مطالب

In modern times many militant atheist thinkers and activists have tried to promote the idea that religions, as well as religious ways of life, are one of the main, if not the main source of evil in the social arena. Some other non-believer scholars, while taking a respectful approach towards religions and religious people, maintaining that it is more rational for people and communities to adopt a non-religious outlook on life and become members of the community of non-believers. In this paper, I do not take issue with the militant non-believers. The reason is that their approach to religion is so ideologically driven that it leaves not much room for proper rational discussions; it only invites some polemical replies. My aim here is, instead, to enter into a dialogue with those non-believer scholars who view religion in a measured and rational way. My intention is to critically assess the claims of this latter group of scholars and explain why, contrary to what they suggest, certain interpretations of religion, and in particular, the Islamic faith, can provide them with better alternatives than their atheist outlooks. The arguments of this paper are mostly in reply to the views expressed by Professor Shearmur in his paper on critical rationalism and religion.

The application of machine learning technologies for cancer detection purposes are rising due to their ever-increasing accuracy. Melanoma is one of the most common types of skin cancer. Detection of melanoma in the early stages can significantly prevent illness and fetal death. The application of innovative machine learning technology is highly relevant and valuable due to medical practitioners' difficulty in early-stage diagnoses. This paper provides an open-source tutorial on the performance of an algorithm that helps to diagnose melanoma by extracting features from dermatoscopic images and their classification. First, we used a Dull-Razor preprocessing method to remove extra details such as hair. Next, histogram adjustments and lighting thresholds were used to increase the contrast and select lesion boundaries. After using a threshold, a binary-classified version of image was obtained, and the boundary of the lesion was determined. As a result, the features from skin tissue were extracted. Finally, a comparative study was conducted between three methods which are Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results show that ANN could achieve better accuracy (83.5%). In order to mitigate the biases in existing studies, the source code of this research is available at hadi-naghavipour.com/ml to serve aspiring researchers for improvement, correction and learning and provide a guideline for technology manager practitioners.

In the 21st century, the meaning of places is essential to people. Meanings have gained symbolic importance through tourism heritage. Tourism is a powerful force  in shaping the world and our understanding of different cultures, people, and  places. This research investigates the complexities of religious tourism and the  management of religious destinations. It obtains criteria for managing religious  goals with a detailed look and uses the descriptive-analytical method based on  library documents and the opinion of research experts. Then, using research experts  and ISM-PLS hybrid approach, a three-level framework for managing religious  goals in Yazd city has been presented and analyzed. The results showed that the  government's decisions and actions were the most effective criteria. The ultimate  goal was to create a sense of trust and security among religious tourists. It is hoped  that the findings of this research will be a starting point for managing religious  tourism destinations and a guide for examining the complexities of Islamic tourism  destinations and their management.

Nowadays, customer satisfaction is regarded as a strategic and influential factor for the success of various businesses. On the other hand, tourism, the world's largest and most diverse industry, is also a significant source of income and job creation for many countries. Therefore, the present study examines the effect of nostalgia, experiential marketing, and experiential values as relatively novel concepts on satisfaction in the tourism industry. In light of the COVID-19 outbreak, the research population includes all tourists who have previously visited the Kashan Rosewater Festival. Furthermore, since the population size is uncertain, it is assumed unlimited. A convenience sampling method was used to select sample members from the population. Methodologically, it is a descriptive survey study based on the subject matter under investigation. It is also a correlational study. A partial least squares structural equation modeling (PLS-SEM) technique was used to analyze the questionnaire results. Based on our findings, nostalgia affects pull motivation, patronage behavior, and future travel intention. However, pull motivation does not influence travel intentions.

This study was conducted to determine the stock portfolio with the best return and low-risk investments using momentum-contrarian strategies (MCSs). The momentum-Contrarian strategy is one of the well-known models to construct the portfolio which suggests buying the stocks with the best performance (the winner stocks) and selling the stocks with the worst performance (the loser stocks). The optimal values of the portfolio's objective function and the weight of all assets in the portfolio that are not necessarily the same are calculated by defining a nonlinear multivariate optimization model combined with momentum-contrarian strategies (MCSs). The return information of companies listed on the Tehran Stock Exchange from 2014 to 2019 was used to select the best optimal portfolio. The results confirmed the stability in the profitability of the contrarian optimal portfolio with minimum risk compared to other optimal portfolios. Furthermore, through MATLAB software the optimal weight of assets in the optimal portfolio is calculated based on statistical data.

This research, in line with the strategy of the World Customs Organization, order to provide a structural model for improving the ethics and administrative health of employees based on the use of information technology in customs affairs with a combined approach of fundamental theory and interpretive structural modeling to understand how information technology affects ethics and Administrative health and investigating the power of influence and the degree of dependence of the variables of ethics and administrative health of employees in the form of drawing a comprehensive structuring model. In the implementation of this research, a mixed method was used in the qualitative part of the foundation's data theory according to the research needs and structured interviews were used to collect data. To collect data in a qualitative method, with the help of Maxqda software, research findings through the opinions of 13 experts in three stages of open-centered and selective coding in the form of 6 categories affecting the ethics and administrative health of employees in the form of the Strauss paradigm model and Corbin and it was expressed theoretically. After building the theory, in the quantitative stage, interpretive structural modeling has been used in order to understand the relationship between the variables and the leveling of the promotion of ethics and administrative health of Iran Customs. The results of the research show that the variable "use of smart technologies" is the first level variable in the promotion of ethics and health, and the variables "Analysis of the situation of manpower and customs processes, ethical characteristics of employees and laws and management of information technology and human resources" » is the second level variable. And the variables of "conditions and structure of the organization and the requirements and infrastructure of information technology" are the variables of the third level.