Postmodernism represents skepticism toward metanarratives AND universal ideologies that dominated the modernist era. The study focuses on three key postmodernist concepts - alternative assessment, process syllabi, AND self-directed LEARNING. Alternative assessment emphasizes evaluating the LEARNING process over summative testing. Process syllabi focus on the LEARNING experience rather than rigid outcomes. Self-directed LEARNING enables student responsibility in setting LEARNING goals AND processes. This paper examined the effects of postmodernist educational concepts on improving Iranian EFL learners’ autonomy. A quasi-experimental pre-test/post-test design was utilized with 60 intermediate level Iranian EFL students divided into an experimental AND control GROUP (n=30 each). The experimental GROUP received instruction utilizing the three postmodernist concepts over 14 weeks, while the control GROUP received traditional modernist instruction. Autonomy was measured using a validated questionnaire before AND after the intervention. The results showed the experimental GROUP demonstrated significant increases in autonomy compared to the control GROUP. Paired sample t-tests revealed significant differences between pre-test AND post-test autonomy for the experimental GROUP across all three postmodern concepts - alternative assessment, process syllabi, AND self-directed LEARNING. This suggests postmodernist concepts that decentralize instruction AND emphasize student process over outcomes can enhance Iranian EFL learners’ self-direction AND responsibility for language acquisition. The study implies EFL syllabus designers AND instruction should transition to postmodern models centered on INDIVIDUALized assessment, flexible syllabi, AND student-guided LEARNING to boost autonomy. Further research can expAND sample sizes AND explore additional postmodernist concepts across diverse demographics.

Many books have been written to illustrate the position of the new technologies in teaching AND LEARNING a foreign or second language (L2); nonetheless, most of these books have not been successful. The HANDbook of Technology AND Second Language Teaching AND LEARNING edited by Caral A. Chapell AND Shanon Sauro AND published by Wiley Blackwell in 2017, provides a comprehensive overview of the various facets of the use of technology in teaching AND LEARNING an L2. This book has not been translated into Persian AND no critical review has been published for it; accordingly, this study sought to both introduce AND critically review this book. The current critical review revealed some advantages including comprehensiveness of the whole book, the expertise of the writers, up-to-dateness, content AND format integrity, AND the use of valid sources. The main criticisms are the exclusion of some of the very recent technologies, perhaps due to the ever-changing AND exponentially volatile nature of the technological improvements AND expansions, the introduction of too many technologies at the theoretical level without providing information about their practical use. Generally, the book is an invaluable AND academically written source for L2 practitioners, postgraduate students, AND researchers.

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.

Introduction: Employee LEARNING AND development are as an important key in the dynamics AND effectiveness of any organization. Nurses as a great source of organizational knowledge AND knowledge transfer can play a major role in the process of organizational LEARNING. That would be facilitate by nurse managers.Aim: This study was done for assessing relationship between levels of GROUP AND INDIVIDUAL LEARNING in clinical nurses' organizational LEARNING in Farabi Hospital in Tehran.Methods AND Materials: This was descriptive-analytical study that was done in (2013-2014) in Farabi hospital in Tehran. AND all of clinical nurses (250) participated. Data gathered by stANDard questioner that had two domains: INDIVIDUAL AND GROUP LEARNING levels. Its reliability was (0.911) for INDIVIDUAL domain AND (0.904) for GROUP domain that estimated by a-Choronbach. All of data analyzed in SPSS19 by statistical descriptive tests.Results: Findings showed that levels of two LEARNING domains in nursing staff were average. So, the mean capability in INDIVIDUAL LEARNING was (65.78±11.78) AND in GROUP LEARNING was (43.56±8.12). Also, two domains in organizational LEARNING had direct correlation (r=0.556) based on Pearson correlation AND linear regression showed that 31% LEARNING in INDIVIDUAL domain explained by GROUP LEARNING (R Square=0.31).Conclusion: Nurse managers would be developed organization LEARNING by developing GROUP domain LEARNING; because it could be enhanced INDIVIDUAL LEARNING domain. So, it is suggested to them implement all of the in- service education by GROUP strategies AND develop these methods.

In this study, leaming intensity is measured in terms of labor cost savings AND productivity growth through experience, as well as the effects of mergers  AND acquisitions on technology LEARNING using a LEARNING curve AND its effects on the performance of oil, gas AND products companies. Oil listed on the Tehran Stock Exchange will be evaluated.The novelty of this article is to explain the direct AND indirect effects of M&A (mergers  AND acquisitions) in technology LEARNING that leads to the LEARNING curve model with the M&A factor in the oil, gas AND petroleum products It can be seen that traditional LEARNING through experience has not reduced costs by increasing cumulative production in refineries AND petrochemical companies. Economies of scale have also failed to reduce operating costs by increasing production at refineries. LEARNING by research in refining AND petrochemical companies has also reduced operating costs by a year, but is less severe. This study also shows that there is no direct effect of mergers AND acquisitions on technology LEARNING in the combination of refining AND petrochemical companies. In addition, the indirect effects of mergers AND acquisitions on technology LEARNING are seen only in petrochemical companies.