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Nowadays, the tourism industry is considered the largest and most diverse industry in the world, which can play a significant role in the economic growth and development of a country as the main source of income and employment. In this regard, the purpose of this study is to focus on evaluating the competitiveness indicators of Shiraz city tourism destination based on a combined model.

For this purpose, a questionnaire with 78 questions was used to evaluate the indicators, in which a total of 1,432 tourists participated, including 927 domestic tourists and 505 international tourists. The collected data were analyzed to determine the hierarchical relationship between the indicators using equation structural modeling.

The research findings indicate that the most effective and influential indicators in the field of tourism competitiveness are natural and handmade resources, and the most influential factors are demand conditions and cultural and natural resources. In this hierarchy, each factor affects its previous level and influences the next level.

This study offers significant potential for uncovering credible and robust approaches to further investigate the contextualization of tourism competitiveness on both national and international scales, thereby generating valuable new insights. By conceptualizing the diverse dimensions of tourism competitiveness and delving into the variations in its impacts across multiple levels, this research not only challenges existing notions but also aids destinations in maintaining and enhancing their market position and share over time. This study offers valuable insights and practical implications for both researchers and practitioners in the field of urban tourism. It enhances the understanding of destination competitiveness, informs policy decisions, facilitates benchmarking and best practices, guides strategic decision-making and promotes sustainable tourism development.

Although prior research has employed various variables to predict player churn, the dynamic evolution of the behavioral patterns of players has received limited attention. In this study, churn prediction models are developed by incorporating the progress level, in-game purchase, social interaction, behavioral pattern and behavioral variability (BV) of players in social casino games (SCGs). The study distinguishes churn prediction between two player groups: monetizers and non-monetizers.

This study employs three machine learning techniques—logistic regression, decision trees and random forests—using real-world player data from an SCG company to construct churn prediction models. Two experiments were conducted. In Experiment 1, BV was combined with four other variable categories to effectively predict churn behaviors across all players (n = 52,246). In Experiment 2, churn prediction models were developed separately for monetizers (n = 16,628) and non-monetizers (n = 35,618).

The findings from Experiment 1 indicate that incorporating BV significantly improves the overall performance of churn prediction models. Experiment 2 demonstrates that churn prediction models achieve better performance and predictive accuracy for monetizers and non-monetizers when BV is calculated over the 3-day to 7-day and 7-day to 14-day windows, respectively.

This study introduces BV as a novel variable category for churn prediction, emphasizing within-person variability and demonstrating its effectiveness in enhancing model performance. Churn prediction models were independently constructed for monetizers and non-monetizers, utilizing different time windows for variable extraction. This approach improves predictive performance and highlights key differences in critical variables influencing churn across the two player groups. The findings provide valuable insights into churn management strategies tailored for monetizers and non-monetizers.

This study explores the formation process of cognitive destination image among first-time Chinese millennial tourists visiting Iran.

Employing a comprehensive mixed-method exploratory approach, blending qualitative and quantitative methodologies, we aim to construct a conceptual model explaining the complex process of tourists’ cognitive destination image development. In the qualitative phase, interviews with 15 Chinese millennials unveiled key factors such as perceived behavioral control, perceived product quality, on-site involvement, engagement, and prior destination image as fundamental influencers of tourists’ cognitive destination image. Subsequently, in the quantitative phase, involving a questionnaire survey with 229 first-time Chinese travelers, we proposed a conceptual model to validate our primary findings.

The results highlight the emergence of on-travel cognitive image as a developmental concept originating from tourists' on-site participation and pre-existing perceptions of the destination. Moreover, we identified perceived behavioral control as the primary antecedent of millennials’ image formation, directly affecting the destination’s image development. Furthermore, increased involvement and engagement were found to diminish perceived behavioral control. Additionally, perceived product quality prompted millennials to engage more with cultural attractions and activities, enriching their in-situ image.

The findings offer valuable insights for destination managers, enabling them to construct tailored strategies to augment the cognitive destination image.

This study provides a novel understanding of the cognitive destination image formation process among Chinese millennial tourists, highlighting the crucial role of perceived behavioral control, involvement, engagement, and perceived product quality. These insights can guide destination managers in enhancing the overall tourist experience and destination appeal.

This study evaluates the reliability of a multi-state system (MSS) with n components, each having two s-dependent components via copulas.

The study employs copula functions to model dependencies between components in an MSS. Specifically, it analyzes a (1,1)-out-of-n three-state system using Frank and Clayton copulas for reliability evaluation. A simulation-based case study of a micro-inverter solar panel system is also conducted using the Farlie–Gumbel–Morgenstern (FGM) copula.

The study finds that incorporating component dependencies significantly impacts the reliability of multi-state systems. Using Frank and Clayton copulas, the analysis shows how dependency structures alter system performance compared to independent models. The case study on a micro-inverter solar panel system, using the FGM copula, demonstrates that real-world systems with dependent components exhibit different performances. Also some effects of dependence parameters on the performance characteristics of the system such as mean residual lifetime and mean past lifetime are also examined.

This study is original in its use of copula functions to evaluate the performance of multi-state systems, particularly focusing on a (1,1)-out-of-n three-state system with dependent components. By applying Frank and Clayton copulas, the research advances reliability analysis by considering component dependencies, often overlooked in traditional models. Additionally, a case study on a micro-inverter solar panel system using the FGM copula highlights the practical application of these methods.

Bangladesh is one of the leading countries that has been facing serious air pollution issues, with an exponentially higher death rate attributed to it than other environmental pollution. This study aims to identify the sources and dynamics of particulate matter (PM) pollution across different micro-environments in Rajshahi City.

PMs’ concentration data were collected from 60 sampling stations, located across the six micro-environments of the study area, throughout the year using “HT 9600 Particle Counter.” To assess the level of pollution, the air quality index (AQI) was calculated, and different methods, including observation, group discussion, interview and questionnaire survey, were used to identify the pollution sources.

Both PM_2.5 and PM₁₀ exhibit varied concentrations in different micro-environments, and the area covered by different AQI classes differs considerably throughout the year. The monthly average concentration of PM_2.5 and PM₁₀ was highest in January, 200 and 400 µg/m³ and was lowest in September, 46 and 99 µg/m³, respectively. Among the total 1,440 observations, 853 observations (59.24%) exceeded the national standard. Based on the pollution level, different months and micro-environments in the city have been ranked in descending order as January > December > February > March > April > November > October > May > June > July > August > September and traffic > commercial > industrial > residential > green cover > riverine environment.

Although numerous research has been conducted on air pollution in Bangladesh, the authors are certain that no attempt has been made to address the issue from a multi- micro-environmental perspective. This makes the methodology and findings truly unique and significant in the context of air pollution research in Bangladesh.

Most Asians are directly or indirectly dependent on agriculture, and most live in rural areas. Since Asia is primarily an agricultural economy, rural tourism and agrotourism are critical in enabling growth. Agrotourism is a term that refers to the fusion of tourism and agriculture. The freshwater turtles (Batagur sp.) are a group of six turtles that once roamed southern Asia's great rivers and estuaries from the Indian Subcontinent to the Indonesian islands. Therefore, this review is to investigate the current state of agrotourism in Batagur sp. conservation and assess the associated challenges and opportunities. This review paper recognized the impacts and benefits for the rural site as an agrotourism industry. Moreover, three initiatives in Batagur sp. conservation programs were also discussed. Agrotourism, with a focus on the conservation of Batagur sp., is a vital driver of economic growth in rural Asian regions. It diversifies rural communities' income sources, enhancing economic stability and resilience.

This research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.

To obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.

The study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.

The study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.

The research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.

This work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

High surface area-to-volume ratios make nanoparticles ideal for cancer heat therapy and targeted medication delivery. Moreover, ternary nanofluids (TNFs) may possess superior thermophysical properties compared to mono- and hybrid nanofluids due to their synergistic effects. In light of this information, the objective of this article is to examine the blood-based TNF flow within convergent/divergent channels under velocity slip and temperature jump.

Leading partial differential equations corresponding to the problem are transformed into a system of nonlinear ordinary differential equations by using similarity variables. The bvp4c code that uses the finite difference method is used to obtain a numerical solution.

The effect of nanoparticles may change depending on the characteristics of flow near the wall. The properties and proportions of the used nanoparticles become important to control the flow. When TNF was used, an increase in the Nusselt number between 4.75% and 6.10% was observed at low Reynolds numbers. At high Reynolds numbers, nanoparticles reduce the Nusselt number and skin friction coefficient values under some special flow conditions. Importantly, the effects of second-order slip on engineering parameters were also investigated. Furthermore, the Nusselt number increases with increasing shape factor.

Obtained results of the study can be beneficial in both nature and engineering, especially blood flow in veins.

The main innovations of this study are the usage of blood-based TNF and the examination of the effect of shape factor in convergent/divergent channels with second-order velocity slip.

Spiti Valley is a home to temples, untouched natural landscapes and is enriched in flora and fauna. This makes Spiti Valley an emerging ecotourism destination. The prime objective of this study is to analyse the development of ecotourism in the state of Himachal Pradesh, India, particularly in the region of Spiti Valley, Himachal Pradesh. This study provides an opportunity to examine the level of awareness about ecotourism among tourists. In order to achieve the objectives of the study quantitative methodology has been adopted. The data were collected through a questionnaire. The findings of the study indicate towards the awareness among the tourists regarding the environment and how everyone is playing their parts to conserve and protect the environment. The study also depicts the economic benefits of ecotourism to the region and how it can generate employment for the local people directly or indirectly.

The main purpose of this study is to develop an environmental mitigation behavior model (EMBM) for predicting waste reduction behaviors among young coastal tourists in Bangladesh by modifying and extending the theory of planned behavior (TPB).

The research has been administered by applying mixed method (both qualitative and quantitative), where study-1(qualitative) identifies factors, study-2 (quantitative) verifies factors by employing exploratory factor analysis (EFA) approach, and study-3 confirms factors influencing waste reduction behaviors among young coastal tourists by employing confirmatory factor analysis (CFA). Study-2 collects 385 valid responses and analyzes by applying the principal component analysis (PCA) technique with the Varimax rotation method by using SPSS-v25, and Study-3 collects 501 valid responses and analyzes by partial least structural equation modeling (PLS-SEM) technique, using Smart PLS3.3.3.

The study findings have revealed that waste reduction intention and perceived ecological balance significantly influence waste reduction behaviors among young coastal tourists in Bangladesh. Further, waste reduction intention is significantly predicted by mitigation attitude, resource conservation norms and environmental perceptions. Furthermore, environmental ethics significantly affects mitigation attitudes and resource conservation norms.

The study offers several insightful implications (e.g. incentives, charging fines, environmental awareness-related knowledge-based campaign, etc.) for policymakers and industry operators, which may be a dynamic cost-effective mechanism for reducing waste at coastal tourism destinations in Bangladesh as well as in the world.

This study addresses the need for developing a model that can explain waste reduction behaviors among young coastal tourists in Bangladesh, and thus, the study uniquely postulates the theory of environmental mitigation behavior for predicting waste reduction behaviors by modifying and extending the TPB.