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Solar energy is critical to the global shift towards sustainable and low-carbon energy solutions. Unlike fossil fuels, solar energy produces no emissions during operation, making it a key driver of environmentally friendly power generation. However, the intermittency of solar power remains a challenge, necessitating efficient energy storage systems to ensure a steady supply. Thermal energy storage systems utilizing phase change materials (PCMs) offer a solution by storing excess solar energy and releasing it when needed. This study focuses on enhancing the charging capacity of the PCM within a novel triplex tube heat exchanger (TTHE). The design incorporates bionic-shaped fins to compensate for thermal conductivity within the PCM. Three artificial neural network (ANN) models were employed to estimate the melting duration for the PCM to attain liquid fractions of 0.5, 0.8, and 1. The expansion angle (θ), initial length (D), and length of fin branches (Z) were systematically varied to investigate their influence on heat absorption. The ANN models exhibited high accuracy, with R2 values of 0.998 for the liquid fraction of 0.5, 0.998 for the liquid fraction of 0.8, and 0.996 for the liquid fraction of 1, demonstrating their precise predictive capability. The results revealed that θ significantly reduced melting time, while Z consistently shortened the duration and eventually became the dominant factor. However, an excessive increase in these geometric parameters led to a counterproductive rise in the total melting time. Through optimization using a genetic algorithm, three optimal designs (design 1 (D1), design 2 (D2), and design 3 (D3)) were proposed. Compared to the fin-less TTHE, these designs reduced the full melting time by 71.58 %, 73.41 %, and 73.54 %, respectively. Faster melting improves the system's ability to store and release thermal energy more quickly, enabling more efficient utilization of solar power within the limited period of sunlight availability (usually 4–6 h a day). These findings underscore the potential of ANN-based approaches in optimizing the efficiency of solar energy storage systems.

This research investigates the thermohydraulic performance and exergy destruction associated with the flow of supercritical carbon dioxide (sCO2) within spirally coiled mini tubes. The study examines the impact of different cross-sectional geometries. The primary objective of the study is to examine the impact of critical parameters, including shape, hydraulic diameter, inlet temperature, mass flux, and operating pressure, on important variables such as friction factor, heat transfer coefficient, and exergy efficiency. The computational simulation employs the RNG k−ε model. The Coupled algorithm was utilized for the determination of velocity and pressure fields, utilizing second-order discretization for domain partitioning and first-order discretization for other terms. The carbon dioxide (CO2) was conceptualized as a compressible gas with complex thermophysical attributes that are contingent upon variations in temperature and pressure. The thermophysical properties of carbon dioxide are evaluated within a defined range of operating conditions (298. 15 K

This article introduces an innovative method for optimizing smart manufacturing system (SMS) by combining digital twin technology (DTT) with teaching–learning-based optimization (TBLO). It creates a simulated model of the physical manufacturing environment, enabling real-time monitoring, simulation and analysis. By leveraging the TLBO algorithm, the system enhances the decision-making process for complex manufacturing tasks, facilitating continuous improvement and adaptation to dynamic production demands. The proposed framework aims to minimize production costs, reduce downtime and improve overall efficiency by optimizing key parameters such as resource allocation, production scheduling and machine performance. Experimental results demonstrate that the DT-TLBO approach can reduce production costs by up to 20%, decrease downtime by 30% and improve overall system efficiency by 25%. This innovative combination of advanced technologies offers a promising solution for modern manufacturing challenges, paving the way for smarter, more responsive production environments.

Liver cancer ranks as the third most common cause of cancer-related death rate, resulting in an estimated 830,180 deaths globally. Recent, 2023 estimation of the American Cancer Society predicted around 29,380 deaths due to this disease in America alone. However, with early detection and proper drug management the survival rate can be greatly extended. Significantly, the noteworthy observation pertains to the absence of consistent drug responses among patients with identical cancer types at the same stage. Hence the need of the hour is the precision medicine that predict patient specific drug response based on molecular data genome expression. In this regard, we propose drug response system that uses deep learning framework that predicts drug response in new cell lines or patient with the aim of accurate prediction and suggesting précised medicine to that particular cancer patient. The prediction of the framework is on the lines of recommended system’s projection of drugs and cell lines into latent pharmacogenomic space. During our experimentation, we utilized the Genomics of Drug Sensitivity in Cancer dataset and observed that Cancer Drug Response and Patient Specific Recommender System (CDRP-PRS) consistently delivers robust predictions and effective response, even when applied to previously unseen patient-derived cell line datasets. CDRP-PRS inferences in the pharmacogenomic space are helpful to understand the drug mechanism to identify the cellular subtypes and their categorized associations. Further it is aimed is to predict the accuracy of drug responses for patients and suggest precise medications tailored to their specific cancer.

Detection of heart diseases (HD) at an early stage diminishes the mortality rate. However, handling huge data is cumbersome for physicians. Hence, there is a need for a tool regarding the automation and processing of large data to help in making precise decisions. The proposed methodology aims to predict cases of HD or cardiovascular diseases (CD) well in advance based on the features of the patient. In the proposed methodology, the Cleveland Dataset of HD collected from University of California, Irvine (UCI) repository is evaluated. The various phases in the proposed methodology include, insight about the data, analyzing the data, feature engineering and finally building the model. The K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), classifier and convolutional neural network (CNN) are used to predict the HD and are evaluated on the dataset. The metrics considered are classification accuracy (CA), recall (r) and precision (p). The resulted CA, ‘r’ and ‘p’ for KNN-based approach are 66.7%, 91.7% and 88.8%, respectively. The SVM-based approach with ‘linear’ kernel achieved CA, ‘r’ and ‘p’ are 74.2%, 85.0% and 74.0%, respectively. The ANN-based approach resulted in 70.08%, 77.0% and 84.2% of CA, ‘p’ and ‘r’, respectively. Finally, the CNN-based prediction achieved CA, ‘p’ and ‘r’ are 83.61%, 76.0% and 97.0%, respectively. The experimental study concludes that the CNN-based prediction model outperformed the KNN, SVM and ANN-based prediction approaches regarding the prediction accuracy. An optimization algorithm can be incorporated into the model in future.

This experimental study investigates the performance of a novel humidifier-dehumidifier (HDH) desalination system integrated with a vapor absorption refrigerator (VAR) and photovoltaic (PV) panels for humid regions. The effects of air mass flow rate (m˙a) and inlet air temperature on key performance indicators were evaluated. Increasing m˙afrom 0.0039 kg/s to 0.0071 kg/s led to a 73 % increase in freshwater output, a 35 % increase in Coefficient of Performance (COP), a 100 % increase in Gain of Output Ratio (GOR), and a 75 % increase in Recovery Ratio (RR), but a 20 % decrease in dehumidifier efficiency. The maximum freshwater production rate of 870 g/h and a GOR of 1.4 were obtained at m˙a= 0.0071 kg/s and inlet air temperature of 50 °C, representing a 117 % increase compared to the lowest temperature and flow rate tested. The cost of freshwater for solar and electric operation modes was 0.075 and 0.094 USD/kg, respectively. The system's novelty lies in the integration of HDH desalination with a VAR and PV system, enabling continuous freshwater production even under cloudy or nighttime conditions. The findings demonstrate the potential of the proposed system for sustainable freshwater production in humid areas.

This study aims to compare how well different designs of combined photovoltaic-thermal collectors work in terms of electricity and heat production. One big benefit of photovoltaic-thermal collectors, compared to regular solar panels, is that they cool down the module and make it produce more electricity efficiently. In simple terms, using this method produces heat and electricity in a device, which means one needs less space to install the collector and module. In a recent study, scientists looked at how well different designs of a solar panel that is also used to generate heat performed. They tested using water to keep it cool. To study and understand how different shapes and sizes affect the performance of a solar panel, we used a computer program called MATLAB to create models of the panel. The research shows that the sheet and tube design with a round or rectangular shape is the most efficient in terms of heat and overall performance. Moreover, when a glass cover is present, it causes electricity to be less efficient while making heat efficiency and overall heat energy higher.

Low-energy cases can be designed more efficiently by using analytical optimization. The non-linear thermal performance of buildings has led to the development of optimization techniques based on simulation. In building optimization, it is important to achieve superior solutions while minimizing calculation expenses. This study aims to optimize an Australian office building using the Balanced Tree Growth Optimizer (BTGO). It has resulted that more than 11.7 % of energy can be saved by the optimization process and also some energy-saving measures. A comparison of the utilized algorithm with benchmark algorithms including the Nelder-Mead method, hybrid Particle Swarm Optimization, Hooke-Jeeves, and Ant Colony Optimization for continuous domain showed that the BTGO can achieve better solutions and needs less computational time.

The heat treatment-T6 offers significant benefits for Aluminum Matrix composites due to changes of structure and material properties like strength, hardness and corrosion resistance. In this study, the preparation of reinforced Molybdenum Disulfide With Al-Zn-Mg alloy composites done by stir casting methodology. Three parameters like temperature (A), Time duration (B) and quenching medium (C) are considered for Heat Treatment Process (HTP). Quenching medium, time and temperature are reserved as constant. The main aim is to compare corrosion or wear characteristics (WC) and Hardness (BHN) of prepared samples before and after HTP. POD device used to conduct test of wear rate. 12 experiments are carried out by change of MOS2 particles volume (5-20 w.t %) and Temperature (400-700º C). the experimental outcomes are discussed and compare the mechanical properties for stir-casting and T6-HTP.