Neural network models have been successfully applied to solve a variety of problems requiring associative recall, constraint satisfaction, and optimization. This paper presents a new scheduling approach based upon a deterministic modified Hopfield model to solve "File-Transfer" scheduling, an NP-Complete constraint satisfaction problem. The proposed model is mapped onto 2-dimensional neural network architecture for the Transfer scheduling of Files between various nodes of a network, by which the overall Transfer times is to be minimized. Neural Network-based Scheduling is achieved by formulating the scheduling problem in terms of energy function, and by using the "Motion Equation" corresponding to the variation of energy levels. The main contribution of this work is an efficient and fast parallel algorithm under time and resource constraints, appropriate for implementation on the parallel machines. However, neurons' motion equation is the core of this guided movement mechanism which searches the scheduling space in parallel, and guarantees that the state of system mostly converges to the optimum state. Yet another important contribution of this work is the new strategy of constraints and variables containments by which the performance and efficacy of the system was considerably improved.

Textbooks play a pivotal role in language learning classrooms. The problem is that among a wide range of textbooks in market which is appropriate for a specific classroom and a group of learners. In order to evaluate ELT textbooks theorists and writers have offered different kinds of evaluative frameworks based on a number of principles and criteria. This study evaluates a series of ELT textbook, namely, American English File by the use of Littlejohn’s (1998) evaluative framework to see what explicit features of the book are, what pedagogic values it has, whether it is in line with its claimed objectives, and what its merits and demerits are. Littlejohn believes that we should evaluate a textbook based on its own pedagogic values and we should see what is in it not what teacher and evaluators think must exist in it. Consequently his framework is claimed to be devoid of any impressionistic ideas and it is in-depth and objective rather than being subjective. Nine ELT experts and ten ELT teachers helped the researcher rate the evaluative checklists. The results of the study show that although a number of shortcomings and drawbacks were found in American English File, it stood up reasonably well to a detailed and in-depth analysis and that its pedagogic values and positive attributes far out-weighed its shortcomings. The internal consistency between ratings was computed via the statistical tool of Cronbach’s alpha that indicated a desirable inter-rater reliability.

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.

Background: GnRH agonist and antagonist were developed to control the premature release of LH surge. There is some difference between two Protocols.Objective: We compared the outcome of frozen-thawed embryo Transfer in infertile women who used GnRH agonist or antagonist Protocol for previous COH cycle and evaluation of any adverse effect of GnRH antagonist on oocyte and embryo.Materials and Methods: The study group included all infertile women who referred to Yazd Research and Clinical Center for Infertility. Overall 20-35 years old women who were candidate for frozen-thawed embryo Transfer with regard to inclusion and exclusion criteria were participated in the study. The patients based on previous control ovarian stimulation (COH) Protocol divided in to two groups: GnRH agonist long Protocol (n=165) and GnRH antagonist multiple dose Protocol (n=165). Frozen-thawed embryos were Transferred after endometrial preparation in both groups. Main outcome measures were: implantation, chemical and clinical pregnancy rate.Results: The implantation and clinical pregnancy rate following cryopreserved embryo Transfer in GnRH agonist group and antagonist group were 16.3% vs. 15.7% (p=0.806) and 38.1% (63.165) vs.36.9% (61.165) (p=0.915) and chemical pregnancy rate was 44.8% (74.165) vs.43.6% (72.165) (p=0.915) respectively.Conclusion: There was no statistically difference between two groups in terms of implantation and pregnancy rate. Although pregnancy rate in fresh embryo Transfer in antagonist cycles was lower than agonist groups, Therefore decrease in these parameters might be due to detrimental effect of GnRH antagonist on the endometrium, not embryo or oocyte.