Search Results (1,747)

Predictive modeling of extrusion processes through machine learning (ML) offers significant improvements over classical response surface methodology (RSM) when addressing nonlinear and multivariable systems. This study evaluated hemp residues (Cannabis sativa) as a non-conventional ingredient in ruminant diets and compared the performance of polynomial regression models against several ML algorithms, including artificial neural networks (ANNs), random forest (RF), K-Nearest neighbors (KNN), and XGBoost. Three experimental datasets from previous extrusion studies were concatenated with new laboratory experiments, creating a unified database in excel. Input variables included extrusion parameters (temperature, screw speed, and moisture) and formulation components, while output variables comprised expansion index, BD, penetration force, water absorption index and water solubility index. Data preprocessing involved robust z-score detection of outliers (MAD criterion) with intra-group winsorization, followed by normalization to a [−1, +1] range. Hyperparameter optimization of ANN models was performed with Optuna, and all algorithms were evaluated through 5-fold cross-validation and independent external validation sets. Results demonstrated that ML models consistently outperformed quadratic regression, with ANNs achieving R² > 0.80 for BD and water solubility index, and RF excelling in predicting solubility. These findings establish machine learning as a robust predictive framework for extrusion processes and highlight hemp residues as a sustainable feed ingredient with potential to improve ruminant nutrition and reduce environmental impacts. Full article

Finding Noether symmetries for time-dependent damped dynamical systems remains a significant challenge. This paper introduces a complete geometric algorithm for determining all Noether point symmetries and first integrals for the general class of Lagrangians $L=A\left(t\right)L_0$, which model motion with general linear damping in a Riemannian space. We derive and prove a central Theorem that systematically links these symmetries to the homothetic algebra of the kinetic metric defined by $L_0$. The power of this method is demonstrated through a comprehensive analysis of the damped Kepler problem. Beyond recovering known results for constant damping, we discover new quadratic first integrals for time-dependent damping $\varphi \left(t\right)=\gamma /t$ with $\gamma =-1$ and $\gamma =-1/3$. We also include preliminary results on the Noether symmetries of the damped harmonic oscillator. Finally, we clarify why a time reparameterization that removes damping yields a physically inequivalent system with different Noether symmetries. This work provides a unified geometric framework for analyzing dissipative systems and reveals new integrable cases. Full article

Computer-aided modeling and mathematical optimization of energy systems are essential for improving operational efficiency and achieving emission reductions, particularly for steam turbine systems with part-load-dependent efficiency characteristics. Mixed-Integer Linear Programming (MILP) is the state of the art, due to its short computational times and reliable convergence. However, its simplifications often reduce model accuracy. Mixed-Integer Nonlinear Programming (MINLP) offers high accuracy but faces long computational times and potential convergence issues. Recent advancements in Mixed-Integer Quadratically Constrained Programming (MIQCP) offer a promising approach for more accurate energy system modeling by enabling quadratic and bilinear representations while avoiding the full complexity of nonlinear programs. This study compares the optimization methods MILP, MINLP and MIQCP for the operational optimization of a steam turbine system. The parameterization of the models is based on hourly measurement data of two real-world steam turbines. Key evaluation criteria include accuracy, computational time, implementation complexity and the deviation in the calculated optimum. The results show that MIQCP improves accuracy compared with MILP while requiring lower computational time than MINLP. Overall, the results demonstrate that MIQCP provides a suitable compromise between model accuracy and computational efficiency for the operational optimization of steam turbine systems. Full article

The combined implementation of land consolidation and smart agriculture is crucial for food security and agricultural carbon neutrality. Using 2010–2024 panel data from 279 Chinese prefecture-level cities, this study constructs an integrated assessment system and examines impact mechanisms and spatial effects using dual machine learning, mediation analysis, and dynamic spatial models. Results show that the interaction between land consolidation and smart agriculture significantly enhances food security at the 10% significance level and promotes agricultural carbon neutrality. Mechanism analysis indicates that agricultural industrial agglomeration positively contributes to both outcomes, while technological innovation significantly promotes carbon neutrality but temporarily suppresses food security. Spatial analysis reveals limited direct effects on local food security but positive indirect and total effects on neighboring regions, with carbon neutrality showing positive direct, indirect, and total effects. After controlling for city fixed effects and quadratic terms, the synergy remains significant, indicating robustness. The study suggests strengthening coordinated governance and innovation-driven regional development to jointly advance food security and agricultural carbon neutrality. Full article

This study investigates the dynamic behaviors of re-entrant unit-cell-shaped steel frames through numerical and experimental modal analyses. Inspired by re-entrant honeycomb structures, individual frame units were modeled to explore how natural frequencies vary with beam cross-sectional dimensions and frame angles. Twenty distinct frame models—incorporating four cross-sectional sizes (4 × 4 mm, 8 × 8 mm, 12 × 12 mm, and 16 × 16 mm) and five main frame angles (120°, 150°, 180°, 210°, and 240°)—were developed using 3D modeling and finite element analysis (FEA) tools, and the first eight natural frequencies and corresponding mode shapes were extracted for each model. The results reveal that lower modes exhibit global bending and torsional behaviors, whereas higher modes demonstrate increasingly localized deformations. It is found that the natural frequencies decrease in the straight frame configuration and increase in the hexagonal configurations, highlighting the critical influence of the frame geometry. Increasing the cross-sectional size consistently enhances the dynamic stiffness, particularly in hexagonal frames. A quadratic polynomial surface regression analysis was performed to model the relationship of the natural frequency with the cross-sectional dimension and frame angle, achieving high predictive accuracy (R² > 0.98). The experimental validation results were in good agreement with the numerical results, with discrepancies generally remaining below 7%. The developed regression model provides an efficient design tool for predicting vibrational behaviors and optimizing frame configurations without extensive simulations; furthermore, experimental modal analyses validated the numerical results, confirming the effectiveness of the model. Overall, this study provides a comprehensive understanding of the dynamic characteristics of re-entrant frame structures and proposes practical design strategies for improving vibrational performance, which is particularly relevant in applications such as machine foundations, vibration isolation systems, and aerospace structures. Full article

Developing an advanced simulation to control a swarm of 20 PocketQube nanosatellites using a linear quadratic regulator (LQR) involves several crucial steps that go beyond the initial scheme. A comprehensive approach requires a deep understanding of orbital mechanics and, in particular, the challenges presented by the nanosatellite platform. The inherent limitations in terms of nanosatellite power, propulsion, and communications systems necessitate careful orbital selection and maneuver planning to achieve mission objectives efficiently and reliably. This includes optimizing launch windows, understanding atmospheric drag effects in low Earth orbits (LEOs), and designing robust attitude control systems to maintain the desired pointing for scientific instruments or communications links. Our work focused on simulating the attitude control of PocketQube nanosatellites in a swarm using the R2022a release of the Matlab/Simulink environment. First, we provided a mathematical model for the relative coordinates of a nanosatellite swarm. Second, we developed a mathematical model of the linear quadratic regulator implementation in the relative navigation. Third, we simulated the attitude control of 20 PocketQube nanosatellites using the Matlab/Simulink environment. Finally, we provided the swarm scenario and attitude control system data. The simulation of an attitude control system for 20 PocketQube nanosatellites using an LQR controller in a swarm successfully demonstrated the stabilization capabilities essential for swarm operations in the space environment. A link to a video of the simulation is provided in the Results section. Full article

This study focuses on the development of sustainable construction materials via geopolymers synthesized from metakaolin and slag, aiming to identify environmentally friendly alternatives for construction material systems. A metakaolin–slag geopolymer mortar (MK–slag) was prepared using metakaolin and slag as fully solid waste raw materials, with sodium silicate solution and sodium hydroxide acting as composite activators. Initially, single-factor experiments were conducted to determine the optimal ranges for metakaolin–slag content, water/binder ratio, and water glass modulus. Subsequently, response surface methodology was employed to develop regression equations that analyze the main and interaction effects of these variables on the 7-day and 28-day compressive strength and water absorption of the mortar. The optimal mix ratio was then identified. The microstructure and formation mechanisms of MK–slag mortar were studied using scanning electron microscopy (SEM), X-ray diffraction (XRD), and mercury intrusion porosimetry (MIP). The results indicate that all factors follow quadratic polynomial relationships with the response variables, showing a regression coefficient (R²) greater than 0.98, indicating an excellent model fit and prediction accuracy. According to model predictions, the optimal mix parameters under multi-objective optimization were found to be a metakaolin-to-slag ratio of 45%: 55%, a water/binder ratio of 0.45, and a water glass modulus of 1.3. After 28 days of curing, the primary hydration products were gel-like substances such as N-A-S-H and C-A-S-H. These gels interweave and overlap to form a high-density, structurally robust binary solid waste geopolymer mortar. This approach expands the application of solid waste materials, such as metakaolin and slag, while enhancing the recycling and utilization efficiency of these waste products. Full article

This paper analyzes the functional feasibility and strategic value of hybrid hydrogen storage and photovoltaic (PV) energy systems at isolated areas, specifically at Egypt’s Shalateen station. The paper is significant as it formulates a solution to the energy independence coupled with economic feasibility issue in regions where the basic energy infrastructure is non-existent or limited. Through the integration of a portfolio of advanced optimization algorithms—Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Multi-Objective Genetic Algorithm (MOGA), Pattern Search, Sequential Quadratic Programming (SQP), and Simulated Annealing—the paper evaluates the performance of two scenarios. The first evaluates the PV system in the absence of hydrogen production to demonstrate how system parameters are optimized by Pattern Search and PSO to achieve a minimum Cost of Energy (COE) of 0.544 USD/kWh. The second extends the system to include hydrogen production, which becomes important to ensure energy continuity during solar irradiation-free months like those during winter months. In this scenario, the same methods of optimization enhance the COE to 0.317 USD/kWh, signifying the economic value of integrating hydrogen storage. The findings underscore the central role played by hybrid renewable energy systems in ensuring high resilience and sustainability of supplies in far-flung districts, where continued enhancement by means of optimization is needed to realize maximum environmental and technological gains. The paper offers a futuristic model towards sustainable, dependable energy solutions key to the energy independence of the future in such challenging environments. Full article

Economic dispatch in wind-integrated power systems is a critical challenge, yet many recent metaheuristics suffer from premature convergence, heavy parameter tuning, and limited ability to escape local optima in non-smooth valve-point landscapes. This study proposes a new hybrid optimization framework, the Fractional Grasshopper Optimization algorithm (FGOA), which integrates fractional-order calculus into the standard Grasshopper Optimization algorithm (GOA) to enhance its search efficiency. The FGOA method is applied to the economic load dispatch (ELD) problem, a nonlinear and nonconvex task that aims to minimize fuel and wind-generation costs while satisfying practical constraints such as valve-point loading effects (VPLEs), generator operating limits, and the stochastic behavior of renewable energy sources. Owing to the increasing role of wind energy, stochastic wind power is modeled through the incomplete gamma function (IGF). To further improve computational accuracy, FGOA is hybridized with Sequential Quadratic Programming (SQP), where FGOA provides global exploration and SQP performs local refinement. The proposed FGOA-SQP approach is validated on systems with 3, 13, and 40 generating units, including mixed thermal and wind sources. Comparative evaluations against recent metaheuristic algorithms demonstrate that FGOA-SQP achieves more accurate and reliable dispatch outcomes. Specifically, the proposed approach achieves fuel cost reductions ranging from 0.047% to 0.71% for the 3-unit system, 0.31% to 27.25% for the 13-unit system, and 0.69% to 12.55% for the 40-unit system when compared with state-of-the-art methods. Statistical results, particularly minimum fitness values, further confirm the superior performance of the FGOA-SQP framework in addressing the ELD problem under wind power uncertainty. Full article

Adaptive filtering remains a cornerstone of modern signal processing but faces fundamental challenges when confronted with rapidly changing or nonlinear environments. This work investigates the integration of deep learning into adaptive-filter architectures to enhance tracking capability and robustness in non-stationary conditions. After reviewing and analyzing classical algorithms—LMS, NLMS, RLS, and a variable step-size LMS (VSS-LMS)—their theoretical stability and mean-square error behavior are formalized under a slow-variation system model. Comprehensive simulations using drifting autoregressive (AR(2)) processes, piecewise-stationary FIR systems, and time-varying sinusoidal signals confirm the classical trade-off between performance and complexity: RLS achieves the lowest steady-state error, at a quadratic cost, whereas LMS remains computationally efficient with slower adaptation. A stabilized VSS-LMS algorithm is proposed to balance these extremes; the results show that it maintains numerical stability under abrupt parameter jumps while attaining steady-state MSEs that are comparable to RLS (approximately 3 × 10⁻²) and superior robustness to noise. These findings are validated by theoretical tracking-error bounds that are derived for bounded parameter drift. Building on this foundation, a deep-learning-driven adaptive filter is introduced, where the update rule is parameterized by a neural function, $U_{\theta }$, that generalizes the classical gradient descent. This approach offers a pathway toward adaptive filters that are capable of self-tuning and context-aware learning, aligning with emerging trends in AI-augmented system architectures and next-generation computing. Future work will focus on online learning and FPGA/ASIC implementations for real-time deployment. Full article

In this paper, a hierarchical adaptive control strategy is proposed to enhance the handling stability of distributed drive electric vehicles. In this strategy, the upper-level fuzzy controller calculates the additional yaw moment and rear wheel angle by utilizing the error between the actual and the target yaw velocity, as well as the error between the actual and the target sideslip angle. The quadratic programming algorithm is adopted to achieve the optimal torque distribution scheme through the lower-level controller, and the electronic stability control system (ESC) is utilized to generate the braking force required for each wheel. The four-wheel steering controller optimizes the rear wheel angle by using proportional feedforward combined with fuzzy feedback or Akerman steering based on the steering wheel angle and vehicle speed, through actuators such as active front-wheel steering (AFS) and active rear-wheel steering (ARS), which generate the steering angle of each wheel. This approach is validated through simulations under serpentine and double-lane-change conditions. Compared to uncontrolled and single-control strategies, the actuators are decoupled, the actual sideslip angle and yaw velocity of the vehicle can effectively track the target value, the actual response is highly consistent with the expected response, the goodness of fit exceeds 90%, peak-to-peak deviation with a small tracking error. Full article

The energy efficiency and hover stability of unmanned aerial vehicles are critical factors, since improper battery utilization and unstable control are major sources of operational failures and accidents. The proportional–integral–derivative (PID) controller, which is applied in approximately 97% of multirotor unmanned aerial vehicle (UAV) systems, is widely used due to its simplicity; however, it is sensitive to external disturbances and often fails to ensure optimal energy utilization, resulting in reduced flight time. Therefore, the experimental investigation of advanced control methods in a real physical environment is well justified. The objective of the present research is the comparative evaluation of seven control strategies—PID, linear quadratic controller with integral action (LQI), model predictive control (MPC), sliding mode control (SMC), backstepping control, fractional-order PID (FOPID), and H∞ control—using a single-degree-of-freedom drone test platform in a MATLAB R2023b-Arduino hardware-in-the-loop (HIL) environment. Although the theoretical advantages and model-based results of the aforementioned control methods are well documented, the number of real-time comparative HIL experiments conducted under identical physical conditions remains limited. Consequently, only a small amount of unified and directly comparable experimental data is available regarding the performance of different controllers. The measurements were performed at a reference height of 120 mm under disturbance-free conditions and under wind loading with a velocity of 10 km/h applied at an angle of 45°. The controller performance was evaluated based on hover accuracy, settling time, overshoot, and real-time measured power consumption. The results indicate that modern control strategies provide significantly improved energy efficiency and faster stabilization compared to the PID controller in both disturbance-free and wind-loaded test scenarios. The investigations confirm that several advanced controllers can be applied more effectively than the PID controller to enhance hover stability and reduce energy consumption. Full article

This paper presents a robust data-driven methodology for estimating transmission loss coefficients (B-coefficients) in power systems using linear and quadratic programming (LP and QP), both of which belong to the family of convex optimization models. The first model employs a linear objective function with linear constraints, ensuring computational efficiency for simpler scenarios. The second model utilizes a quadratic objective function, also under linear constraints, to better capture more complex nonlinear relationships. By framing the estimation problem as a parameter identification task, both methodologies minimize the cost functions that quantify the mismatch between measured and modeled power losses. By considering a broad range of operational scenarios, our approach effectively captures the stochastic behavior inherent in power system operations. The effectiveness of both the LP and QP models is validated in terms of their ability to accurately extract physically meaningful B-coefficients from diverse simulation datasets. This study underscores the potential of integrating linear and quadratic programming as powerful and scalable tools for data-driven parameter estimation in modern power systems, especially in environments characterized by uncertainty or incomplete information. Full article

In real life, almost all systems are nonlinear in nature. The electromagnetic levitation system (EMLS) is one such system that has a wide range of applications due to its frictionless, fast, and affordable technique. Optimal control and sliding mode control (SMC) techniques are often used controllers for EMLS. However, these techniques can achieve the required levitation but lag in having perfect set-point tracking and robustness against uncertainties. To get over these drawbacks, this article proposes the design of unconstrained mismatched uncertainties, constrained mismatched uncertainties, and integral sliding mode control with mismatched uncertainties for the current-controlled-type electromagnetic levitation system (CC-EMLS). The modeled equations of CC-EMLS are transfomed in terms of the mismatched uncertainties, and the required control action is obtained with and without constraints on the control input. The quadratic performance function is suggested for the unconstrained control scheme and is solved using the Hamilton–Jacobi–Bellman (HJB) equation. The non-quadratic cost function is designed for the constrained control method, and the HJB equation is utilized to obtain the solution. Both control schemes provide robustness to the system, but deviations in the set point are observed in tracking the position of the ball when the changes in the payload occur in the system. Therefore, integral sliding mode control with robust–optimal (IOSMC) gain is proposed for the CC-EMLS to overcome the steady-state error in the other two schemes. The stability is proven using the direct method of Lyapunov stability. The essential studies based on the simulation are carried out to showcase the performance of the proposed control schemes. The integral performance indicators are compared for all three proposed control schemes to highlight the efficacy, robustness, and efficiency of the designed controllers. Full article

Accurate modeling and parameter estimation of photovoltaic (PV) systems are vital for advancing energy sustainability and achieving global decarbonization goals. Reliable PV models enable better integration of solar resources into smart grids, improve system efficiency, and reduce maintenance costs. This aligns with the vision of sustainable energy systems that combine intelligent optimization with environmental responsibility. The recently introduced Black-Winged Kite Algorithm (BWKA) has shown promise by emulating the predatory and migratory behaviors of black-winged kites; however, it still suffers from issues of slow convergence, limited population diversity, and imbalance between exploration and exploitation. To address these limitations, this paper proposes an Improved Black-Winged Kite Algorithm (IBWKA) that integrates two novel strategies: (i) a Soft-Rime Search (SRS) modulation in the attacking phase, which introduces a smoothly decaying nonlinear factor to adaptively balance global exploration and local exploitation, and (ii) a Quadratic Interpolation (QI) refinement mechanism, applied to a subset of elite individuals, that accelerates local search by fitting a parabola through representative candidate solutions and guiding the search toward promising minima. These dual enhancements reinforce both global diversity and local accuracy, preventing premature convergence and improving convergence speed. The effectiveness of the proposed IBWKA in contrast to the standard BWKA is validated through a comprehensive experimental study for accurate parameter identification of PV models, including single-, double-, and three-diode equivalents, using standard datasets (RTC France and STM6_40_36). The findings show that IBWKA delivers higher accuracy and faster convergence than existing methods, with its improvements confirmed through statistical analysis. Compared to BWKA and others, it proves to be more robust, reliable, and consistent. By combining adaptive exploration, strong diversity maintenance, and refined local search, IBWKA emerges as a versatile optimization tool. Full article