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.

Nowadays, the fundamental role of having a purpose for life in physical and mental health has been confirmed. According to victor frankl, presence of a purpose in life gives life a meaning and increases resilience against pains and traumas. The importance of the purpose in life construct reveals the need for a reliable and valid tool to measure it. Crumbaugh and Maholick's purpose in life questionnaire is the first and one of the most applied tools for the assessment of life's purposefulness. The aim of this research is to determine the FACTOR structure of purpose in life questionnaire. The questionnaire was administered on 206 students who were selected through random stratified sampling at Ferdowsi University of Mashhad. Exploratory FACTOR analysis showed that there are two FACTORs "comprehension" and "purpose" and this finding were confirmed by confirmatory FACTOR analysis. Altogether results of this research showed FACTOR validity of the purpose in life questionnaire with a two FACTOR pattern

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.

This paper investigates the heat TRANSFERs and ablation of thermal insulators used in motors and nozzles. The heat and mass TRANSFER equations are considered in two dimensions in a convergent-divergent nozzle. The finite volume method and the implicit method for time dependence have been used to solve these equations. We used the equations finite volume method with implicit formulation for time dependency to solve equations. The reaction equation, which is written in the form of Arrhenius, is solved using the Runge-Kutta method, and the density and the flux of the gas produced at each step are obtained. Also, we represent a model for the rate of recession. The validation of the model is compared with the experimental results in silica-phenolic ablation insulators and shows a good agreement of the simulation results with the experimental observations. After validation of the obtained results. The effect of the blowing correction coefficient has been investigated, the results show that this coefficient is very important for the accurate design of ablation insulators. The blowing correction FACTOR reduces the convection heat TRANSFER coefficient, reduces the surface recession, and thus, reduces the back temperature of the insulation. Therefore, in cases where the thickness of the insulation is significant or the heat of ablation is low. This parameter can be important and affect the final results and thus the final design.

Background: This work investigated the TRANSFER FACTOR of radionuclides from clay loam soil to sesame and cowpea plants. Materials and Methods: Twenty samples from the plant and twenty samples from its soil were collected from five different locations (farms). Gamma-ray spectrometry was used to determine the activity concentration for the samples. In addition, the soil physicochemical characteristics such as pH value, the amount of organic content and texture of soil were investigated by pH meter, Walkley-Black and particle size distribution (Pipette) methods, respectively. Results: The average activity concentrations, respectively, of 226Ra, 232Th, and 40K were 12. 75, 10. 20 and 131. 75 Bq kg-1 for the clay loam soil, 5. 20, 4. 15 and 171. 00 Bq kg-1 for sesame and 6. 70, 5. 60 and 182. 90 Bq kg-1 for cowpea. The TRANSFER FACTOR from soil to sesame and cowpea was discussed. The average values of TRANSFER FACTOR were 0. 51, 0. 53 and 1. 36 (for cowpea) and 0. 42, 0. 43 and 1. 33 (for sesame), respectively for 226Ra, 232Th and 40K. The results showed that the TRANSFER FACTOR in cowpea is much greater than that in sesame. As a result of the ingestion of the radionuclides from the plants, the average annual dose was lower than the 290 μ Sv y-1 world average. Conclusion: Accordingly, the radiological risk due to the intake of the natural radionuclides in these plants was immaterial.