Browse Articles
Discover research articles across all indexed journals
Enhanced hybrid microgrid stability with electric vehicle integration using PDA-FOPIID control optimized by tianji’s horse racing algorithm
Abstract Ensuring stable voltage and frequency regulation in hybrid microgrids has become a pressing challenge due to the increasing penetration of renewable energy sources and the integration of electric vehicles. The strong coupling between active and reactive power dynamics often leads to instability, degraded dynamic performance, and difficulties in maintaining tie-line power balance. To address these challenges, this study proposes a novel cascaded controller that combines Proportional-Derivative-Acceleration (PDA) and Fractional-Order Proportional-Integral-Double Integral-Derivative (FOPIID) control strategies, optimized using Tianji’s Horse Racing Optimization (THRO) algorithm. The proposed PDA-FOPIID controller functions as a robust secondary control strategy that effectively decouples voltage and frequency regulation while improving transient response. Optimization through THRO ensures superior performance compared to other well-known metaheuristic algorithms, achieving the lowest Integral Time-Square Error (ITSE) value of 0.0297. Extensive MATLAB/Simulink simulations under diverse operating conditions including step load changes, random fluctuations, multi-step perturbations, and high renewable penetration demonstrate the controller’s effectiveness. Comparative results against several benchmark controllers, namely Proportional-Integral-Derivative (PID), Tilt-Integral-Derivative (TID), Fractional-Order PID (FOPID), PD-(1 + PI), and FOPI-PIDD², confirm that the proposed PDA-FOPIID achieves approximately 48% faster settling time, improved damping, and superior voltage and frequency stability. These findings highlight its potential as a promising solution for enhancing the reliability and resilience of modern hybrid microgrids with high renewable and electric vehicle integration.
Metagenome analysis of Citrus sinensis rhizosphere infected with Candidatus liberibacter asiaticus reveals distinct structure in bacterial communities
Visual prediction method based on time series-driven LSTM model
Hybrid cryptographic approach for strengthening IoT and 5G/B5G network security
Abstract The rapid evolution of fifth-generation (5G) and beyond (B5G) networks has introduced significant security challenges, necessitating advanced cryptographic mechanisms to protect sensitive data during transmission. Traditional encryption models often struggle to balance security, computational efficiency, and adaptability to dynamic network conditions. This study proposes a novel hybrid cryptographic framework integrating the Advanced Encryption Standard (AES), Data Encryption Standard (DES), and Rivest–Shamir–Adleman (RSA) algorithms. AES and DES provide high-speed symmetric encryption for efficient data protection, while RSA enables secure key exchange and authentication. The integration of dynamic round keys enhances encryption complexity, improving resistance to cryptanalytic attacks. Performance evaluations, including encryption and decryption time analysis, data expansion metrics, and throughput assessments, demonstrate that the proposed framework achieves an optimal balance between security and computational overhead. Benchmark comparisons with traditional and post-quantum cryptographic models highlight the superior efficiency and reduced data expansion of the hybrid approach. Furthermore, practical implementation on ESP32 hardware confirms the model’s feasibility for real-time encryption in resource-constrained environments typical of 5G applications. This scalable and flexible encryption paradigm addresses current and emerging security requirements in high-speed wireless networks, with future work focusing on integration with quantum-resistant cryptographic mechanisms to enhance resilience against evolving cyber threats. Experimental results show that the hybrid model achieves up to 30% higher throughput, 10–15% lower data expansion, and reduced encryption/decryption time compared to baseline algorithms, with successful ESP32 implementation and 100% decryption accuracy for key sizes up to 128 bits.
Quantum phase transitions in the spin-1 bilinear-biquadratic Heisenberg model based on classical and quantum correlations
Machine learning integration of multi-modal radiomics and clinical factors predicts refracture risk after percutaneous kyphoplasty in postmenopausal women
Egyptian basalt powder as a fortifier for improved performance and sustainability of alkali-activated slag cement
Anatomy of climate change research in Italian doctoral dissertations using a machine learning approach
Multi-ship detection and classification with feature enhancement and lightweight fusion
Safety trial assessing 1.77 cm2 H2O2 producing electrochemical bandages on healthy human skin
Surface modifications of graphitic carbon nitride with metal particles and polymer blend as antibacterial and dye removal agents
Tracking sources and transformations of dissolved sulfate in karstic sub-basins in Southwest China using dual isotopes and water chemistry
Differential effects of human density, environmental health, and group size on urban coyote detection, boldness, and exploration
Abstract Comparative studies show that urban coyotes behave differently from rural counterparts. However, these studies often homogenize cities. Cities feature diverse pressures for wildlife, such as variation in human densities and environmental health, two factors known to increase risk-taking. This heterogeneity creates a landscape of risk, which may drive locally adapted behavioral strategies within cities. Yet, the influence of these pressures on coyote behavior remains unclear. To investigate this, we conducted novel object testing at 24 sites across gradients of human density and pollution. We recorded coyote detections, group size, and behavioral responses to the novel object, focusing on time spent alert, time spent close, and total exploration. We found that coyote detections varied with human density and pollution, with markedly lower detections in areas with high human density and pollution. Coyote boldness (time spent alert and close) and exploration were uniformly associated with human density, with coyotes in high human density areas displaying elevated boldness and heightened exploration. We also found that time spent close and exploration increase with group size. In contrast, coyote risk-taking did not vary with pollution. Our results suggest that heterogeneity in human density, environmental health, and social context differentially affects coyote ecology, which may have consequences for human-carnivore coexistence.
Evaluating large transformer models for anomaly detection of resource-constrained IoT devices for intrusion detection system
Effect of nano-curcumin supplementation on liver fibrosis in patients with NAFLD-associated fibrosis: a double-blind randomized controlled trial
A numerical integrated flow-stress processing model for plain weave textile composites
Abstract This work proposes a numerical integrated flow-stress processing model in order to simulate the flow front of the resin and manufacturing-induced deformation during the fabrication cycle. In the flow model, each layer of the dry fiber fabric is modeled as an orthotropic porous material. The resin flow behavior is predicted using the volume of fluid method, incorporating the temperature- and cure-dependent viscosity of the resin. After the infusion model, the resin filling factor and the degree of cure are transferred to a multi-physics curing model to predict the residual stress. In this model, each lamina is modeled as a homogeneous orthotropic material with mechanical properties calculated from the in-situ resin modulus and the elastic modulus of the fiber based on the micromechanics. The composite properties, along with the thermal strains and chemical shrinkage, are incorporated into an orthotropic constitutive law to predict the residual stress. The accuracy of the proposed model is validated through comparison between the spring-in angle predicted by the numerical model and the experimental results.
Microwave therapy promotes wound healing in diabetic mice by activating IL-33/ST2-mediated M2 macrophage polarization
Enhancing enviromics based predictions in common bean multi-environment trials
Chemically treated groundnut shell fibers for enhanced mechanical and blast performance of concrete
Abstract Groundnut shell fiber, an agricultural byproduct rich in cellulose and lignin, offers potential as a sustainable reinforcement in high-performance concrete. This study investigates the effect of chemically treated groundnut shell fibers on the mechanical and blast performance of fiber-reinforced concrete (FRC). Fibers were subjected to alkali, silane, and acetylation treatments, followed by characterization through tensile testing, FTIR, and XRD analyses to evaluate structural and chemical modifications. The results demonstrated that NaOH treatment enhanced tensile strength and modulus of elasticity due to improved crystallinity and reduced fiber diameter. Concrete specimens incorporating fibers at varying dosages (0.5%, 1.0%, and 1.5% by weight of cement) were prepared and tested for compressive, split tensile, and flexural strengths. Findings revealed that untreated and pretreated fibers generally reduced strength, while post-treated fibers improved performance at optimized levels. The best outcomes were achieved at 0.5% post-treated fiber, showing approximately 10–11% improvement in compressive and tensile strengths compared to the control mix, with flexural strength optimized at 1.0%. Beyond these levels, fiber addition reduced performance due to poor dispersion and higher water absorption. Finite element blast simulations further indicated that treated fiber-reinforced panels exhibited improved stress distribution and reduced localized deformation compared to conventional concrete. Overall, the study highlights that chemically treated groundnut shell fibers can enhance the toughness and energy absorption capacity of concrete when used at optimum proportions. However, the blast resistance results are based on numerical modeling, and further large-scale experimental validation is required before structural application in high-impact environments.