Low temperature is one of the most significant environmental factors that threaten the survival of subtropical and tropical plant species. By conducting a study, which was arranged in a completely randomized design with three replicates, the relative freezing tolerance (FT) of four Iranian pomegranate cultivars, including ‘Alak Torsh’, ‘Tabestaneh Torsh’, ‘Poost Sefid’, and ‘Poost Syah’, as well as its correlation with some biochemical indices, were investigated. From each cultivar, pieces of one-year-old shoot samples were treated with controlled freezing temperatures (−11, −14, and −17 ℃) to determine lethal temperatures (LT₅₀) based on survival percentage, electrolyte leakage, phenolic leakage, and tetrazolium staining test (TST) methods. Results showed that FT was higher in the second year with a lower minimum temperature and a higher concentration of cryoprotectants. The stronger correlation of electrolyte leakage with survival percentage (r = 0.93***) compared to the other three indices explained that this index could be the most reliable injury index in determining the pomegranate FT to investigate freezing effects. Of all four cultivars, ‘Poost Syah’ was the hardest by presenting a higher FT than ~ −14 ℃ in mid-winter. Accordingly, this pomegranate cultivar seems to be promising to grow in regions with a higher risk of freezing and to be involved in breeding programs to develop novel commercial cultivars.

This research aims to identify best practices and policy guidelines that foster sustainable urban ecotourism. As urban areas continue to expand, integrating ecotourism into urban planning emerges as a critical approach to sustainable development. This paper compares the policies and practices of urban ecotourism development in Thailand and China, aiming to construct a sustainable framework applicable to urban ecotourism development. Employing a comparative literature review, this research synthesizes findings from peer-reviewed journals, governmental reports, and case studies published between 2000 and 2024. The analysis focuses on the policies and practices adopted by Thailand and China to promote urban ecotourism, examining their effectiveness, challenges, and outcomes. The review shows distinct approaches in the two countries, with Thailand emphasizing community-based practices and stakeholder involvement and China primarily focusing on top-down policy initiatives for urban ecotourism development. Despite differing strategies, both countries demonstrate a commitment to integrating ecotourism into urban development plans. From the environmental, socio-cultural, and economic three dimensions, key successes include enhanced biodiversity conservation, increased local community participation, and improved tourist satisfaction. Challenges such as inadequate policy implementation, environmental degradation, and the sustainability of ecotourism practices are also discussed. The conclusion is that a holistic approach to urban ecotourism development that aligns policy and practice with the principles of sustainability is meaningful. The proposed framework offers actionable insights for policymakers, urban planners, and ecotourism practitioners aiming to use the potential of ecotourism as a tool for sustainable urban development in Thailand, China, and beyond.

Rapid population growth and inadequate adherence to scientific and managerial principles in urban planning have intensified numerous challenges, pushing major Iranian cities toward instability. Tehran, as the capital and one of the most urbanized regions in the country, faces significant sustainability threats that require immediate attention. These challenges are not unique to Tehran but represent a broader issue faced by rapidly urbanizing cities worldwide, particularly in developing countries. Addressing such challenges is critical to fostering sustainable development on a global scale. While urban sustainability has been extensively studied, limited research has focused on the indicators of urban instability and their tangible impacts on sustainable urban planning. This study aims to bridge this gap by identifying and analyzing key factors contributing to urban instability across economic, environmental, and social dimensions, with Tehran serving as a representative case. The findings reveal that economic instability is driven by uncertainty in economic policies, fluctuating housing prices, non-standard housing conditions, income disparity, unemployment, and cost of living pressures. Environmental instability is exacerbated by climate change, urban heat islands, floods, transportation mismanagement, energy insecurity, pollution, and insufficient green infrastructure. Social instability arises from limited social interaction, unequal access to services, weak community participation, social harms, and diminished urban safety and welfare. By framing these local challenges within a global context, the study underscores the interconnectedness of these dimensions and highlights the necessity for integrated, evidence-based approaches that combine local insights with global best practices. The findings aim to contribute to the broader discourse on sustainable urban development by offering actionable insights and strategies that can be adapted and implemented in other rapidly urbanizing cities. This research serves as a guide for policymakers, urban planners, and stakeholders worldwide, emphasizing the importance of holistic and resilient urban strategies to address the multifaceted challenges of sustainability and instability.

While infrastructure provides necessary public services and is vital for the socio-economic development of a nation, public funds alone cannot finance all infrastructure needs in society, especially after the COVID-19 pandemic, where many countries are facing budget deficits. Although private financing schemes, such as public-private partnerships (PPPs) and land value capture, have been considered intensively, they have yet to produce adequate private capital flows to infrastructure projects due to a lack of incentives for private investors. Against the background, this paper proposes a new financing mechanism in which governments might divert some of the increased tax revenue from the spillover effects of newly constructed infrastructures to fund the private sector through grants or subsidies. The empirical work in Vietnam shows a significant increase in tax revenues after completing two expressways, supporting our idea about spillover effects, which includes small- and medium-sized enterprise (SME) development. This study’s results suggest that spillover effects can bring new opportunities for governments and multilateral development banks (MDBs) to implement infrastructure projects with greater private sector involvement in the region. It also proposes some financial schemes, such as land capture and financing for business startups, including SMEs, to enhance the spillover effects of infrastructure.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.