Investigating the Mediating Role of Market Orientation between Internal Marketing and the Development of Entrepreneurial Orientation within Private Sports Clubs

Purpose–This studyexamines the role of market orientation in the relationship between internal marketing and entrepreneurial orientation within private sports clubs. Design/methodology/approach–The research is a descriptive-correlational study based on private sports clubs employees within Iran (Sanandaj). A theoretical model was developed based on the literature and tested using SPSS and PLS-SEM software. Findings– The findings indicate a positive relationship between internal marketing and employees’ entrepreneurial orientation. Market orientation has also played a positive mediating role in the relationship between internal marketing and entrepreneurial orientation. Originality/value– The results suggest a higher level of market orientation in the organization can increase teamwork and, consequently, entrepreneurship development among employees. This is important in sports clubs as employees have a significant role in the success of the sports club. Club employees’ satisfaction, generated through internal marketing, provides is a prerequisite for customer satisfaction. This therefore creates an environment supportive of entrepreneurial orientation in the club

Adaptive Rule-Base Influence Function Mechanism for Cultural Algorithm

This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. This rule based system is optimized using Genetic Algorithm (GA). The proposed modified CA algorithm is compared with several other optimization algorithms including GA, particle swarm optimization (PSO), especially standard version of cultural algorithm. The obtained results demonstrate that the proposed modification enhances the performance of the CA in terms of global optimality.Optimization is an important issue in different scientific applications. Many researches dedicated to algorithms that can be used to find an optimal solution for different applications. Intelligence optimizations which are generally classified as, evolutionary computations techniques like Genetic Algorithm, evolutionary strategy, and evolutionary programming, and swarm intelligence algorithms like particle swarm intelligence algorithm and ant colony optimization, etc are powerful tools for solving optimization problem

Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products

Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products

The Iranian blood pressure measurement campaign, 2019: study protocol and preliminary results

Purpose Hypertension is one of the most important risk factors for premature mortality and morbidity in Iran. The objective of the Iranian blood pressure (BP) measurement campaign was to identify individuals with raised blood pressure and providing appropriate care and increase the awareness among the public and policymakers of the importance of tackling hypertension. Methods The campaign was conducted in two phases. The first (communication) phase started on May 17th (International Hypertension Day). The second phase started on June 8th, 2019, and lasted up to July 7th during which, blood pressures were measured. The target population was Iranians aged >= 30 years. Participants voluntarily referred to health houses in rural and health posts and comprehensive health centers in urban areas in the setting of the Primary Health Care network. Additionally, over 13,700 temporary stations were set up in highly visited places in urban areas. Volunteer healthcare staff interviewed the participants, measured their BP, and provided them with lifestyle advice and knowledge of the risks and consequences of high blood pressure. They referred participants to physicians in case their BP was high. Participants immediately received a text message containing the relevant advice based on their measured BP and their past history. Results Blood pressure was measured for a total of 26,678,394 participants in the campaign. A total of 13,722,148 participants (51.4%) were female. The mean age was 46 +/- 14.1 years. Among total participants, 15,012,693 adults (56.3%) with no past history of hypertension had normal BP, 7,959,288 participants had BP in the prehypertension range (29.8%), and finally, 3,706,413 participants (13.9%) had either past medical history of hypertension, used medications, or had high BP measured in the campaign. Conclusion The campaign was feasible with the objective to increase the awareness among the public and policymakers of the importance of tackling hypertension in Iran