Purpose: The purpose of this applied research is to study the effectiveness of gamification on corporate Training.  Methodology: A gamified course was designed and implemented to train the location of Fire Hosing Cabinet for 24 employees of a firefighting maintenance company in Iran Mall shopping Center in Tehran. Using a quantitative quasi-experimental research plan (post-test only control group design) the participants of the study were randomly assigned to treatment (12) and control (12) groups and trained for a week.  Conclusion: The descriptive and interpretive result of the posttest analyses indicated the effectiveness of gamification of the Training performed for the employees of the firefighting maintenance company in Iran Mall shopping Center in Tehran. Moreover, the descriptive result of Gamification Acceptance Questionnaire answered by the members of the experimental group after gamified Training indicated that all the participants in the experimental group were satisfied with the gamified Training course.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic Algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic Algorithm and multi-objective optimization goals. The genetic Algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the Algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Fuzzy cognitive maps (FCMs) that are soft computing techniques, by combining fuzzy logic and neural network theory, have been known as a powerful tool for modeling complex systems. Utilization of different learning Algorithms to overcome the weaknesses of this model, is one of the active area of science. In this paper, a new hybrid Algorithm based on nonlinear Hebbian learning and real-coded genetic Algorithm is introduced, which operate in an entangled way and by improving the characteristics of each of these two Algorithms, can be applied in different decision-making models with high precision. The proposed model is implemented on a process control problem.

The maps of the space layout have been considered by the architects as one of the first steps of the architectural design process. The theoretical framework of the high-performance architecture emphasizes that the topological and geometrical structure of these maps is adopted from the latent concepts. These concepts were formed under the influence of the subjective and objective variables. According to the research hypothesis, the space layout maps are subject to the latent patterns that are the basis for their formation. Using the computational strength for contributing to predicting the space layouts has always been a controversial issue in contemporary architecture and has been the prospect for future architecture. The current paper used the data-driven artificial intelligence methods for generating the heat maps of the space layout. Despite the conventional methods that try to define the layout plans based on the absolute mathematical relations, the designed method tries to take the spatial layout generator function from the experience of designing successful patterns with a designed based approach. Therefore, a set of 300 plans of the apartments in Tehran has been provided, and four types of different inputs have been supplied for Training the artificial intelligence model. In the present research, cGan Algorithm was used as one of the most efficient Algorithms. This Algorithm creates artificial intelligence and has been trained based on the provided layout patterns. This Algorithm can regulate the mapping function to generate the target image based on the input image. After completing the process of Training the cGAN model, the heat maps of the space layouts of 10 new apartments were tested. Also, the quality of the predicted answers was evaluated based on the predetermined five regulations. The suggested model based on the design-based approach is following modern construction technologies, such as the application of metadata, deep learning, machine learning, efficiency and smart consumption of energy, and energy-view optimization.

The widespread use of smart devices on one hand, as well as the rise of computational and technical capabilities, including motion sensors, global positioning and powerful cameras, have made the location-based services more popular. One of the most utilizable of these services is location-based Training services, which has become a hot topic in the field of mobile Training in recent years. Since mobile learning can be done at anytime and anywhere, there are many benefits to incorporate real-world objects into educational content. Obviously, the augmented reality technology is very useful and functional. On the other hand, the issue of positioning is to be done correctly and accurately so that spatial and educational information is not accompanied by errors and mistakes. The purpose of this research is to provide an Algorithm for positioning in space-based, field-based learning based on augmented reality. In this regard, the library method analyzes some of the existing positioning systems and their challenges and benefits, and is presented with an Algorithmic approach to identify the user's position in the outer space during mobile learning.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the Multi-Objective Chaotic Particle Swarm Optimization was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the Multi-Objective Chaotic Particle Swarm Optimization for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.

One of the basic topics in hydrological and river engineering studies is flood routing.Flood flooding is common in multi-tributary rivers and rivers without intermediate basin statistics. Therefore, to achieve the determination of slopes and cross-sections in all sections of the river, the Muskingum hydrological model is a useful method that helps to save information on the depth and flow of the flood at any time by saving time and money. To specify. In this study, the nonlinear parameters of the new Muskingum model are optimized based on the fly Algorithm (MA). In this non-linear model of Muskingum, which has eight parameters, the recovery coefficient γ is used, which has more or less values ​​than the number of peaks discharged in the output hydrograph.To evaluate the performance of Muskingum's new nonlinear model with the new MA Algorithm, the Wilson and Weisman-Lewis case study has been used by many previous researchers for validation.The results of the MA Algorithm for Wilson and Weissman-Lewis rivers show the minimization of the residual squares (SSQ) as the objective function, which is 3.21 for the Wilson River and 68722 for the Weissman River. The results of this study showed that the proposed model has high accuracy in estimating the output discharge values.

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.

Objective: The present study was conducted with the aim of effective school-based self-control skill Training on increasing psychological hardiness and self-efficacy of male students of the first secondary level.Method: This research is a semi-experimental study with a pre-test-post-test design with a control group. The statistical population of the present study included male students of the first secondary school in Qom city, 30 students were selected using the available sampling method and divided into two groups of 15 people completely randomly. Students in the experimental group participated in 8 sessions of the school-based self-control Training program, and the control group did not receive Training. Before and after the implementation of the intervention, both groups were evaluated using the psychological hardiness scale of Lang and Goulet (2003) and the self-efficacy scale of children and adolescents (SEQ-C). The data was analyzed using spss-22 software and through repeated measures analysis of variance test.Findings: The results showed that school-based self-control skill Training has a significant effect on increasing students' psychological hardiness and self-efficacy (P<0.01). Conclusion: The findings of the present study indicated that school-based self-control skill Training can be an effective intervention in increasing psychological hardiness and self-efficacy in students.