Carbon Fiber Reinforced Polymer (CFRP) composites are widely used in reinforced concrete beams (RCBs) due to their high strength-to-weight ratio, corrosion resistance, and ease of application. However, accurately predicting the failure mode characteristics in these beams remains a significant challenge. This study presents a comprehensive framework for analyzing and predicting the failure modes of CFRP-strengthened RCBs using Deep Residual Neural Networks (DRNNs), finite element modeling (FEM), and Density-Based Sensitivity Analysis (DBSA). The proposed framework integrates experimental data and FEM simulations, leveraging DRNNs to model complex, nonlinear relationships between input parameters and failure mechanisms. DRNNs' architecture—comprising convolutional layers, residual building units, and batch normalization—facilitates accurate prediction of debonding loads, displacements, and failure modes. Residual shortcuts (i.e., connections), unlike other neural network architectures, allowed to bypass a few layers in the deep network architecture, circumventing the regular training with high accuracy problems. The DRNNs were instrumental in reducing reliance on large-scale physical experiments by generating synthetic data points required for DBSA convergence, making this framework practical for real-world applications. The study highlights the efficiency of DRNNs in reducing experimental requirements, achieving a high prediction accuracy of 90% and an AUC of 0.87. Validation of FEM with experimental results confirmed the framework's reliability in replicating real-world structural behavior. DBSA identified CFRP thickness and concrete density as critical factors influencing debonding load and displacement, while dry fiber density predominantly affects failure modes. Also, elastic properties of CFRP and concrete are essential for influencing debonding patterns.

Nanofluids are superior to pure fluids in heat transfer applications, such as direct solar collectors, due to enhanced thermophysical characteristics. Optimal dispersion is crucial, and direct ultrasonication is the most effective method in dispersing nanoparticles and preparing stable nanofluids; however, the optimal sonication time remains uncertain across different nanofluid types. In this study, alumina (Al₂O₃)–water nanofluid (0.1 wt%) and sodium dodecyl sulfate (SDS) surfactant were prepared using ultrasonication (up to 240 min). This study linked sonication duration to performance indicators—including zeta potential, particle size distribution, optical absorbance, pH, conductivity, and solar thermal conversion—to investigate dispersion and Photothermal Conversion Efficiency (PTE) under a solar simulator. The results indicate that sonication for up to 180 min enhances zeta potential, reduces agglomerate size …

This paper presents a multiport converter (MPC) designed for electric vehicle (EV) applications, with
potential use in renewable energy systems (RES). The proposed MPC interfaces the AC grid, enabling
seamless energy transfer from input sources to multiple output ports. These output ports facilitate the
simultaneous management of two distinct voltage levels through a single DC link, enhancing system
flexibility and efficiency. The Control scheme, employing Proportional-Resonant (PR) and Propor-
tional-Integral (PI) controllers, is implemented to regulate power flow between input sources and
loads. The system operates in three modes: Mode 1 employs power factor correction (PFC) to
synchronize voltage and current while reducing harmonics, the Total Harmonic Distortion (THD) of
the grid current is 3.65%; Mode 2 allows grid-to-battery charging while supplying a low-voltage load;
and Mode 3 functions as a single-input dual-output converter to power both the motor and auxiliary
loads. Additionally, Artificial Gorilla Troops Optimizer (AGTO) is used to rapidly and precisely tune
controller parameters. The proposed MPC features a modular structure and achieves a high efficiency
of 98.64%, surpassing the reported efficiencies in previous studies, while consisting of 12 components
only, making it a promising solution for sustainable energy management in EVs and RES applications.

This study presents a small-scale hip exoskeleton incorporating bi-directional artificial muscles constructed with springs of Shape Memory Alloy (SMA). The prototype can effectively support hip motion in both extension and flexion, spanning an angular range of −20∘ to 200∘. Experiments for thermo-mechanical characterization were executed to assess the performance of the SMA muscles throughout the entire motion range. The outcomes not only confirmed the suitability of the SMA muscles for the designed exoskeleton but also provided valuable insights into their behavior and capabilities. System identification experiments were carried out to establish an accurate transfer function, guiding the tuning of Proportional-Integral-Derivative (PID) controllers for enhanced motion-control effectiveness. The safety of the SMA system was addressed with a focus on preventing overheating. Challenges in accurately measuring the temperature of a thin spring were overcome by utilizing two thermocouples for each SMA springs group. Additionally, conventional SMA temperature measurement methods, such as infrared and resistance-based techniques, are limited by high cost, nonlinearity, and small range. This study presents a model-based temperature estimation algorithm that integrates a heat transfer model, electrical input data, and thermocouple data to enable accurate and real-time SMA temperature estimation without additional sensors, offering a cost-effective and reliable alternative. To evaluate the small hip prototype and its controller capabilities, control experiments were executed for both stepping and sinusoidal trajectories. The exoskeleton successfully tracked the desired trajectories, showing its precision. Moreover, system performance under adaptive control was further investigated, revealing an RMSE of 0.94∘ 94⁰ in sinusoidal trajectory experiments, indicating reliable disturbance rejection in the angle measurements.

The main challenge in the automation of the large rotary crane with tower-torsion is the accurate positioning and vibration suppression of the load-sway. The start-of-the-art optimal trajectory generation approaches need to consider several state and input constraints to increase the accuracy; therefore, it requires a large amount of computation time and is not applicable for the practical environment. This study presents an efficient method for optimal trajectory generation considering load-sway suppression and collision avoidance in a fast computation time, which includes two control strategies: the offline bi-objective trajectory generation between the contradictory objectives of total motion time and the collision avoidance fitting function, and the online modification of the optimal trajectory, which is formulated as one-degree-of-freedom optimization to reduce the total motion time and satisfy the entire constraints. The experimental validation with a lab-scale three-dimensional rotary crane is provided to show the effectiveness of the proposed method for practical applications.

Rotary crane systems are essential for transporting heavy loads and hazardous materials. Manual operation can be challenging for new or unskilled operators. This study addresses the challenge of precise final load positioning in construction sites by proposing a trajectory generation system that integrates obstacle avoidance and load-sway suppression. A load monitor camera (LMC) captures the load environment, and the result is displayed on a user-friendly interface designed with error prevention, simplicity, and ergonomic considerations. A usability evaluation has confirmed that the interface reduces task completion time and is well accepted by novice users. The operator selects the final load position from the LMC image, after which a slow-motion trajectory is automatically generated using a cycloidal velocity profile to suppress load-sway. The A* algorithm is used for obstacle avoidance, and its efficiency has been validated through comparison with the Dijkstra algorithm. A benchmark comparison with an S-curve trajectory using trapezoidal trajectory profile has demonstrated the proposed method’s superiority in minimizing sway. Additionally, a disturbance sensitivity analysis under wind conditions has evaluated system robustness and highlights potential improvements. Simulations and lab-scale experiments have confirmed that the proposed method enables safe, smooth, and precise final positioning while avoiding obstacles.

The calculation of the output current of distribution generation (DG) units interfaced with an inverter during the fault is a major issue for isolated and grid-connected distributed networks. The droop control inverter interfaced DG has controlled output current within 2 pu. during the fault. Furthermore, the current output of DG during the fault depends on solar irradiation and wind speed, increasing the uncertainty due to the intermittent nature of renewable energy sources. The installation of DG modifies the fault current direction and strength, which makes relay coordination more difficult. Overcurrent relays are used to defend isolated and grid-connected microgrids. This paper uses different techniques to study the fault current's probability distribution function (PDF) for isolated and grid-connected MGs. We use the droop control and virtual impedance techniques to calculate the probability of the short circuit current that the inverter-interfaced DG contributes. Wind and PV system output power generation samples are tested on MGs using the Monte Carlo Simulation (MCS) approach. A coordination time probability for relays on each line has been calculated to find the mean and standard deviation values of a setting time for overcurrent relays on a faulted bus. The proposed probabilistic model has been tested on the isolated and grid-connected IEEE 33-bus with 5 DGs and MGs using MATLAB code. We found that the droop control method gives a much longer overcurrent relay operating time than the virtual impedance method. This is true for DG buses for both modes of isolated and grid-connected MGs, as well as buses that connect branches. Additionally, for two modes—isolated and grid-connected MGs—the standard deviation of the relay operating time calculated by droop control is higher than its value calculated by the virtual impedance on the same bus.