Nanofluid is a new class of heat transfer fluids engineered by dispersing metallic or non-metallic nanoparticles with a typical size of less than 100 nm in the conventional heat transfer fluids. This article aims to investigate the overall and convection heat transfer coefficient and Nusselt number of Al2o3-water nanofluid flowing in a horizontal double pipe heat exchanger under turbulent flow (14000 £Re£ 26500) conditions. Al2o3 nanoparticles with diameter of 20 nm dispersed in Deionized water with volume concentrations of 0.1% £ j £ 0.3% vol. are used as the test fluid. The results show that the overall and convection heat transfer coefficient and Nusselt number of nanofluid were approximately 15% -21% greater than that of pure fluid. Additionally, the heat transfer coefficient and Nusselt number increase with an increase in flow rate, Reynolds number, nanoparticle concentration and nanofluid temperature. Finally, the new correlations were proposed for predicting the Nusselt number of the nanofluids, especially. Employing particles of nanometer dimension suspended in solution as nanofluid shows considerable increase in the nanofluid thermal conductivity and heat transfer coefficient which result in increasing heat transfer and decreasing operational cost.

The aim is to study nonlinear wave dynamics in solar spicules and jets. The life of jets in the context of Alfven wave dynamics is focused. Here, further insight into the solar atmospheric effects together with initial conditions on the dynamics of Alfven waves along with the characteristic parameters of the spicule or jet itself are provided. Results are based on the theory of magnetohydrodynamics. the location of shock formation by the interplay of the internal and external plasma-beta conditions together with the initial steady flow speeds which are rooted in the initiation location of the solar jet are illustrated. It was known that the plasma-beta of a solar jet affects the shock formation time of torsional Alfven waves. However, its efficiency is shown to be dependent on the external plasma beta conditions. The shock formation time for plasma-beta conditions over unity is directly proportional to the plasma-beta, similar to plasma-beta conditions equal to or below unity. In the case where the plasma-beta inside the magnetic structure is small, the shock formation time is accelerated by increasing the external plasma-beta. In photospheric conditions, as for coronal conditions, the time of shock formation is inversely proportional to the external plasma-beta. When the internal plasma-beta is fixed, for various steady flow speeds, the external plasma-beta accelerates the formation of shocks. These results help us to better understand the role of Alfven waves in solar jets in the transfer of energy to the solar system.

Face recognition from digital images is used for surveillance and authentication in cities, organizations, and personal devices. Internet of Things (IoT)-powered face recognition systems use multiple sensors and one or more servers to process data. All sensor data from initial methods was sent to the central server for processing, raising concerns about sensitive data disclosure. The main concern was that all data from all sectors that could contain confidential information was placed in a central server. Federated learning can solve this problem by using several local model training servers for each region and a central aggregation server to form a global model in IoT networks. This article presents a novel approach to optimize data transfer and convergence time in federated learning for a face recognition task using Non-dominated Sorting Genetic Algorithm II (NSGA II). The aim of the study is to balance the trade-off between training time and model accuracy in a federated learning environment. The results demonstrate the effectiveness of the proposed approach in reducing data transfer and convergence time, leading to improved performance in face recognition accuracy. This research provides insights for researchers and practitioners to enhance the efficiency of federated learning in real-world applications.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

In this paper, we investigate the vibrational behavior of a double-beam doubly clamped beam subjected to the harmonic external load with different amplitudes using a passive absorber. This system actually provides a simple and localized model of marine structures stimulated by external fluid. For modeling of the beam, the Euler-Bernoulli theory and for modeling of nonlinear energy sink, nonlinear springs and linear dampers have been used. The system response is obtained by two analytical methods (complexification averaging method) and numerical (fourth order Rang-Kuta method) and accordingly, the proper range of system and absorber’ s parameters are extracted to optimally reduce vibrations. In addition, the effects of system damping, the effect of higher modes, quasi-periodic response regions and instability conditions on the relative displacement and center of mass of the system response are investigated. The results showed that the load thresholds for the occurrence of various phenomena such as the detached resonance frequency region and the Hopf and saddle node bifurcations, the probability of occurrence of the jump phenomenon in the system and the nonlinear adsorbent efficiency will change with the change of the adsorbent location.

In this paper, we address the challenges of energy and throughput management in a Wireless Body Area Network (WBAN) with a focus on a heart rate sensor. Our approach utilizes the sleep and wake-up method to minimize sensor energy consumption while harnessing Radio Frequency (RF) waves and human activities (running, walking, and relaxing) as Energy Harvesting (EH) sources to supplement battery power. Bluetooth Low Energy 5 (BLE5) technology is employed for wireless information and energy transfer. Our goal is to strike a balance between throughput and battery residual energy. The advantages of using 𝒬-learning for action selection in comparison to Random Action (RA) selection are demonstrated through simulations. The results reveal that the reward function in 𝒬-learning, incorporating a balancing parameter, effectively achieves a compromise between throughput and battery residual energy. Additionally, our 𝒬-learning method improves system throughput by 43% compared to RA selection. In addition, we compare the performance of the 𝒬-learning and State- Action- Reward- State- Action (SARSA) algorithms using the same reward function to evaluate their respective abilities in managing system throughput and battery residual energy. These findings have significant implications for developing energy-efficient WBANs, enabling prolonged operation and reliable data transmission.

This research focuses on the effect of passive methods in the field of energy storage, produce energy by using renewable energies, and also to achieve the construction patterns of residential buildings in the climate conditions of Shiraz, with the aim reducing energy consumption. Statistics show that in Iran, fuel consumption in residential and commercial buildings with an average annual growth of 7. It is about 2.37% of the country's total fuel consumption. One way to minimize energy consumption in this sector is to increase the role of zero-energy buildings. Zero-energy buildings provide technical ways to consume less energy in buildings. These buildings are designed and built so that all or a large part of these provide the energy they need through the combination of energy saving and the use of renewable energy technology. In this research, after studying the climate condition of the city of Shiraz, a building in the Energy Plus software, the simulation and climatic data of the Shiraz station was introduced to the software. To achieve the most optimal conditions of energy consumption in the climatic conditions of Shiraz, each of the building elements Residential was optimized. The simulation shows that it is possible to reduce the energy consumption of the building by using passive building materials and designs, including double-glazed glass, Wall insulation, etc. On the other hand, by using solar energy, using pvsyst software, you can supply the energy needed by the building.