Accurate parameter identification is a critical prerequisite for reliable modeling, analysis, and control of nonlinear dynamical systems. This study introduces the stellar oscillation optimizer (SOO), a recently proposed metaheuristic inspired by the oscillatory behavior of stars, and investigates its effectiveness in estimating system parameters through a unified optimization framework. The identification problem is formulated as the minimization of a trajectory–mismatch cost function, where candidate solutions are iteratively refined by the oscillatory dynamics of SOO. To comprehensively evaluate its performance, four benchmark systems were considered: three canonical chaotic models (Lorenz, Chen, and Rössler) and a practical engineering case represented by a permanent-magnet synchronous motor (PMSM). The outcomes were benchmarked against several state-of-the-art algorithms, including Kirchhoff’s law algorithm (KLA), Tianji’s horse racing optimization (THRO), puma optimizer (PO), and hiking optimization algorithm (HOA), under a standardized protocol. The results show that SOO consistently achieves numerically convergent solutions with machine-precision-level residuals under deterministic and noise-free simulation settings, while maintaining strong robustness across independent runs. In chaotic benchmarks, the reported residuals approach floating-point limits, which indicates stable numerical convergence rather than guaranteed physical identifiability under real measurement conditions. On the PMSM model, SOO demonstrates accurate and repeatable parameter estimation within the adopted simulation framework.

Accurate and energy-efficient temperature regulation in electric furnace systems remains a challenging control problem due to nonlinear dynamics, significant thermal inertia, and inevitable time delays. Conventional proportional-integral-derivative (PID) and PID-acceleration (PIDA) controllers, though widely used, often exhibit degraded performance under such conditions, particularly when implemented in a single-degree-of-freedom. To address these limitations, this study proposes, for the first time, a two-degree-of-freedom (2-DOF) PIDA controller tailored for electric furnace temperature control. The controller structure allows independent tuning of set-point tracking and disturbance rejection by introducing separate feedforward paths in the proportional and derivative channels while maintaining integral and acceleration actions on the error signal. To optimize the controller parameters, the recently developed greater cane rat algorithm (GCRA) is employed for the first time in this context. A novel adaptive objective function (combining normalized overshoot, normalized settling time, and cumulative tracking error) guides the tuning process to achieve a balanced improvement in both transient and steady-state performance. The proposed GCRA-based 2-DOF PIDA controller is evaluated through extensive simulations and compared against state-of-the-art metaheuristic tuning approaches, including polar fox optimization (PFA), hiking optimization (HOA), success-history based adaptive differential evolution with linear population size reduction (L-SHADE), and particle swarm optimization (PSO), as well as several benchmark furnace control methods. Results demonstrate that the proposed method consistently achieves faster settling times, reduced overshoot, and near-zero steady-state error, while maintaining robustness under external disturbances and measurement noise. For instance, in the nominal case, the method yields an overshoot of 1.8382% and a settling time of 3.4542 s, outperforming PFA, HOA, L-SHADE, and PSO. Robustness tests under load disturbances and measurement noise confirm stable operation with minimal performance degradation, achieving less than 2.5% overshoot and under 4 s settling time across all evaluated scenarios. These findings highlight the potential of the GCRA-based 2-DOF PIDA controller as a high-precision and energy-efficient solution for temperature regulation in industrial time-delay systems.

This paper presents an AI-driven multi-level Delta-Sigma Modulator (DSM) for Software Defined Radios (SDRs) in 6G environments. By leveraging advanced artificial intelligence techniques, the proposed DSM dynamically selects both the optimal quantization levels and their corresponding values, significantly enhancing coding efficiency and signal-to-noise ratio (SNR). The architecture integrates signal input, AI-driven multi-level quantization, noise shaping, digital-to-analog conversion, feedback loop, and adaptive control, demonstrating substantial performance improvements under varying channel conditions. Extensive simulation results confirm that the model achieves superior adaptability, maintaining high coding efficiency across different noise and interference scenarios. Furthermore, the integration of deep reinforcement learning enables real-time dynamic optimization, ensuring system stability and robustness even in harsh 6G communication environments. This work sets a foundation for future advancements in cognitive SDR systems, highlighting the transformative role of AI in next-generation wireless technologies.

Parameter identification of chaotic systems such as Lorenz, Chen, and Rössler has long been recognized as a challenging inverse problem, since even slight perturbations in system coefficients can yield qualitatively different trajectories. Conventional time-domain error formulations are often ill-conditioned under these conditions, which has motivated the design of more robust objective functions and the adoption of metaheuristic optimization strategies. In this study, a hybrid birds of prey-based optimization with differential evolution (h-BPBODE) is introduced to address these challenges. The method enriches the four canonical behavioral phases of BPBO (individual hunting, group hunting, attacking the weakest, and relocation) by embedding DE mutation and crossover operators after each candidate update. This design injects recombinative diversity while retaining BPBO’s adaptive and collective search mechanisms, thereby improving the balance between exploration and exploitation. The algorithm is validated on Lorenz, Chen, and Rössler systems, where the task is to recover unknown parameters by minimizing trajectory mismatches between true and simulated models. Comparative simulations against standard BPBO, starfish optimization, hippopotamus optimization, particle swarm optimization (PSO), and DE confirm that h-BPBODE consistently achieves exact parameter recovery with negligible residuals, faster convergence, and markedly lower run-to-run variance. Statistical analyses, convergence traces, and parameter evolution curves further demonstrate its robustness and precision. These findings establish h-BPBODE as a reliable and efficient framework for chaotic system identification and suggest its potential for broader nonlinear estimation tasks.

The conventional least-mean-square (LMS) algorithm has a poor performance when the input autocorrelation’s eigenvalue spread is quite large. For instance, the cost function is inadequately described when impulsive noise is present, making it impossible for the LMS approach to correctly identify the system. The cost function of the conventional LMS method is enhanced by the addition of a $l_{0}$ or $l_{1}$ -norm penalty component. This extra term exploits system sparsity and improves the performance of the LMS algorithm. In this work, we propose a sparse variable step-size LMS approach in a code-division multiple access (CDMA) system. The proposed algorithm makes use of an arctan constraint in the cost function of the algorithm. This constraint enforces a zero attraction of the filter coefficients based on the relative value of each filter coefficient among all entries. Convergence analysis of the proposed algorithm in the mean sense is derived. The CDMA is employed by spreading the transmitted symbols using an m-sequence during the transmission process where the channel state is considered constant over a symbol period. At the receiver, decorrelation stage is considered prior to the channel estimation. This step improves the input signal-to-noise ratio (SNR) at the filter’s input, thereby accelerating the adaptive filter’s convergence. However, this gain, in the rate of convergence, comes at the expense of the system’s spectral efficiency. The proposed method shows high performance, in terms of steady-state mean-square deviation (MSD), compared to those of the zero attracting LMS (ZA-LMS) and the reweighted ZA-LMS (RZA-LMS) algorithms in a system identification setting with an impulsive noise environment. In addition, the proposed algorithm has shown significant performance in estimating the channel for CDMA communication systems in terms of bit-error rate (BER).

In the field of lighter substitute materials, sandwich plate models of composite and hybrid foam cores are used in this study. Three core structures: composite core structure and then the core is replaced by a structure of a closed and open repeating cellular pattern manufactured with 3D printing technology. It finally integrated both into one hybrid open-cell core filled with foam and employed the same device (WBW-100E) to conduct the three-point bending experiment. The test was conducted based on the international standard (ASTM-C 393-00) to perform the three-point bending investigation on the sandwich structure. Flexural test finding, with the hybrid polyurethane/polytropic acid (PUR/PLA) core, the ultimate bending load is increased by 127.7% compared to the open-cell structure core. In addition, the maximum deflection increased by 163.3%. The simulation results of three-point bending indicate that employing a hybrid combination of PUR-PLA led to an increase of 382.3%, and for PUR–TPU by 111.8%; however, the highest value recorded with PUR/PLA, which has the slightest stress error among the tests. Also, it is reported that when the volume fraction of reinforced aluminum particles is increased, the overall deformation becomes more sufficient, and the test accuracy improves; for example, rising from 0.5% to 3%, the midspan deflection of composite (foam-Al) is increased by 40.34%. There were noticeable improvements in mechanical properties in the 2.5% composite foam-Al.

In recent times, the rise in demand for IoT-based Human Activity Recognition (HAR) applications across diverse sectors such as health monitoring, elderly care, gait analysis, security, and Industry 5.0, has been noteworthy. A critical challenge encountered in these developments is the accuracy of reference models, prompting this research to focus on enhancing model precision through advanced machine learning (ML) methodologies. The study meticulously analyzed a distinct machine learning strategy, represented in the k-Nearest Neighbor (k-NN) approach. Data was meticulously gathered from 102 individuals, aged between 18 and 43, and segmented into training and testing datasets. These datasets were instrumental in the supervised learning phase, utilizing refined ML techniques. This rigorous process enabled the accurate identification of twelve daily activities, ranging from sedentary behaviors like sitting and laying to dynamic movements such as walking, jogging, and cycling. The findings revealed that Weighted k-Nearest Neighbor (Wk-NN) approach outperformed with a remarkable accuracy of 98.6

Electron cyclotron resonance heating method of Particle-in-Cell code was used to analyze heating phenomena, axial kinetic energy, and self-consistent electric field of confined electron plasma in ELTRAP device by hydrogen and helium background gases. The electromagnetic simulations were performed at a constant power of 3.8 V for different RF drives (0.5 GHz- 8 GHz), as well as for 1 GHz constant frequency at these varying amplitudes (1 V-3.8 V). The impacts of axial and radial temperatures were found maximum at 1.8 V and 5 GHz as compared to other amplitudes and frequencies for both background gases. These effects are higher at varying radio frequencies due to more ionization and secondary electrons production and maximum recorded radial temperature for hydrogen background gas was 170.41 eV. The axial kinetic energy impacts were found more effective in the outer radial part (between 0.03 and 0.04 meters) of the ELTRAP device due to applied VRF through C8 electrode. The self-consistent electric field was found higher for helium background gas at 5 GHz RF than other amplitudes and radio frequencies. The excitation and ionization rates were found to be higher along the radial direction (r-axis) than the axial direction (z-axis) in helium background gas as compared to hydrogen background gas. The current studies are advantageous for nuclear physics applications, beam physics, microelectronics, coherent radiation devices and also in magnetrons.

Elbow dislocation and instability present significant clinical challenges, necessitating a thorough understanding of the underlyingbio-mechanical mechanisms. In this study, a quasi-static three-dimensional finite element model of the human elbow joint isdeveloped to investigate stress distribution and stages of dislocation in the human elbow under various loading conditions. Themodel simulates the elbow joint in different degrees of flexion (30°, 45°, 60°, and 90°) and forearm positions (pronation andsupination), providing detailed insights into the progression of dislocation. Significant findings include the identification of highstress concentrations on the humerus at 90° flexion and on the radial and coronoid processes at 30°, 45°, and 60° flexion.Three reproducible stages of dislocation were observed, particularly in flexed positions with forearm pronation or supination.These stages align with experimental observations1 and highlight the initial occurrence of bony failures, such as radial headand ulnar coronoid fractures, preceding soft tissue tears. Clinically, the study underscores that early-stage low-impact posteriorelbow dislocations retain enough stability to be managed with closed reduction and early mobilization. However, as dislocationsprogress, significant damage to the medial and lateral collateral ligaments is expected, necessitating more invasive treatments.This research provides valuable bio-mechanical insights into elbow dislocation, aiding in the development of improved treatmentstrategies and enhancing patient outcomes through precise and timely clinical interventions. The validated FEM serves as apowerful tool for pre-surgical planning offering a comprehensive understanding of elbow joint mechanics

One of the most significant barriers to broadening the use of solar energy is low conversion efficiency, which necessitates the development of novel techniques to enhance solar energy conversion equipment design. The correct modeling and estimation of solar cell parameters are critical for the control, design, and simulation of PV panels to achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity to become caught in some local optima. In this study, a Growth Optimization (GO) algorithm is developed and simulated from humans’ learning and reflection capacities in social growing activities. It is based on mimicking two stages. First, learning is a procedure through which people mature by absorbing information from others. Second, reflection is examining one’s weaknesses and altering one’s learning techniques to aid in one’s improvement. It is developed for estimating PV parameters for two different solar PV modules, RTC France and Kyocera KC200GT PV modules, based on manufacturing technology and solar cell modeling. Three present-day techniques are contrasted to GO’s performance which is the energy valley optimizer (EVO), Five Phases Algorithm (FPA), and Hazelnut tree search (HTS) algorithm. The simulation results enhance the electrical properties of PV systems due to the implemented GO technique. Additionally, the developed GO technique can determine unexplained PV parameters by considering diverse operating settings of varying temperatures and irradiances. For the RTC France PV module, GO achieves improvements of 19.51%, 1.6%, and 0.74% compared to the EVO, FPA, and HTS considering the PVSD and 51.92%, 4.06%, and 8.33% considering the PVDD, respectively. For the Kyocera KC200GT PV module, the proposed GO achieves improvements of 94.71%, 12.36%, and 58.02% considering the PVSD and 96.97%, 5.66%, and 61.20% considering the PVDD, respectively.

This study assesses quad-band metamaterial perfect absorbers (MPAs) based on a double X-shaped ring resonator for electromagnetic interference (EMI) shielding applications. EMI shielding applications are primarily concerned with the shielding effectiveness values where the resonance is uniformly or non-sequentially modulated depending on the reflection and absorption behaviour. The proposed unit cell consists of double X-shaped ring resonators, a dielectric substrate of Rogers RT5870 with 1.575 mm thickness, a sensing layer, and a copper ground layer. The presented MPA yielded maximum absorptions of 99.9%, 99.9%, 99.9%, and 99.8% at 4.87 GHz, 7.49 GHz, 11.78 GHz, and 13.09 GHz resonance frequencies for the transverse electric (TE) and transverse magnetic (TM) modes at a normal polarisation angle. When the electromagnetic (EM) field with the surface current flow was investigated, the mechanisms of quad-band perfect absorption were revealed. Moreover, the theoretical analysis indicated that the MPA provides a shielding effectiveness of more than 45 dB across all bands in both TE and TM modes. An analogous circuit demonstrated that it could yield superior MPAs using the ADS software. Based on the findings, the suggested MPA is anticipated to be valuable for EMI shielding purposes.

To minimize the environmental pollution, encourage the usage of alternate fuels and limit the usage of fossil fuels, which strengthens the range of integration of renewable energy sources, namely wind and solar power, has taken significance in many nations. The interest in the economic and technological challenges of integrating renewable energy sources like wind power into power grid systems has grown in response to the rising demand. Forecasting is essential and critical for securing integration with the power grid systems. To balance the power supply and demand, the grid operators has to schedule an optimal power generation from fossil fuel-based power generation and give priority to wind power generation.In this work, an efficient System on Chip (SoC) based IoT sensors are used to collect the real time data. The field sample of wind speed and direction is taken in customized intervals (once in 5 seconds).For wind power generation forecasting, a powerful real time architecture with the combination of Apache Kafka, a messaging queue system together with Apache Storm a processing engine, Mongo DB a heterogeneous data base and with an intelligent machine learning algorithms for Clustering and Classification is used. The wind power generation mainly depends on three prime factors i.e. Wind speed, Wind direction and Wind density. Also, the environment temperature, and humidity also considered for this application. With the use of Density Based Spacial Clustering of Application with Noise (DBSCAN) algorithm, cluster the similar pattern of Wind velocity and direction which leads to maximum power generation through wind source. Based on the similar relationship of the pattern, it is possible to forecast the wind power generation in advance together with the standard static (historical) data already stored in the cloud server. Random forest classification algorithm is also used together with DBSCAN to improve the classification accuracy.

In this paper, a numerical algorithm is developed for the solution of second order linear and nonlinear integro-differential equations. The Haar collocation technique is applied to second order linear and nonlinear integro-differential equations. In Haar technique, the second order derivative in both linear and nonlinear integro-differential equation is approximated using Haar functions and the process of integration is used to obtain the expression of first order derivative and expression for the unknown function. Some linear and nonlinear examples are taken from literature for checking validation and convergence of proposed technique. The maximum absolute and root mean square errors are compared with the exact solution at different collocation and gauss points. The convergence rate using different numbers of collocation points is also calculated, which is approximately equal to 2.

The primary objective of this work is to emulate machine empathy through digitizing human emotions. A simple proofof-concept experiment is conducted, where a brain-computer interface (BCI) captures the brain's electroencephalogram (EEG) signals using an Emotiv Epoc headset. A two dimensional (2D) intensity (heat) map of the brain's EEG is obtained for a pre-defined set of an emotional stimulus, namely excitement and stress. An artificial neural network (ANN) is subsequently used for classifying the 2D image. The key contribution of this work is to leverage the already powerful and mature tools for image recognition developed in ANN systems for emotion recognition through adapting the 2D intensity map of the EEG brain activity. The resulting BCI system was set-up to control a surrogate humanoid robot, allowing the robot to emulate empathy and interact with the subject according to pre-defined behavioural models. The ANN classifier exhibited an accuracy of 87.5% for recognizing two of the emotional states targeted in this study.

Performance of adaptive filters highly depends on the eigenvalue spread of the autocorrelation matrix of the input signal. For example, performance of the least-mean-square (LMS) algorithm deteriorates if this eigenvalue spread is relatively high. Recently, a least lncosh (lncosh) algorithm has been proposed to enhance the performance of the LMS algorithm. The algorithm utilizes lncosh function of the error in its cost function. Based on this, we propose a new algorithm for that imposes an l 0 - norm penalty to the cost function of the lncosh algorithm. This penalty term is capable to exploit the system sparsity in system identification settings. The performance of the proposed algorithm has been measured in the presence of correlated and non-correlated input signals. The proposed algorithm has shown significant performance compared to those of the lncosh and re-weighted zero-attracting LMS (RZA-LMS) algorithms in different system identification setting.

This paper presents a fully integrated CMOS Operational Floating Current Conveyor (OFCC) circuit. The proposed circuit is designed for instrumentation amplifier circuits. The CMOS OFCC circuit is designed and simulated using Cadence in TSMC 90 m technology kit. The circuit aims at two different design goals. The first goal is to design a low power consumption circuit (LBW design) while the second is to design a high bandwidth circuit (HBW design). The total power consumption of the LBW design is 1.26 mW with 30 MHz bandwidth while the power consumption of the HBW design is 3 mW with 104.6 MHz bandwidth.

This paper presents a Performance comparison between the SerDes architecture and the time-based architectures. The challenges and the drawbacks in designing both architectures have been discussed. An example of 4-bit 2Gb/s and 5-bit 2.5Gb/s SerDes links and PPM-TDC links have been designed and simulated in 180nm CMOS technology. The FFT and the percentage energy concentrated in different bandwidths of the transmitted signals have been studied and compared. The timing diagram of the received signals at the end of 40-inch FR4 channel is provided and compared. 3Gb/s and 4Gb/s links have been designed and simulated too using SerDes and time-based approach and the results are compared. The time-based architectures show better performance over SerDes architectures when limited number of bits are transmitted

Recent advances in the field of neurorobotics are surveyed with emphasis on the areas of brain-computer interface systems, brain-based robots and the human brain project. A simple proof-of-concept experiment, inspired by the mapping of the human brain into a robot, is described and constructed. The capturing of brain electroencephalogram signals was performed through an Emotiv Epoc headset. The resulting brain-computer interface was set-up to control a surrogate NAO humanoid robot, while feeding sensory data back to the subject. The various applications of this emerging technology is discussed while emphasising its research and pedagogical value.

In sparse system identification, exploiting the sparsity property improves the performance of the least mean square (LMS) algorithm. The improvement is achieved by adding an $l_{0}$ or $l_{1}$-norm penalty term to the cost function of the conventional LMS algorithm. However, when the eignenvalue spread of the autocorrelation of the input is relatively large, the performance of the LMS algorithm is poor. For instance, the LMS algorithm cannot perform accurate system identification under the impulsive noise environment because its cost function is not well defined for such scenarios. In this paper, we propose a sparse variable step-size LMS algorithm that employs arctan constraint in the cost function of the algorithm. This constraint imposes a zero attraction of the filter coefficients according to the relative value of each filter coefficient among all the entries. This, in turn, leads to an improved performance when the system is sparse. Different experiments have been conducted to investigate the performance of the proposed algorithm. Simulation results show that the proposed algorithm outperforms the zero attracting LMS (ZA-LMS) and the reweighted ZA-LMS (RZA-LMS) algorithms in a system identification setting under impulsive noise environment.

In this paper, a dual-branch topology driven by a Delta-Sigma Modulator (DSM) with a complex quantizer, also known as the Complex Delta Sigma Modulator (CxDSM), with a 3-level quantized output signal is proposed. By de-multiplexing the 3-level Delta-Sigma-quantized signal into two bi-level streams, an efficiency enhancement over the operational frequency range is achieved. The de-multiplexed signals drive a dual-branch amplification block composed of two switch-mode back-to-back power amplifiers working at peak power. A signal processing technique known as quantization noise reduction with In-band Filtering (QNRIF) is applied to each of the de-multiplexed streams to boost the overall performances; particularly the Adjacent Channel Leakage Ratio (ACLR). After amplification, the two branches are combined using a non-isolated combiner, preserving the efficiency of the transmitter. A comprehensive study on the operation of this topology and signal characteristics used to drive the dual-branch Switch-Mode Power Amplifiers (SMPAs) was established. Moreover, this work proposes a highly efficient design of the amplification block based on a back-to-back power topology performing a dynamic load modulation exploiting the non-overlapping properties of the de-multiplexed Complex DSM signal. For experimental validation, the proposed de-multiplexed 3-level Delta-Sigma topology was implemented on the BEEcube™ platform followed by the back-to-back Class-E switch-mode power amplification block. The full transceiver is assessed using a 4th-Generation mobile communications standard LTE (Long Term Evolution) standard 1.4 MHz signal with a peak to average power ratio (PAPR) of 8 dB. The dual-branch topology exhibited a good linearity and a coding efficiency of the transmitter chain higher than 72% across the band of frequency from 1.8 GHz to 2.7 GHz.