Pailan College of Management and Technology

The biomass-based dryer of agricultural produce is a reliable and consistent drying solution for regions with abundant rainfall to reduce postharvest losses. It is known that the performance of dryers varies depending on the use of energy storage materials and associated thermal profiles. This article proposes the design of a biomass-fueled natural convection dryer and the thermal stress analysis of its structures based on the space-time geometry considering multidimensional temperature fields. We present the computational analysis of the thermal profiles of the dryer integrating the energy storage materials. The experiments are conducted under different conditions, such as (a) without thermal storage materials, (b) using paraffin wax or pebbles as energy storage materials, and (c) using both the energy storage materials in a mix. Results indicate that combining thermal storage materials yields maximum heat retention, maintaining higher temperatures for a longer time duration. Thermal stress analysis confirms that all dryer components remain structurally safe under operating temperatures, with manageable thermal stresses and adequate allowances for expansion ensuring stable and reliable performance. Furthermore, we present the topological analysis of heat distribution profiles of drying trays placed in the drying chamber providing analytical insights. The analytically predicted thermal stresses were validated using comsol thermo-mechanical von Mises stress simulations, showing strong agreement with deviations within 1.8-4.9% for key dryer components. We show that there is interplay between various uniformities of thermal profiles and the topological formulations exposing several interesting properties, which would lead to the improved design for better utilization of available thermal energy in the drying chamber.

In this study, we aim to enhance temperature control in electric furnaces, addressing key challenges in precision and response stability. We propose an integrated approach featuring a proportional–integral–derivative with N filter (PIDN) controller alongside the artificial rabbit’s optimization (ARO) algorithm. The proposed PIDN controller incorporates adaptive tuning techniques designed to improve response accuracy and reduce overshoot, tailored specifically for the dynamic requirements of electric furnace applications. To optimize the PIDN parameters, we employ the ARO algorithm, a recent metaheuristic inspired by rabbit social behaviors, which has been customized for this control application. To evaluate the performance of the proposed framework, we introduce a modified objective function based on the integral of absolute error, emphasizing both transient and steady-state improvements. Comparative assessments with traditional controllers and metaheuristic algorithms, including the electric eel foraging optimization and whale optimization algorithm tuned by PIDN, genetic algorithm tuned by PID, Cohen–Coon algorithm tuned by PID, and direct synthesis algorithm tuned by PID, confirm the superior efficacy of our approach. Extensive tests including statistical analysis, noisy condition, and time and frequency domain evaluations demonstrate that our controller remains robust under nonideal conditions, such as measurement noise, external disturbances, and saturation. Results underscore the adaptability and effectiveness of this approach, marking a significant advancement in temperature control for electric furnaces.

The problem of infinite impulse response (IIR) system identification is a key and demanding issue in signal processing and control systems, which requires appropriate system dynamics describing a model that also ensures the system’s stability and computational efficiency. In this study, a modified atom search optimizer (mASO) is proposed, combining an adaptive gbest-guided mechanism to overcome weaknesses in the standard atom search optimizer. Through this proposed mASO, time wastes that are related to poor local optimum and loss of control over exploration and exploitation processes are tamed, meaning that this idea is suited for difficult instances of OCO. The algorithm was tested on second to fifth-order systems and employed the same-order models as well as the reduced-order IIR models. These analyses include mean square error (MSE) estimates, pole-zero diagram checks, and statistical tests for significance for a variety of factors. Several optimization methods were employed in order to compare the performance of mASO, including ASO, moth flame optimizations, particle swarm optimizations, inclined plane system optimization, gravitational search algorithm, genetic algorithm, selfish herd optimizations, and many other contemporary ones. In all the systems mentioned, mASO was found to have performed well in comparison, noting that average MSE values of the second order same order model were as high as 7.8072E−34 and 3.6286E−03 for the fourth order reduced order model. Details of pole-zero diagrams confirmed stability and dynamic accuracy, while the statistical results indicated that mASO is consistent and robust under varying systems. The findings point out the mASO’s capacity to be used in complex IIR system identification tasks with high efficiency. Its proven success against other algorithms makes the mASO a credible and formidable technique for several engineering works, including the design of robust filters and the modeling of dynamic systems.

Advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the medical field and transformed translational medicine. These technologies enable more accurate disease trajectory models while enhancing patient-centered care. However, challenges such as heterogeneous datasets, class imbalance, and scalability remain barriers to achieving optimal predictive performance. This study proposes a novel AI-based framework that integrates Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) to address these challenges. The framework was evaluated using two distinct datasets: MIMIC-IV, a critical care database containing clinical data of critically ill patients, and the UK Biobank, which comprises genetic, clinical, and lifestyle data from 500,000 participants. Key performance metrics, including Accuracy, Precision, Recall, F1-Score, and AUROC, were used to assess the framework against traditional and advanced ML models. The proposed framework demonstrated superior performance compared to classical models such as Logistic Regression, Random Forest, Support Vector Machines (SVM), and Neural Networks. For example, on the UK Biobank dataset, the model achieved an AUROC of 0.96, significantly outperforming Neural Networks (0.92). The framework was also efficient, requiring only 32.4 s for training on MIMIC-IV, with low prediction latency, making it suitable for real-time applications. The proposed AI-based framework effectively addresses critical challenges in translational medicine, offering superior predictive accuracy and efficiency. Its robust performance across diverse datasets highlights its potential for integration into real-time clinical decision support systems, facilitating personalized medicine and improving patient outcomes. Future research will focus on enhancing scalability and interpretability for broader clinical applications.

Operational technology, industrial automation, advanced healthcare systems, and smart city infrastructures are common forms of IoT integrated distributed networks. Numerous IoT components require vast amounts of power for sensing, data extraction, processing, and sharing over the internet. Moreover, IoT devices are in operation with a lack of standards that poses a severe security threat. Intrusion detection systems and cryptographic algorithms are two important components in the security posture of a cyberspace. IoT applications provide real-time services, so security measures are active 24/7/365. Hence, these algorithms are also related to energy consumption. It is a study of security algorithms based on their hardware and software implementations. This review systematically searches for and selects the latest research documents, conducting an analysis of sustainability and security. It depicts the lightweight features of cryptographic algorithms; the relationship of gate density, chip area and power consumption in CMOS technology; and the importance of Machine Learning (MA) applications for IDS and cryptographic solutions in Next Generation IoT (NGIoT).