided valuable insights into the resulting mechanical and physical performance of the biocomposites. Full article

The integration of photovoltaic (PV) generation into distribution systems supports decarbonization and cost reduction but introduces challenges for secure and efficient operation due to voltage fluctuations and power flow variability. Traditional centralized optimal power flow (OPF) methods require full system observability and significant computational resources, limiting their real-time applicability in active distribution systems. This paper proposes a deep neural network (DNN)-based OPF control framework designed for active distribution systems with high PV penetration under limited measurement availability. The proposed method leverages offline convex chance-constrained OPF (convex-CCOPF) solutions, generated through iterative simulations across a wide range of PV and load conditions, to train the DNN to approximate optimal control actions, including on-load tap changer (OLTC) positions and inverter reactive power dispatch. To address observability constraints, the DNN is trained using a reduced set of strategically selected measurement points, making it suitable for real-world deployment in distribution systems with sparse sensing infrastructure. The effectiveness of the proposed framework is validated on the IEEE 33-bus test system under varying operating conditions. The simulation results demonstrate that the DNN achieves near-optimal performance with a significantly reduced computation time compared to conventional OPF solvers while maintaining voltage profiles within permissible limits and minimizing power losses. Full article

This paper proposes a real-time energy management strategy for low-voltage microgrids that combines short-horizon forecasting with a rule-based supervisory controller to coordinate battery energy storage usage and reactive power support provided by flexible alternating current transmission technologies. The central contribution is the forecast-informed, joint orchestration of active charging and reactive power dispatch to regulate voltage and preserve stability under large photovoltaic variability and uncertain electric vehicle demand. The work also introduces a resilience response index that quantifies performance under external disturbances, forecasting delays, and increasing levels of electric-vehicle integration. Validation is carried out through time-domain numerical simulations in MATLAB/Simulink using realistic solar irradiance and electric vehicle charging profiles. The results show that the coordinated strategy reduces voltage deviation events, maintains stable operation across a wide range of scenarios, and enables electric vehicle charging to be supplied predominantly by renewable generation. Sensitivity analysis further indicates that support from flexible alternating current devices becomes particularly decisive at high charging demand and in the presence of forecasting latency, underscoring the practical value of the proposed approach for distribution-level microgrids. Full article

In modern power systems, FACTS tools are essential for addressing voltage variation along with fault ride-through (FRT) challenges within the electrical power systems, particularly for wind generation integration. Several prominent publications emphasize that the squirrel cage induction generator (SCIG) currently comprises about 15% of operational wind turbines. This research proposes the use of FACTS devices to boost voltage stability and FRT capability. The implementation of these devices leads to improved efficiency in the electrical power system. This study considers many events, including an ideal wind profile, turbulent wind profile, symmetrical faults, and unsymmetrical faults, to support the proposed selection. Furthermore, the proposed approach is evaluated by comparison between a fixed capacitor, static synchronous compensator (STATCOM), and Static VAR Compensator (SVC) to guarantee the achievement of voltage stability, reactive power consumption, and FRT capacity under various wind speed profiles and fault conditions. An overall evaluation is conducted to compare them in all examined circumstances and to highlight their advantages and effects. The simulation findings demonstrate the efficacy and primacy of FACTS in enhancing the functioning of an integrated wind system, which is built upon a grid-connected SCIG, as well as enhancing the power system performance. The MATLAB/Simulink toolbox is used to design the models of SCIG, SVC, and STATCOM. Full article

This paper proposes a design and control methodology for a Quasi-Y-Source impedance source inverter (QS-YSI) as a power electronics interface for Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) applications in the context of virtual power plants (VPPs). The work presents an analysis of bidirectional power transfer using Electric Vehicles (EVs) to supply power to the utility grid, businesses, and homes, thereby acting as distributed energy resources. The proposed QS-YSI topology supports both V2G and G2V operation while providing reactive power compensation and enabling the decoupled tracking of active power (P) and reactive power (Q), demonstrating the capability of EVs to return energy to the grid and to provide ancillary services such as power factor correction. The key contributions are a detailed control design methodology that includes pulsating DC-link voltage regulation, inverter output current reference tracking in the synchronous $dq$ reference frame considering DC-link voltage dynamics, and a modified Pulse Width Modulation (PWM) technique for effective decoupling of DC link and inverter output current control. Finally, the feasibility and validity of the proposed approach are demonstrated through simulations of the complete system under nominal conditions and experiments conducted considering a small-scale prototype. Full article

Graphene/h-BN/Si heterostructures show considerable potential for future use in infrared detection and photovoltaic technologies due to their adjustable electrical behavior and well-matched interfacial structure. The near-lattice match between graphene and hexagonal boron nitride (h-BN) enables the deposition of low-defect-density graphene on h-BN surfaces. This study presents a thorough exploration of the low-frequency electrical noise behavior of graphene/h-BN/Si heterojunctions under both forward and reverse bias conditions at room temperature. Graphene nanolayers were directly grown on h-BN films using microwave plasma-enhanced CVD. The h-BN layers were formed by reactive high-power impulse magnetron sputtering (HIPIMS). Four h-BN thicknesses were examined: 1 nm, 3 nm, 5 nm, and 15 nm. A reference graphene/Si junction (without h-BN) prepared under identical synthesis conditions was also studied for comparison. Low-frequency noise analysis enabled the identification of dominant charge transport mechanisms in the different device structures. Our results demonstrate that grain boundaries act as dominant defects contributing to increased noise intensity under high forward bias. Statistical analysis of voltage noise spectral density across multiple samples, supported by Raman spectroscopy, reveals that hydrogen-related defects significantly contribute to 1/f noise in the linear region of the junction’s current–voltage characteristics. This study provides the first in-depth insight into the impact of h-BN interlayers on low-frequency noise in graphene/Si heterojunctions. Full article

Reactive oxygen species (ROS) act as double-edged swords in cancer biology—facilitating tumor growth, survival, and metastasis at moderate levels while inducing oxidative damage and cell death when exceeding cellular buffering capacity. To survive under chronic oxidative stress, cancer cells rely on robust antioxidant systems such as the glutathione (GSH) and thioredoxin (Trx), and superoxide dismutases (SODs). These systems maintain redox homeostasis and sustain ROS-sensitive signaling pathways including MAPK/ERK, PI3K/Akt/mTOR, NF-κB, STAT3, and HIF-1α. Targeting the antioxidant defense mechanisms of cancer cells has emerged as a promising therapeutic strategy. Inhibiting the glutathione system induces ferroptosis, a non-apoptotic form of cell death driven by lipid peroxidation, with compounds like withaferin A and altretamine showing strong preclinical activity. Disruption of the Trx system by agents such as PX-12 and dimethyl fumarate (DMF) impairs redox-sensitive survival signaling. Trx reductase inhibition by auranofin or mitomycin C further destabilizes redox balance, promoting mitochondrial dysfunction and apoptosis. SOD1 inhibitors, including ATN-224 and disulfiram, selectively enhance oxidative stress in tumor cells and are currently being tested in clinical trials. Mounting preclinical and clinical evidence supports redox modulation as a cancer-selective vulnerability. Pharmacologically tipping the redox balance beyond the threshold of cellular tolerance offers a rational and potentially powerful approach to eliminate malignant cells while sparing healthy tissue, highlighting novel strategies for targeted cancer therapy at the interface of redox biology and oncology. Full article

Perfluoromethyl vinyl ether (PMVE) has recently emerged as a promising environmentally friendly insulating gas with potential for practical applications in the power industry. When mixed with CO₂, the PMVE/CO₂ mixture exhibits an elevated liquefaction temperature and enhanced insulation performance, making it suitable for engineering use. In this study, density functional theory (DFT) calculations were employed to investigate the reactive sites of PMVE molecules. The results indicate that the C₂–O and C₃–O bonds are the most susceptible to breakage, highlighting their high reactivity. The optimal insulation performance of the PMVE/CO₂ mixture is achieved at a CO₂ concentration of approximately 60%, with significant molecular decomposition observed at temperatures exceeding 2600 K. The primary decomposition products include C₂F₂, COF₃, COF₂, F, C₂F₃, CO, CF₃, and C₂F₄. Both high temperature and elevated CO₂ content accelerate the decomposition process. These findings provide valuable insights into the insulation properties and thermal stability of the PMVE/CO₂ system, offering theoretical support for its potential application in eco-friendly high-voltage insulation technologies. Full article

The utilization of ammonia as a fuel for gas turbines involves practical challenges due to its low reactivity, narrow flammability limits, and slow laminar flame propagation. One of the potential solutions to enhance the combustion reactivity of ammonia is co-firing with syngas. This paper presents an experimental and numerical investigation of the laminar burning velocity (LBV) of ammonia/syngas/air mixtures under elevated pressures (up to 10 bar) and temperatures (up to 473 K). Experiments were conducted in a constant-volume combustion chamber with a total volume of 11 L equipped with a dual-electrode capacitive discharge ignition system. A systematic sensitivity analysis was conducted to experimentally evaluate the system performance under various syngas compositions and equivalence ratios from 0.7 to 1.6 and ultimately identify the factors with the most impact on the system. As a complement to the experiments, a detailed numerical simulation was carried out integrating available kinetic mechanisms—chemical reaction sets and their rates—to support advancements in the understanding and optimization of ammonia/syngas co-firing dynamics. The sensitivity analysis results reveal that LBV is significantly enhanced by increasing the hydrogen content (>50%). Furthermore, the LBV of the gas mixture is found to increase with the use of a rich flame, higher mole fractions of syngas, and higher initial temperatures. The results indicate that higher pressure reduces LBV by 40% but at the same time enhances the adiabatic flame temperature (by 100 K) due to an equilibrium shift. The analysis was also extended to quantify the impact of syngas mole fractions and elevated initial temperatures. The kinetics of the reactions are analyzed through the reaction pathways, and the results reveal how the preferred pathways vary under lean and rich flame conditions. These findings provide valid quantitative design data for optimizing the combustion kinetics of ammonia/syngas blends, offering valuable design data for ammonia-based combustion systems in industrial gas turbines and power generation applications, reducing NOₓ emissions by up to 30%, and guiding future research directions toward kinetic models and emission control strategies. Full article

This study investigates the influence of vacancy engineering and nitrogen doping on the structural, electronic, and optical properties of T-graphene fragments (TFs) using density functional theory (DFT) and time-dependent DFT (TD-DFT). A central vacancy and five pyridinic nitrogen doping configurations are explored to modulate the optoelectronic behavior. All systems are thermodynamically stable, exhibiting tunable HOMO–LUMO gaps, orbital distributions, and charge transfer characteristics. Optical absorption spectra show redshifts and enhanced oscillator strengths in doped variants, notably v-NTF2 and v-NTF4. Nonlinear optical (NLO) analysis reveals significant enhancement in both static and frequency-dependent responses. v-NTF2 displays an exceptionally high first-order hyperpolarizability (⟨β⟩ = 1228.05 au), along with a strong electro-optic Pockels effect (β (−ω; ω, 0)) and second harmonic generation (β (−2ω; ω, ω)). Its third-order response, γ (−2ω; ω, ω, 0), also exceeds 1.2 × 10⁵ au under visible excitation. Conceptual DFT descriptors and energy decomposition analysis further supports the observed trends in reactivity, charge delocalization, and stability. These findings demonstrate that strategic nitrogen doping in vacancy-engineered TFs is a powerful route to tailor electronic excitation, optical absorption, and nonlinear susceptibility. The results offer valuable insight into the rational design of next-generation carbon-based materials for optoelectronic, photonic, and NLO device applications. Full article

Background: This study aimed to examine the effects of an 8-week plyometric training program on lower-limb explosive strength, jump performance, musculotendinous stiffness, reactive strength index (RSI), and multidirectional speed in elite Polish badminton players. Methods: Twenty-four athletes were randomly assigned to either an experimental group (n = 15), which supplemented their regular badminton training with plyometric exercises, or a control group (n = 15), which continued standard technical training. Performance assessments included squat jump (SJ), countermovement jump (CMJ), single-leg jumps, sprint tests (5 m, 10 m), lateral movements, musculotendinous stiffness, and RSI measurements. Results: The experimental group showed statistically significant improvements in jump height, power output, stiffness, and 10 m sprint and lateral slide-step performance (p < 0.05), with large effect sizes. No significant changes were observed in the control group. Single-leg jump improvements suggested potential benefits for addressing lower-limb asymmetries. Conclusions: An 8-week plyometric intervention significantly enhanced lower-limb explosive performance and multidirectional movement capabilities in young badminton players. These findings support the integration of targeted plyometric training into regular training programs to optimize physical performance, improve movement efficiency, and potentially reduce injury risk in high-intensity racket sports. Full article

Achieving sustainable energy goals requires efficient integration of renewables like wind energy. Doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) operate efficiently across a range of speeds, making them well-suited for modern renewable energy systems. However, sudden wind speed variations can cause power oscillations, rotor speed fluctuations, and voltage instability. Traditional proportional–integral (PI) controllers struggle with such nonlinear, rapidly changing scenarios. A control approach utilizing support vector regression (SVR) is proposed for the DFIG wind turbine system. The SVR controller manages both active and reactive power by simultaneously controlling the rotor- and grid-side converters (RSC and GSC). Simulations under a sudden wind speed variation from 10 to 12 m per second show the SVR approach reduces settling time significantly (up to 70.3%), suppresses oscillations in rotor speed, torque, and power output, and maintains over 97% DC-link voltage stability. These improvements enhance power quality, reliability, and system performance, demonstrating the SVR controller’s superiority over conventional PI methods for variable-speed wind energy systems. Full article

As the penetration rate of new energy continues to increase, the active voltage support capability of the power system is decreasing. The grid-forming static var generator (GFM-SVG) features the advantages of fast dynamic response, strong reactive power support, and high overload capacity, which play an important role in maintaining voltage stability. However, the parameters of the GFM-SVG are often unknown due to trade secret reasons. Meanwhile, the parameters may be changed during the long-term operation of the system, which brings challenges to the system stability analysis and control. Aiming at this problem, a parameter identification method based on trajectory sensitivity analysis and the proximal policy optimization (PPO) algorithm is proposed in this paper. Firstly, through trajectory sensitivity analysis, the key influential parameters on the output characteristics of the GFM-SVG can be selected, which can reduce the dimensionality of the identification parameters and improve the identification efficiency. Then, a parameter identification framework based on the PPO algorithm is constructed for GFM-SVGs, which utilizes its adaptive learning capability to achieve accurate identification of the key parameters of the system. Finally, the effectiveness of the proposed parameter identification method is verified through simulation examples. The simulation results show that the identification error of the parameters in the GFM-SVG is small. The proposed method can characterize the output response of the GFM-SVG under different operating conditions. Full article

Cervical cancer represents a significant global health challenge. Photodynamic therapy (PDT) appears to be a promising, minimally invasive alternative to standard treatments. However, the clinical efficacy of PDT is sometimes limited by the low solubility and aggregation of photosensitizers, their non-selective distribution in the body, hypoxia in the tumor microenvironment, and limited light penetration. Recent advances in nanoparticle and nanocomposite platforms have addressed these challenges by integrating multiple functional components into a single delivery system. By encapsulating or conjugating photosensitizers in biodegradable matrices, such as mesoporous silica, organometallic structures and core–shell construct nanocarriers increase stability in water and extend circulation time, enabling both passive and active targeting through ligand decoration. Up-conversion and dual-wavelength responsive cores facilitate deep light conversion in tissues, while simultaneous delivery of hypoxia-modulating agents alleviates oxygen deprivation to sustain reactive oxygen species generation. Controllable “motor-cargo” constructs and surface modifications improve intratumoral diffusion, while aggregation-induced emission dyes and plasmonic elements support real-time imaging and quantitative monitoring of therapeutic response. Together, these multifunctional nanosystems have demonstrated potent cytotoxicity in vitro and significant tumor suppression in vivo in mouse models of cervical cancer. Combining targeted delivery, controlled release, hypoxia mitigation, and image guidance, engineered nanoparticles provide a versatile and powerful platform to overcome the current limitations of PDT and pave the way toward more effective, patient-specific treatments for cervical malignancies. Our review of the literature summarizes studies on nanoparticles and nanocomposites used in PDT monotherapy for cervical cancer, published between 2023 and July 2025. Full article

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type. Full article