درخت حوزه‌های تخصصی

Mystical tourism could have a significant role in the development of tourism industry. It is also possible to achieve a significant contribution by investing in the pristine sector of mystical tourism and create an authentic Iranian mystical cultural space. Considering the characteristic of a mystical tourist, which is generally spiritual and cognitive issues, gaining inner peace and the meaning of existence, it should be taken into consideration in branding a mystical destination. For this reason, this research tries to investigate The effect of visiting the mystical destination brand on love and fascination with the destination brand. In addition, the cultural destination of tourism can be created with a strong relationship between tourism and culture. Attarnishaburi tomb is one of the potential tourist areas that has the ability to attract tourists at the national and international level with unique historical, cultural, and religious features. The current research was conducted on the people who visited the tomb of Atar Neishaburi in the winter of 1400. Among the current population, 348 people were selected as a research sample based on Cochran's formula with the available sampling method. Statistical calculations were performed by SPSS and Amos software, and the structural equation modeling method was used to check the conceptual model of the research. The findings of the research indicate that brand awareness in mystical tourism according to the criteria of expectation from the brand, consumer brand allocation, secrets, sensitivity to the brand, intimacy and interest in the brand, respect for the brand, in terms of the fit of the tourist with The destination brand creates a positive and meaningful impact. The presence of an important relationship between tourists and the brand, with enjoyable experiences gained from the brand, creates more trust in the brand. The experiences created in connection with the brand can have potential positive effects on trust and satisfaction with the brand. One of the most important tasks of brand managers is to understand a set of associations around the brand because it can be valuable in the brand.

In healthcare, a diagnosis is reached after a thorough physical assessment and analysis of the patient's medicinal history, as well as the utilization of appropriate diagnostic tests and procedures. 1.7 million People worldwide lose their lives every year due to complications from chronic kidney disease (CKD). Despite the availability of other diagnostic approaches, this investigation relies on machine learning because of its superior accuracy. Patients with chronic kidney disease (CKD) who experience health complications like high blood pressure, anemia, mineral-bone disorder, poor nutrition, acid abnormalities, and neurological-complications may benefit from timely and exact recognition of the disease's levels so that they can begin treatment with the most effective medications as soon as possible. Several works have been investigated on the early recognition of CKD utilizing machine-learning (ML) strategies. The accuracy of stage anticipations was not their primary concern. Both binary and multiclass classification methods have been used for stage anticipation in this investigation. Random-Forest (RF), Support-Vector-Machine (SVM), and Decision-Tree (DT) are the prediction models employed. Feature-selection has been carried out through scrutiny of variation and recursive feature elimination utilizing cross-validation (CV). 10-flod CV was utilized to assess the models. Experiments showed that RF utilizing recursive feature removal with CV outperformed SVM and DT.

The objective of this study was to examine the relationships between entrepreneurial self-efficacy, technological capability, and innovation capability among startup leaders. Specifically, the study aimed to determine the extent to which technological and innovation capabilities predict entrepreneurial self-efficacy, providing insights into the factors that drive entrepreneurial success in startup environments. A cross-sectional research design was employed, involving 290 startup leaders selected based on the sample size recommendations from the Morgan and Krejcie table. Participants completed a structured questionnaire assessing entrepreneurial self-efficacy, technological capability, and innovation capability. Data analysis included descriptive statistics, Pearson correlation, and multiple linear regression using SPSS version 27. Assumptions of linearity, normality, and homoscedasticity were checked and confirmed to ensure the validity of the regression model. Pearson correlation analysis showed significant positive relationships between entrepreneurial self-efficacy and both technological capability (r = 0.52, p < 0.001) and innovation capability (r = 0.56, p < 0.001). Multiple linear regression analysis revealed that technological capability (B = 0.37, SE = 0.08, β = 0.30, t = 4.63, p < 0.001) and innovation capability (B = 0.45, SE = 0.09, β = 0.35, t = 5.14, p < 0.001) were significant predictors of entrepreneurial self-efficacy, explaining 40% of its variance (R^2 = 0.40, F(2, 287) = 46.83, p < 0.001). The study concludes that both technological capability and innovation capability significantly enhance entrepreneurial self-efficacy among startup leaders. These findings underscore the importance of fostering these capabilities to drive entrepreneurial success. The study contributes to the literature by highlighting the critical role of self-efficacy and providing practical implications for entrepreneurs, policymakers, and educators aiming to support entrepreneurial development.