مطالب

Purpose- The "Smart Village" approach includes long-term social, economic, and environmental activities of the rural community that involve widespread participation in local governance processes, promoting entrepreneurship, preserving the rural environment and, ultimately, sustainable development of rural areas. Therefore, the development of scenarios that facilitate the realization of the smart village approach in the peri-urban settlements of the metropolis of Tehran should be emphasized.Design/methodology/approach-This research is a descriptive-analytical study from the perspective of its objectives it is applied. Data and information were collected through library research, documentary studies, and survey methods (interviews). By reviewing scientific sources and conducting interviews with experts, 57 influencing factors of the smart village approach on the sustainability of peri-urban settlements were identified, categorized into 5 dimensions, and a total of 35 people, including managers of relevant organizations and university experts, were interviewed. The analysis of data was based on futures studies techniques, including structural analysis and mutual effects analysis (done using Micmac and Scenario Wizard software).Finding- The research findings show that there are a very large number of possible scenarios for the impact of the smart village on the sustainability of peri-urban settlements in the metropolis of Tehran within the range of Islamshahr County. Among them, 14 scenarios have weak compatibility and only 1 scenario is in a state of strong and sustainable compatibility (zero incompatibility). The first scenario, which is a positive one, has a mutual effect score of 733 and a compatibility value of 13, while the second scenario, which indicates unfavorable conditions for the future impact of the smart village on sustainable development, has a mutual effect score of -144 and a compatibility value of 5. The third scenario also has a compatibility value of -2 and with a mutual effect score of 63, it can be one of the impactful scenarios for the smart village on the sustainability of peri-urban settlements in the metropolis of Tehran. At the end of the research, operational suggestions are presented to enhance the indices of a smart village to achieve sustainable development in the rural settlements of the studied area.Originality/Value - For the first time in Iran, scenarios have been developed on the impact of smart villages on the sustainability of peri-urban settlements, and from this perspective, it is innovative and among the first research in the field.

Background: Autism Spectrum Quotient (AQ) functions as an evaluative instrument designed to assess traits associated with autism. Variances exist within the brain of individuals on the autism spectrum. Moreover, certain exercises have the potential to impact specific regions of the brain. Aim: This study aims to investigate how the program training of this study impact Autism—Spectrum Quotient. Materials and Methods: Forty-two children aged 7-10 years with ASD were recruited to participate in this study. These children were randomly divided into two groups: the control group (N=10, 7.80±1.35 years) and the experimental group (N=32, age: 8.16±1.16 years). The experimental group underwent an 8-week training program. The Autism Spectrum Quotient (ASQ) questionnaire was used to assess the participants' scores in the pretest and posttest. This questionnaire consists of five subscales: social skills, attention switching, attention to details, communication, and imagination, as well as a total score. Results: Based on the results, the program training had an impact on attention to detail (P=0.017) and social skills (P=0.057). However, there was no significant difference between the pretest and posttest scores for attention switching (P=0.096), communication (P=0.07), and imagination (P=0.161). Conclusion: Based on the results, we can conclude that the program had a small but significant effect in terms of quantity. Due to the specific and unique structure of the brain, it takes time to achieve a substantial impact.

Accurate traffic classification is important for various network activities such as accurate network management and proper resource utilization. Port-based approaches, deep packet inspection, and machine learning are widely used techniques for classifying and analyzing network traffic flows. Most classification methods are suitable for small-scale datasets and cannot achieve a high classification accuracy owing to their shallow learning structure and limited learning ability. The emergence of deep learning technology and software-driven networks has enabled the application of classification methods for processing large-scale data.In this study, a two-step classification method based on deep learning algorithms is presented, which can achieve high classification accuracy without manually selecting and extracting features. In the proposed method, an Autoencoder was used to extract features and remove unnecessary and redundant features. In the second step, the proposed method uses the features extracted by the autoencoder from a hybrid deep-learning model based on the CNN and LSTM algorithms to classify network traffic.To evaluate the proposed method, the results of the proposed two-stage hybrid method is compared with comparative algorithms including decision tree, Naïve Bayes, random forest. The proposed combined CNN+LSTM method obtains the best results by obtaining values of 0.997, 0.972, 0.959, and 0.964, respectively, for the evaluation criteria of, accuracy, precision, recall, and F1 score.The proposed method is a practical and operational method with high accuracy, which can be applied in the real world and used in the detection of security anomalies in networks using traffic classification and network data.