درخت حوزه‌های تخصصی

Wireless sensor networks (WSNs) are the sensor nodes that are generally interconnected and these nodes communicate wirelessly to collect the data from the environment around them. Sensor nodes are low-powered and distributed in a decentralized ADHOC manner. WSNs setup comes with some limitations such as energy constraints and the cooperative demands essential to perform multi-hop geographic routing for Internet of things (IoT) applications. Quality of Service (QoS) depends to a great extent on offering participating nodes an incentive for collaborating. The real-time applications of WSNs have potential design limitations such as energy and other QoS constraints in the context of IoT. This work formulates two different re-transmission strategies for optimized cross-layer design and also integrates the framework with a swarm intelligence technique assisted Bacteria Foraging Optimization (BFO) for efficient Cluster Head (CH) selection. The proposed work designs analytical modelling to realize the formulated concept and also simulates the computational design in a numerical computing tool. Finally, an extensive simulation is carried out using the MATLAB simulation tool. The obtained results show the effectiveness of the proposed system in terms of delay is reduced by 12%, throughput is increased to 10%, remaining energy of the network is increased to 11%, and packet delivery ratio of the proposed work in increased to 7-9% in comparison with the existing system.

As one of the richest intellectual domains of mankind, Islamic culture has played a prominent role in the formation of diverse dimensions of Islamic community. In this regard, the rich Islamic art, which is intermixed with the elegance of Islamic architecture, is regarded as one of the aspects of Towhid (belief in the oneness of God) and divine worldview. Since the mankind is not a single-dimensional creature, and is innately inclined to beauty and innovation, architects have sought to create an original work imbued with spiritual meanings and themes within a material realm through reliance on their experience, thoughts, individual creativity as well as an aesthetic spirit on one hand, and the manifestation of spirituality and other notions on the other. The contemporary human, by setting out on journey on the earth, has made an attempt to seek knowledge and awareness, and Islamic architecture is considered as an epiphany of human nature as well as a manifestation of contemplation and existence in the depth of human spirit, which is believed to be one of the most significant elements of tourism. The present research is aimed at exploring the conceptual symbols in Islamic architecture originating from Islamic worldview, as the rich Islamic principles have inspired Muslim architects, whose thoughts and environment have been influenced by Islamic worldview. The present article has been written adopting a descriptive-analytical method. The results indicate that architectural elements in Iran have not been created without taking Islamic ideology and concepts into account, but rather all the elements applied are endowed with spirituality and divine symbols.

Predicting financial markets has always been one of the most challenging issues, attracting the attention of many investors and researchers. In this regard, deep learning methods have been used a lot recently. Due to the desired results, such networks are always in development and progress. One of the networks that is being implemented in various fields is capsule network. The first time the classification capsule network was introduced, it was able to attract a lot of attention with its success on MNIST data 1 . In such networks, as in the other ones, the parameters are obtained by minimizing a loss function. In this paper, we first change the classification capsule network to a regression capsule network by modifying the last layer of the network. Then we use different information measures such as Kullnack-Leibler, Lin-Wang and Triangular information measures as a loss function, and compare their results with wellknown models including Artificial Neural Network (ANN), Convolutional Network (CNN) and Long Short-Term Memory (LSTM) as well as common used loss functions such as Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Using appropriate accuracy metrics, it is shown that the capsule network using triangular information measure is well able to predict the price of bitcoin for the medium and long term period including 10, 90 and 180 days.

Due to the undeniable role of tourism in the development process of the country, managers of the tourism industry are always faced with the challenge of attracting tourists and gaining their satisfaction and loyalty. The image of the destination is one of the components that has a great impact on the tourist's decision and the behavioral consequences of his trip. The purpose of this study was to investigate the effect of cultural events and mental conflict on the image and the intention to visit the destination among tourists in Sarab-e Meymeh. The statistical population of this study consisted of tourists in Sarab-e Meymeh township of Dehloran City in the spring of 1398, which due to the unknown of the number of tourists in the region, the sample size was selected 385 people, which is the highest sample size. The present study is operational in terms of purpose and with a survey nature of correlation is based on structural equations. The required data was collected using a questionnaire tool. The questionnaire was acceptable in terms of validity and reliability. The results of data analysis using structural equation method with Smart Pls software showed that cultural events and mental conflicts have a positive and significant effect on the image and intention for visiting the destination among tourists in Sarab Meymeh. In fact, a good general image and the intention for visiting again the Sarab-e Meymeh township requires attention to the brand of the event, structural components, cultural events, brand of the place, tourist mental engagement, general image, cognitive image and emotional image of the destination. Tourism managers and marketers must pay special attention to these fundamental and important factors.

Media literacy is a topic that is concerned with the audience's power of understanding of the way in which media works and how they make sense thereof, and seeks to make this analysis a habit and a task for the audience. In this regard, present research provides a model for promotion of public relations with an emphasis on media literacy among managers and employees of Mazandaran Gas Company. In this paper, the data were sorted out through three stages after introduction of the concept media literacy using the qualitative method of grounded theory and holding 15 in-depth, semi-structured interviews with the academics and experts of the field, and following a content analysis. Based on the findings, the model is made up of five components: 1. The elements promoting media literacy at three macro (i.e. formulation of educational rubrics, changing educational methods, culture building with media literacy potential, adopting a problem-oriented approach, creating a national Internet, coherence and consistency in policy making and planning, and media content monitoring), intermediate (i.e. group interaction with colleagues and management training), and micro (i.e. technological knowledge, cognitive knowledge, media knowledge, and content production); 2. Decisive factors (categories of macro, intermediate, and macro level factors); 3. Interfering factors (including government intervention, lack of legislation or regulations); 4. Strategies (including policy and planning strategies, educational strategies, and culture- and discourse building strategies; 5. Consequences (i.e. economic, social, cultural, political, technological, communication, environmental, and individual-behavioral consequences).