Lakshmi Narain College of Technology

This study experimentally characterizes Myrtus communis leaves and integrates those data into a computational model to improve indirect solar drying performance. Sorption isotherms were measured by the static gravimetric method at 28, 40, and 50 degrees C; GAB and the medicinal-plant-tailored LESPAM models best described adsorption/ desorption (maximum R 2 up to 0.9989 and reduced chi 2 as 6.3 & times; 10 -4 ). Thin-layer drying experiments in an indirect solar dryer (collector heat flux 700 W/m 2 ; inlet air 0.8 m/s) were fitted with several kinetic models: the Midilli model performed best (R 2 = 0.9944; reduced chi 2 = 1.79 & times; 10 -4 ). Experimentally, leaves reached marketable dryness in about 6 h under Constantine (Algeria) conditions. COMSOL Multiphysics CFD simulations, driven by measured sorption and kinetic parameters, reproduced intra-chamber maxima of 319 K and air speeds up to 0.83 m/s, and captured humidity evolution showing negligible vapor flux after 6 h. Non-uniform airflow is identified as the main cause of drying heterogeneity, and design/operational changes (e.g., adjusted inlet velocity and tray layout) are recommended to improve uniformity. As result, ensuring uniform airflow distribution helps in minimizing the experimental effort and reduce parameter dependency, hence simplify the overall process for the benefit of general use in industrial applications. The combined experimental-computational approach enables realistic performance prediction. Proposed design modifications has been studied, which leads to optimize the airflow distribution, and as result, reducing the heterogeneity inside the solar dryers has been achieved.

Partial shading reduces solar PV array performance by causing mismatch losses and multiple power peaks, making conventional MPPT methods ineffective in tracking the global maximum power point.*. This paper proposes an intelligent hybrid control framework that integrates LSTM neural networks with ACO to dynamically reconfigure PV arrays for enhanced power generation. The LSTM model forecasts short-term irradiance and shading patterns using time-series sensor data, while the ACO algorithm determines the optimal series–parallel configuration of PV modules under predicted conditions. A real-time switching matrix implements the selected configuration, and a closed-loop feedback mechanism continuously updates both the LSTM model and ACO pheromone trails based on actual system performance. MATLAB-based simulation results demonstrate high prediction accuracy (R2 = 0.992), low error metrics (MAPE = 2.54%), and improved power extraction under partial shading, validating the effectiveness of the proposed mixture approach.

Football is a sport that enjoys massive popularity and at the same time it is a huge financial market. The worth of football players in the market nowadays has a tendency of being on a constant rise. The use of computer science in sports analysis has totally transformed the process of assessing players and selecting teams by means of the sophistication in data analytics, machine learning (ML), and artificial intelligence (AI). The model that this study uncovers is a player recommendation model that about data analytics along with AI-based visualization techniques which help crisis and recruitment in football. This research is based on the FIFA-20 dataset, but it is not aimed at making predictions about video-game ratings. The dataset is instead considered to be a structured and standardized proxy of the actual football performance characteristics, with the rating being a summative assessment of technical, physical and tactical properties that are of interest when applied to football analytics. A model that utilizes a Multi-Layer Perceptron (MLP) architecture with two hidden layers is proposed and evaluated in comparison to the Rio de Janeiro and other models like Optimized Linear Regression, LightGBM, Random Forest Regression (RFR), and XGBoost. The suggested MLP architecture won in generalizability with an R² score of 99.13, RMSE of 0.6410, and MAE of 0.4485, which were the scores of competing models. The cross-validation findings, which provided an average R² and a standard deviation, further substantiated its stability. This research backs up the claim that AI-based tools, among other things, could be used in talent spotting with precious tips for player selection, efficiency evaluation, and team distribution in contemporary football.

This paper presents a compact dual-port dielectric–graphene-based terahertz (THz) antenna designed for next-generation 6G communication systems. The proposed antenna is excited through a rectangular slot aperture, which efficiently couples energy from the microstrip feed to the dielectric resonator, thereby enhancing impedance matching and radiation performance. To enhance performance, two engineered metasurface structures are incorporated into the design. A vertically oriented metasurface wall is introduced between the ports to suppress surface wave coupling and improve inter-port isolation beyond 25 dB. Additionally, a horizontally suspended metasurface acting as a partially reflecting surface (PRS) enables controlled beam tilting of approximately ± 45°, thereby enhancing radiation pattern diversity. The antenna is realized on a compact SiO₂ substrate (thickness: 1.58 μm, εr = 3.5) with overall dimensions in the micrometer range, making it suitable for integrated THz systems. It operates efficiently over the 4.4–4.75 THz frequency band, achieving a peak gain of approximately 5.8 dBi with stable radiation characteristics. The proposed design also exhibits excellent MIMO performance with envelope correlation coefficient (ECC) less than 0.15 and diversity gain close to 10 dB. The results are validated using both HFSS and CST full-wave solvers, showing strong agreement.

The rapid growth of wind power generation has created a demand for intelligent control techniques capable of maintaining reliable and efficient turbine operation under continuously changing wind conditions. Conventional controllers, including PID controllers and optimization methods with static parameters, often experience performance degradation because they cannot effectively adapt to nonlinear system dynamics and unpredictable wind variations. To overcome these limitations, this work describes an ML-GA for adaptable minimization of wind turbine controller constraints, adaptable a focus on blade pitch regulation. The proposed framework combines a ML model with a GA to continuously adjust controller parameters according to the turbine's operating conditions. The learning model utilizes historical and operational data to guide the optimization process, thereby improving search efficiency and reducing computational effort. Furthermore, adaptive crossover and mutation operators enhance the exploration and exploitation capabilities of the genetic algorithm under dynamic environments. The proposed controller is implemented and evaluated in the MATLAB environment. The results demonstrate that the proposed approach increases the comparative power output by 12.77%, provides more effective rotor speed regulation, and exhibits superior adaptability to fluctuating wind conditions. These findings indicate that the controller is a capable intelligent control strategy for improving the recital and reliability of modern wind turbine conversion systems (WTCS).