ali ghiasian

A persuasive strategy for advertisement on social mediaAbstractSocial media has been expanded and developed remarkably nowadays . Many users are active in social media every day so that these media considerably influence their audience. Social media applications installed on smartphones are developing and popular due to features such as easy access , attractiveness, interactivity, and high performance in terms of providing the information needed by users . In this work, we attempted to design a persuasive strategy to advertise on Instagram. It was applied exploratory-descriptive research conducted based on a cross-sectional design. Data were collected using qualitative and quantitative methods. The statistical population of the study included cyberspace experts and masters. Experts were first interviewed using questionnaires, and texts of the interviews were then encoded through content analysis in MAXQDA, and ultimately 58 indicators were selected. For quantitative analysis of the data obtained from the close-ended questionnaire , the sample size was determined 196 based on the study population and using Cochran's formula. The results were divided into 11 categories using the exploratory factor analysis method. The results confirmed a proper correlation between the study elements and items.

The main purpose of this study is to present an intellectual capital development model in network marketing using confirmatory factor analysis method and ISM technique. According to the general purpose of this research, this research is applied in terms of purpose and descriptive data collection method - The statistical population of this study includes 6900 marketers and activists in the field of network marketing in Tehran. In this study, according to Morgan's table, 367 people were randomly selected as the sample. The tool used in this study, the standard questionnaire for measuring intellectual capital BONTIS (2004) included 37 questions and three questions related to network marketing. Due to the standard nature of this questionnaire, only formal validity is sufficient. Cronbach's alpha method was used to calculate the reliability of the questionnaire. For the questionnaire performed on a sample of 30 people, Cronbach's reliability in the intellectual capital measurement questionnaire was calculated to be (0.899).The results of the confirmatory factor analysis test show that the main components of intellectual capital in network marketing include physical capital, human capital and structural capital. In addition, the results of the structural model show the most important components affecting The development of intellectual capital in network marketing is in a favorable position in the dimension of human capital with a path coefficient of 0.89 in the first place, structural capital with a path coefficient of 0.74 in the second place and finally physical capital with a path coefficient of 0.68 in the third place.

The Internet of Things (IoT) has provided a platform for different devices to interact extensively over the Internet. This technology produces a large amount of data that needs to be stored and processed in devices. On the other hand, hardware constraints on IoT devices pose a major challenge to energy consumption and latency. Edge Computing (EC) has brought many benefits as a promising solution to increase the computing and storage capacity of internet-connected devices. It is expected that EC helps IoT to reduce energy consumption and computational delay of devices. However, the conditions of the wireless channel and specifically the possibility of errors in data transmission over wireless links could have an adverse impact on the performance of the whole system. In this paper, by performing calculations in both local and edge environments and by considering the retransmission of data to eliminate possible errors, the role of wireless channels in the efficiency of offloading to the EC is investigated. The results show that offloading the calculations to EC does not always reduce energy consumption and delay. Therefore, the conditions of the wireless channel should be considered for an appropriate decision regarding the offloading of tasks to the EC or its execution in IoT devices.

This research is applied in terms of applied research, in terms of research method is exploratory-descriptive research, in terms of time horizon is cross-sectional, and in terms of data collection method includes a combination of qualitative and quantitative methods. The statistical population of this research consists of all cyberspace policy experts and experts, who were identified as 12 people. The steps were as follows: first, an interview was conducted in the form of a questionnaire from 12 experts, then the text of the interviews was coded openly, axially and selectively by content analysis method by maxqda software. Finally, 58 indicators were selected. Regarding the quantitative analysis of the data obtained from the closed questionnaire, the sample size was determined based on the study population and using Cochran's formula about 196 people. Then, by analyzing the exploratory factor by SPSS software, they were divided into 11 categories that the elements and items of the questionnaire are well correlated with each other, and finally all 58 indicators for this stage were approved. Which was tested using confirmatory factor analysis and path analysis. In order to refine and reliable the data, exploratory factor analysis of SPSS software and kmo test were used to ensure the adequacy of the sample size of the questionnaire and Bartlett sphericity test was used to test the correlation between variables. The structural model in the form of confirmatory factor analysis, path analysis, LISREL software has been used.

The significant impact of information and communication technology (ICT) on Greenhouse Gas emissions has caused a considerable research interest in energy management of network devices in recent years. As the amount of power consumption in routers and switches is a function of their working frequency, one approach to energy saving is to adapt working frequency of such devices dynamically and proportional to their traffic load. This paper describes this approach in Software Defined Networking (SDN) framework by proposing a Mixed Integer Linear Programming (MILP) formulation for a network of FPGA based interconnected Openflow switches. The prerequisite information of switch power profile is obtained by running experimental tests. The proposed method is applied to an infrastructural dataset extracted from the standard SNDlib database. Simulation results show that frequency scaling technique reduces power consumption by more than 37% in comparison to other well-known power-saving approaches.

WBANs (Wireless Body Area Network) are expected to consume very low electrical power. One of the most important factors of energy consumption in WBAN is the presence of interference between the transmitter and receiver nodes. In this paper, a fuzzy- genetic based power control method is proposed to intelligently align transmission power of sensor nodes within a WBAN. This technique utilizes the concepts of genetic algorithms to set up membership functions of fuzzy controller and its knowledge base so that by controlling the level of transmission power of each sensor, the entire network energy consumption is reduced by mitigating the impact of interference in neighborhood nodes. Compared to distributed Greedy power control algorithm, it is shown that the proposed genetic-fuzzy power control approach can reduce power consumption by at least 34 percent.

Electric Vehicle (EVs) usage causes to decrease the consumption of fossil fuel resources and pollution. If such EVs include vehicle to grid (V2G) capability, then a smart scheduling can be used in order to obtain more profit. The goal of this paper is to present a charge and discharge scheduling to maximize the profit of EV owner, using genetic algorithm (GA). The suggested method can be applicable without deteriorating the normal usage of EV. Besides that, in this method the hourly traveled distance and state of charge (SOC) of EV are considered in each hour of day and night. To evaluate the accuracy of this algorithm, stochastic data are generated and the algorithm is repeated 1000 times. Finally, the expected profit of EV owner is calculated for 3 modes named without V2G ability, with V2G and 72 ampere charger and with V2G and supercharger. The results show that the 3rd mode is more profitable than the other ones.

Advanced Encryption Standard (AES) is one of the most common standard encryption algorithms. Inspired by its characteristics, AES algorithm can be implemented on various hardware platforms such as FPGA. Also, the data path can be implemented in either loop-unrolling or rolling architecture. These two architectures have direct impact on the amount of area consumption on the chip as well as system throughput. Then, a smart design should be able to consider the trade-off between area and throughput and provide a good balance between these two conflicting factors. In this paper, we propose such a design to represent the area-throughput trade-off for FPGA implementation of the AES algorithm. With loop unrolling and pipelining techniques, throughput of 71.35 Gbps is achievable in Virtex 7 FPGA (xc7v585t-3ff1157). This design has just used 3669 Slices on the chip. The extracted results from the Place & Route report of Xilinx ISE 14.2 indicates that the maximum attainable clock frequency is 570.776 MHz.

One of the most effective techniques for energy management in wireless sensor networks is clustering. How to establish clusters and the method used to choose cluster heads are the most important factors in creating optimal clusters. In fact, appropriate cluster head selection results in longer network lifetime and more data delivery to the sink. In this paper, after exploring clustering algorithms based on computational intelligence techniques and evaluating the strengths and weaknesses of each, we provide two novel clustering schemes termed as Energy Aware Genetic Clustering Algorithm (EAGCA) and EAGCA*. In these centralized algorithms, we try to achieve an optimal clustering by using global information such as number of neighbor nodes, the energy of neighbor nodes, local distance and local traffic load on each node. Compared to some well-known clustering algorithms such as LEACH and EAERP, simulation results demonstrate that EAGCA and EAGCA* are able to create more appropriate clusters in terms of energy consumption, life time and total number of received data to the sink.