In this work, pebbles of higher specific heat than the conventional absorber materials like aluminium or copper are proposed as a absorber in the solar flat plate collector. The proposed collector are integrated into the building design and constructed with masonry. Tests were conducted by varying the operating parameters which influence its performance, like the flow rate of the heat-absorbing medium, and the tilt of the collector using both coated and uncoated pebbles. The maximum temperature difference that could be measured for a conventional absorber was approximately 8 °C for a flow rate of 0.6 L/min. While for a coated and uncoated absorber, it was 7 °C and 5.5 °C respectively. This difference decreased with an increase in flow rates from 0.6 L/min to 1.2 L/min. For all the flow rates, it was observed that the average difference in efficiency between the coated and the conventional absorber collector is 5.82 %, while the difference between the coated and uncoated absorber collector is 15.68 %. Thus, it is very much evident that by replacing the conventional absorber with the proposed coated pebble absorber, the overall loss in efficiency is just 5.82 %, but the advantages are enormous. Along with the experimental study, numerical analysis was also carried out with CFD modeling. The numerical results agreed well with experimental results with the least error. Therefore, CFD simulation can be further used to optimize the design of the collector.

The Internet of Things (IoT) has gained much attention recently because of its prominent role in setting up smart products and technologies that utilize their intelligence. A Wireless Sensor Network (WSN), a vital part of an IoT system, comprises numerous tiny sensors that sense an object’s physical properties and transmit information hop by hop to a central device (sink node) through low-power and short-range transceivers. In many IoT applications, sensor nodes generate multiple data streams with varying demands for data generation or transmission rates, reliability and delay, resulting in a heterogeneous or diverse traffic load. Even though the Routing Protocol for Low Power and Lossy Networks (RPL), an IPv6 standard, is intended to meet the Quality of Service (QoS) needs of low-power IoT deployments, it exhibits poor load balancing in networks with diverse traffic loads. This research proposes Load and Congestion Aware RPL (LCARPL) that concentrates on enhancing the energy efficiency and reliability of these heterogeneous traffic loaded networks with nodes having different data generation or transmission rates, by utilizing load and packet delay metrics for determining the best parent node. Grey Relational Analysis (GRA) is used to select the best parent from potential candidates. The Cooja simulation results demonstrate a significant improvement in the performance of a heterogeneous traffic loaded network across packet delivery, energy usage, delay and control message overheads. Moreover, the work provides a comprehensive assessment of the RPL network using two standard objective functions by evaluating and comparing its performance under both homogeneous and heterogeneous conditions across varying numbers of IoT nodes.

Optimal Energy Management for Hybrid PV-Wind-Battery Microgrids through Markov Decision Processes Technique

Cardiovascular disease (CVD) continues to be a leading global cause of death and requires advanced models and personalized risk prediction algorithms. This research presents an adaptive deep learning framework that combines feature selection and signal fusion to improve individual level cardiac risk prediction. The adaptive deep learning model collects multimodal physiological signals, including Electrocardiogram (ECG), photoplethysmogram (PPG), and other bio-signals, to create an overall health profile for each patient. The feature selection component of the adaptive model enhances the model performance by reducing noise and dimensionality of the input, enhancing learning efficiency. To enhance data representativeness, a multi-signal or multi-channel fusion paradigm was applied, taking advantage of feature correlations across multiple signals to result in a more accurate and robust representation of cardiovascular health status. The proposed predictive architecture combines convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to efficiently analyse both spatial and temporal dependencies present in the signals. In testing using a public, benchmark dataset containing over 10,000 patient records, the model achieved an accuracy of 94.5%, precision of 93.8%, recall of 92.3%, and an F1 score of 93.0%. The entire diagnostic system enables remote monitoring and highly accurate and predictive results in real-time. The proposed research represents the first adaptive deep learning approach to signal fusion for robust and personalized CVD risk prediction, while addressing existing challenges within predictive health care systems.