درخت حوزه‌های تخصصی

Objective:  This article focusses on emotional atmosphere and family structure among suicidal attempt among 20 to 13year-old clients referred to hospitals in Hormozgan Province. Methods: The sample consisted of 146 male and female adolescents who attempted to suicide. They were selected by random sampling method. In this research, the Hill Burn family questionnaire, Fellowship Suicide Factor Questionnaire (ASQ) and family structure questionnaire (FAD) were used to collect data. To analyze the data, Pearson correlation coefficient and entered regression were used. SPSS-20 statistical software was used for data analysis. Results: The results of the analysis showed that there is a negative relationship between the family's emotional atmosphere and its subscales with suicide attempt and also there is a negative relationship between family structure and its dimensions with suicide attempt. Conclusion: The results showed that there is a negative relationship between family psycho-emotional atmosphere and family structure with suicide attempt. The results of regression coefficients analysis showed that family emotional atmosphere and family structure were significantly associated with suicide attempt and could predict clients' suicide attempt (R 2 = 56, N = 146, P ≤ 0.001).

The growth and complexity of society justify the need for relevant economic information, information systems, and information-generating processes, and the need for auditing as part of the information reporting process increases. This has affected the professional functions of auditors. The purpose of this research is to choose the influential dimension of mindfulness of auditors' professional judgments based on social pressure analysis based on Rough Theory. The methodology of this research is mixed and it has been used by Meta-synthesis, Delphi and Rough Theory. The target population was the qualitative, similar research and academic experts in the field of accounting. However, the target population in a small number of 19 audit partners had more than 5 years of work experience, which is acceptable from the statistical population due to the requirement of Ruff theory analysis. In this study, based on the Meta-synthesis analysis of selected researches, 4 propositions of pressures based on social compliance and 3 components of auditors' professional judgment were determined. The results in this section show that the most effective proposition of social compliance pressures was the market pressure proposition, which affects the inferential consciousness of auditors in their professional judgment and violates the auditors' mental functions such as skepticism and objectivity.

This paper aims to show the asymmetric effect of oil shocks on Iran’s economy. It uses nonlinear time series models to investigate the asymmetric effect of oil shocks on resource allocation in Iran’s economy. The results show that adverse oil shocks have been more persistent during the last decades and severely negatively affect resource allocation in Iran’s economy. Different oil shocks have different implications for importing and exporting countries, and the rigidity of state fiscal systems in exporting countries causes adverse oil shocks to be more persistent. The oil economy’s response to positive and negative oil shocks depends on the structure of the economy. The government budget and trade balance have significant implications for the effects of oil shocks on oil-exporting economies. The government budget is highly dependent on oil revenues, so in the case of adverse oil shocks, the pass-through exchange rate will cause high inflation because of foreign exchange shortage and overshoot in the exchange rate. 

The tremendous advances in artificial intelligence over the past decade have led to their increasing use in financial markets. In recent years a large number of investment companies and hedge funds have been implementing algorithmic and automated trading on their trading. The speed of decision-making and execution is the most important factor in the success of institutional and individual investors in capital markets. Algorithmic trading using machine learning methods has been able to improve the performance of investors by finding investment opportunities as well as time entry and exit of trading. The purpose of this study is to achieve a better portfolio performance by designing an intelligent and fully automated trading system that investors with the support of this system, in addition to finding the best opportunities in the market, can allocate resources optimally. The present study consists of four separate steps. Respectively, tuning the parameters of technical indicators, detecting the current market regime (trending or non-trending), issuing a definite signal (buy, sell or hold) from the indicators’ signals and finally portfolio rebalancing. These 4 steps respectively are performed using genetic algorithm, fuzzy logic, artificial neural network and conventional portfolio optimization model. The results show the complete superiority of the proposed model in achieving higher returns and less risk compared to the performance of the TEDPIX and other mutual funds in the same period.

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.

From the recent literature review, it is evident that existing agile methodology lacks the method to evaluate the requirement understanding of agile team members for a given set of requirement chosen for agile software development. Hence, there is a need to introduce a requirement understanding check to ensure every agile team member follows the given requirement clearly without any ambiguity. To fill this existing gap, this research paper proposes to extend the usage of story cards to evaluate the understanding of the given requirement and to highlight any challenges and risks in the early stage of requirement understanding under agile software development methodology, if any. This paper primarily focuses to introduce a robust requirement understanding evaluation process in agile methodology. The research results were found to be motivating and were analyzed by comparing the data-points using time-series for performing agile query analysis, agile team velocity analysis and agile team involvement analysis for two agile teams where one team delivered the sprint output using agile traditional method while another team opted for proposed approach. A considerable decrease of 33.07% was observed in the number of queried raised and a significant increase of 26.36% in agile velocity was observed for agile sprint under proposed approach when compared to agile traditional approach. Also, a significant shift from 40%-80% team involvement under traditional agile method was uplifted to 80%-90% team involvement under proposed approach.

The epidemic situation generated as a result of COVID -19 crossways the sphere observed the practices of various emerging technology like Internet of Thing (IoT) along with norm of dynamic fields. The wireless communication  based on networks such as wireless mesh networks (WMN) and Mobile Ad-hoc networks (MANETS) proven to be very successful for monitoring of patients remotely. The MANET protocols that are simulated in this study are  Ad-hoc On Demand Vector (AODV), Secure AODV (SAODV) and Hybrid Wireless Mesh Protocol (HWMP). In this investigation work, most appropriate routing protocols to knob DDoS attacks are simulated using NS-2 and assessed in terms of average energy consumption in the state of changing speed connections among devices called mesh nodes. Further ANOVA test is utilized for further accessing for the best suited routing protocol for handling the data packets, which is HWMP , considerable less susceptible for DDoS assaults dominant in healthcare field.