The expanding blue economy, marked by its focus on sustainable use of ocean resources, offers enormous opportunity for Small and Medium-sized Enterprises (SMEs). However, for SMEs to properly integrate and succeed in this economy, they must first have a thorough awareness of the sector’s challenges and prospects. This research used a scoping review and a qualitative study to identify the challenges and opportunities facing SMEs operating in the blue economy. The study discovered recurring themes and gaps in the existing literature by conducting an extensive examination of scholarly publications. The key challenges identified include complicated regulatory frameworks, restricted access to funding, infrastructure restrictions, talent deficiencies, government support, and market outreach. In-depth interviews with Malaysian SME leaders, industry stakeholders, and policymakers were conducted to decipher these findings. The results of interviews confirmed the relevance of the regulatory framework, infrastructure restrictions, talent deficit, and market access challenges in the Malaysian context. In particular, the study revealed emerging opportunities for Malaysian blue SMEs in sectors such as renewable energy, sustainable fisheries, marine biotechnology, and ecotourism. The study emphasizes the importance of an encouraging policy framework, knowledge-sharing platforms, and capacity building activities. It finishes by underlining the ability of SMEs to drive a sustainable and thriving blue economy, if challenges are systematically handled, and opportunities are appropriately capitalized.

In light of swift urbanization and the lack of precise land use maps in urban regions, comprehending land use patterns becomes vital for efficient planning and promoting sustainable development. The objective of this study is to assess the land use pattern in order to catalyze sustainable township development in the study area. The procedure adopted involved acquiring the cadastral layout plan of the study area, scanning, and digitizing it. Additionally, satellite imagery of the area was obtained, and both the cadastral plan and satellite imagery were geo-referenced and digitized using ArcGIS 9.2 software. These processes resulted in reasonable accuracy, with a root mean square (RMS) error of 0.002 inches, surpassing the standard of 0.004 inches. The digitized cadastral plan and satellite imagery were overlaid to produce a layered digital map of the area. A social survey of the area was conducted to identify the specific use of individual plots. Furthermore, a relational database system was created in ArcCatalog to facilitate data management and querying. The research findings demonstrated the approach's effectiveness in enabling queries for the use of any particular plot, making it adaptable to a wide range of inquiries. Notably, the study revealed the diverse purposes for which different plots were utilized, including residential, commercial, educational, and lodging. An essential aspect of land use mapping is identifying areas prone to risks and hazards, such as rising sea levels, flooding, drought, and fire. The research contributes to sustainable township development by pinpointing these vulnerable zones and providing valuable insights for urban planning and risk mitigation strategies. This is a valuable resource for urban planners, policymakers, and stakeholders, enabling them to make informed decisions to optimize land use and promote sustainable development in the study area.

This study aims to predict whether university students will make efficient use of Artificial Intelligence (AI) in the coming years, using a statistical analysis that predicts the outcome of a binary dependent variable (in this case, the efficient use of AI). Several independent variables, such as digital skills management or the use of Chat GPT, are considered.The results obtained allow us to know that inefficient use is linked to the lack of digital skills or age, among other factors, whereas Social Sciences students have the least probability of using Chat GPT efficiently, and the youngest students are the ones who make the worst use of AI.

Urban trees are one of the valuable storage in metropolitan areas. Nowadays, a particular attention is paid to the trees and spends million dollars per year to their maintenance. Trees are often subjected to abiotic factors, such as fungi, bacteria, and insects, which lead to decline mechanical strength and wood properties. The objective of this study was to determine the potential degradation of Elm tree wood by Phellinus pomaceus fungi, and Biscogniauxia mediteranae endophyte. Biological decay tests were done according to EN 113 standard and impact bending test in accordance with ASTM-D256-04 standard. The results indicated that with longer incubation time, weight loss increased for both sapwood and heartwood. Fungal deterioration leads to changes in the impact bending. In order to manage street trees, knowing tree characteristics is very important and should be regularly monitored and evaluated in order to identify defects in the trees.

This study aims to investigate the impact of dance training on the mental health of college students. Utilizing experimental research methods, we established an experimental group and a control group to compare changes in mental health dimensions—including anxiety, depression, self-esteem, and social skills—between the two groups before and after 12 weeks of dance training. The findings indicate that dance training significantly reduces levels of anxiety and depression, while also improving self-esteem and social skills, thereby enhancing social adaptability. These results provide empirical support for the use of dance as an intervention for mental health and offer new insights for mental health education in colleges and universities.