Motivation-based Market Segmentation in Rural Tourism: the Case of Sámán, Iran

Market segmentation is a pivotal and under-investigated issue when evaluating decision-making processes and motivational factors shaping rural tourism. The present study has examined market segments of rural tourists in Iran based on their socio-demographic attributes, travel characteristics and preferred leisure activities, profiling rural tourists on the base of their motivational background. The survey results indicated that rural tourism in the study area is a heterogeneous market, whose development depends on general trends in Middle East tourism market. A comprehensive knowledge of rural tourism actors may help formulating appropriate marketing strategies for internal areas destined to tourism growth

Comparative analysis of local residents’ perceptions of the impacts of tourism on rural areas: A case study of the villages in the basin of the Kolan river in Malayer County

Until now, a large number of studies on tourism in rural regions have concentrated on the recognition of its positive and negative impacts on economic, social, cultural, and environmental dimensions. However, considering the different views held by local people of the impact of tourism, few studies have been carried out to compare the perceptions of stakeholders, especially from the perspective of rural residents. This research aims to do a comparative analysis of the perceptions of local residents about the impacts of tourism on the villages of Kolan river basin in Malayer County. The research method is a mixed procedure. In the qualitative phase, during six sessions, group brainstorming was done by 60 persons participating as local volunteers. They tried to identify the positive and negative impacts of tourism. The findings in this phase were used to design a questionnaire as a measurement tool in the quantitative phase. As many as 350 questionnaires were given to 10% of the population over 15 years of age. The data were analyzed by descriptive statistics, exploratory factor analysis, cluster analysis, discriminant analysis, chi-square, and ANOVA. According to the exploratory factor analysis, the positive impacts were classified into economic, environmental, and social factors. Similarly, the negative impacts were classified into three factors in terms of environmental, social and security, and cultural impacts. The results of the cluster analysis showed three different perspectives. In most cases, there were significant differences between the perceptions of the local residents in terms of positive and negative impacts of tourism. The findings are consistent with the principles of social exchange theory

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Precision diagnostics in cardiac tumours: Integrating echocardiography and pathology with advanced machine learning on limited data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25 % and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94 % in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

The Simultaneous Effect of Aerobic Exercise and Matricaria chamomilla L. Flower Extract on the Serum Level of Peptide C in Streptozotocin-Induced Diabetic Rats

Background and Objectives: Serum level of C peptide is considered as one of the indicators of diabetes treatment process. In this study, simultaneous effect of aerobic exercise and Matricaria chamomilla L. flower (MFE) extract, was investigated on the serum level of C peptide in male diabetic rats.   Methods: In this experimental study, 24 male Wistar rats (weight range, 200±20g), were randomly divided into 4 groups (n=6): control group (diabetic without treatment and training), MFE (diabetic treated with the extract), aerobic training (diabetic and training), and MFE+aerobic training (diabetic treated with extract+aerobic training). The aerobic training was performed as running on treadmill for 12 weeks (5 days per week, 60min/day, 26meter/min). The rats were diabetized with a single dose of streptozotocin (65mg/kg bw, ip). The MFE groups that daily received 200mg/kg orally (gavaged) along with exercise, were tested for 12 weeks. Data were analyzed using Shapiro-Wilk test, one way ANOVA, and Tukey post hoc test at the significant level of p<0.05.   Results: In this study, the serum level of C peptide significantly increased in MFE group, exercise group, and the chamomile extract along with exercise group compared to the control group (p<0.01).   Conclusion: The findings of this research indicated that aerobic exercise and use of MFE caused an increase in the serum level of C peptide in diabetic rats

The role of herbal medicine in the side effects of chemotherapy

Nowadays, significant progress has beenmade in the treatment of cancer. Chemotherapyis one of the most common treatmentsin cancer management. The use of chemotherapydrugs is generally associated with various serious andnon-medical complications. Nausea and vomiting withChemotherapy are among the most severe side effectsand of major concern for patients with cancer. Due to thelimited effect and dangerous side effects of taking antiemeticdrugs, herbal medicine has been welcomed by patientsas one of the most active and complementary Drugsin this field. The contradictory results in this area led theresearcher to carry out the present study with the aim ofsystematically reviewing the effects of medicinal plants onchemotherapy-induced nausea and vomiting. In this structuredreview, all studies during the years 2008-2018 usingkeywords chemotherapy, chemotherapy side effects,medicinal herbs, nausea and vomiting and drug side effectsfrom internal and external databases. The data wereanalyzed using meta-analysis method; the selected articleswere collected according to inclusion criteria and finallywere examined more closely. After searching the databasesand extracting a large number of articles by title andabstract, 360 articles were reviewed and finally 18 articleswere reviewed. The herbal remedies used to prevent andtreat chemotherapy-induced nausea were ginger, chamomile,mint, cardamom, and onion, respectively. Early detectionof side effects in patients can prevent forced discontinuationof treatment or reduce the dose of medicationsto control side effects that will reduce the effectivenessof treatment. Therefore, it is possible to improve the conditionof patients and reduce the side effects of chemotherapyby providing appropriate educational facilities andprograms on how to use these herbs. These interventionshave also been studied in patients with a wide range ofcancers, while each type of cancer has its own chemotherapyprotocol and differs in severity from nausea to otherprotocols, As a result, it is not easy to judge the efficacy ofdifferent types of herbs on chemotherapy-induced nauseaand to generalize the results to other cancers, so furtherresearch is recommended by the researcher

Tourism insurance and Socio-demograhic contexts in Iran: a Delphi panel

The 'insurance’ issue in Iran was investigated in the tourism sector through interviews with specialists and managers of tourism and travel insurance. The role of insurance in developing tourism sector was widely discussed

Challenges toward Sustainability? Experiences and Approaches to Literary Tourism from Iran

Interdisciplinary narrative studies are of great importance in several disciplines, especially in the humanities and social sciences. Cultural tourism and its sub-disciplines, including the complex issue of &lsquo;literary tourism&rsquo;, is an interdisciplinary field of investigation, positioned in between geography and urban&ndash;rural studies. In Iran, this form of tourism has been neglected so far&mdash;with no distinction between urban and rural areas&mdash;despite a particularly rich literary heritage. The present study recognizes the challenge of literary tourism in Iran, delineating some possible actions to develop it as a future engine of economic growth, especially in rural districts. As a contribution to a refined comprehension of literary tourism development paths, a content analysis was run collecting views and textual data on literary tourism in Iran. The empirical results of this study indicate that the mentioned challenges can be classified into several main dimensions and a broader set of sub-themes. The possible actions responding to such challenges can be classified into more dimensions and a vast number of sub-themes. Actions reducing territorial disparities and fueling entrepreneurship in local communities are appropriate to stimulate the emergence (and, possibly, consolidation) of literary tourism districts in Iran, giving an original contribution to sustainable development especially&mdash;but not exclusively&mdash;in rural settlements

Challenges toward Sustainability? Experiences and Approaches to Literary Tourism from Iran

Interdisciplinary narrative studies are of great importance in several disciplines, especially in the humanities and social sciences. Cultural tourism and its sub-disciplines, including the complex issue of ‘literary tourism’, is an interdisciplinary field of investigation, positioned in between geography and urban–rural studies. In Iran, this form of tourism has been neglected so far—with no distinction between urban and rural areas—despite a particularly rich literary heritage. The present study recognizes the challenge of literary tourism in Iran, delineating some possible actions to develop it as a future engine of economic growth, especially in rural districts. As a contribution to a refined comprehension of literary tourism development paths, a content analysis was run collecting views and textual data on literary tourism in Iran. The empirical results of this study indicate that the mentioned challenges can be classified into several main dimensions and a broader set of sub-themes. The possible actions responding to such challenges can be classified into more dimensions and a vast number of sub-themes. Actions reducing territorial disparities and fueling entrepreneurship in local communities are appropriate to stimulate the emergence (and, possibly, consolidation) of literary tourism districts in Iran, giving an original contribution to sustainable development especially—but not exclusively—in rural settlements