This research explores the relationship between the independent variables (need for achievement, risk-taking, family support, economic factors, and the dependent variable of women’s enterprises’ success) and examines the moderating influence of socio-cultural factors. A survey-based methodology was adopted. One hundred sixty-nine small and medium-sized enterprises (SMEs) in the Palestinian West Bank were surveyed using structured questionnaires. Structural equation modeling (SEM) was conducted by using the Smart-PLS program. The results indicate that women entrepreneurs’ success in SMEs is positively and significantly impacted by the need for achievement as an internal factor and economic factors and family support as external factors. Furthermore, sociocultural factors did not show any significant moderating influence. By gaining knowledge about the relationship between internal and external factors and the success of women-owned SMEs, this study adds to the body of literature already in existence. These factors can be considered in the success of these enterprises, particularly in an environment full of political and economic fluctuations. Furthermore, the research is said to be the first of its type in Palestine, particularly concerning SMEs run by women. It also supports entrepreneurs by providing them with resources that might aid in the growth and success of their businesses.

The rapid advancement of artificial intelligence (AI) technology is profoundly transforming the information ecosystem, reshaping the ways in which information is produced, distributed, and consumed. This study explores the impact of AI on the information environment, examining the challenges and opportunities for sustainable development in the age of AI. The research is motivated by the need to address the growing concerns about the reliability and sustainability of the information ecosystem in the face of AI-driven changes. Through a comprehensive analysis of the current AI landscape, including a review of existing literature and case studies, the study diagnoses the social implications of AI-driven changes in information ecosystems. The findings reveal a complex interplay between technological innovation and social responsibility, highlighting the need for collaborative governance strategies to navigate the tensions between the benefits and risks of AI. The study contributes to the growing discourse on AI governance by proposing a multi-stakeholder framework that emphasizes the importance of inclusive participation, transparency, and accountability in shaping the future of information. The research offers actionable insights for policymakers, industry leaders, and civil society organizations seeking to foster a trustworthy and inclusive information environment in the era of AI, while harnessing the potential of AI-driven innovations for sustainable development.

This research underscores the importance of enhancing the early detection of diabetic retinopathy and glaucoma, two prominent culprits behind vision loss. Typically, retinal diseases lurk without symptoms until they inflict severe vision impairment, underscoring the critical need for early identification. The research is centered on the potential of leveraging fundus images, which offer invaluable insights by analyzing various attributes of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. The conventional practice of manually segmenting retinal vessels by medical professionals is both intricate and time-consuming, demanding specialized expertise. This approach, reliant on pathologists, grapples with limitations related to scalability and accessibility. To surmount these challenges, the research introduces an automated solution employing computer vision. It conducts an evaluation of diverse retinal vessel segmentation and classification methods, including machine learning, filtering-based, and model-based techniques. Robust performance assessments, involving metrics like the true positive rate, true negative rate, and accuracy, facilitate a comprehensive comparison of these methodologies. The ultimate goal of this research is to create more efficient and accessible diagnostic tools, consequently enhancing the early detection of eye diseases through automated retinal vessel segmentation and classification. This endeavor combines the capabilities of computer vision and deep learning to pioneer new benchmarks in the realm of biomedical imaging, thereby addressing the pressing issues surrounding eye disease diagnosis.

Climate change is one of the most critical global challenges, driven primarily by the rapid increase in greenhouse gas concentrations. Carbon sequestration, the process by which ecosystems capture and store carbon, plays a key role in mitigating climate change. This study investigates the factors influencing carbon sequestration in subtropical planted forest ecosystems. Field data were collected from 100 randomly sampled plots of varying sizes (20 m² × 20 m² for trees, 5 m² × 5 m² for shrubs, and 1 m² × 1 m² for herbs) between February and April 2022. A total of 3,440 plants representing 36 species were recorded, with Prosopis juliflora and Prosopis cineraria as the dominant tree species and Desmostachya bipinnata as the dominant herb. Regression analysis, Pearson correlation, and structural equation modeling were performed using R software to explore relationships between carbon sequestration and various biotic and abiotic factors. Biotic factors such as diameter at breast height (DBH; R=0.94), tree height (R=0.83), and crown area (R=0.98) showed strong positive correlations with carbon sequestration. Abiotic factors like litter (R=0.37), humus depth (R=0.43), and electrical conductivity (E.C; R=0.11) also positively influenced carbon storage. Conversely, pH (R=-0.058), total dissolved solids (TDS; R=-0.067), organic matter (R=-0.1), and nitrogen (R=-0.096) negatively impacted carbon sequestration. The findings highlight that both biotic and abiotic factors significantly influence carbon sequestration in planted forests. To enhance carbon storage and mitigate climate change, efforts such as afforestation, reforestation, and conservation of subtropical forest ecosystems are essential.

This paper reviews the emerging potential of mid-tier transit, articulating how a complex set of established and new factors could contribute both to better transit outcomes and the associated urban regeneration around station precincts. The analysis is based on two structured literature reviews, supported by insights from the authors’ original research. The first provides an overview of the established and new rationale for mid-tier technologies such as the established Light Rail Transit (LRT) and Bus Rapid Transit (BRT) as well as the new Trackless Tram Systems (TTS). The established role for mid-tier transit is now being given extra reasons for it to be a major focus of urban infrastructure especially due to the need for net zero cities. The second review, is a detailed consideration of established and new factors that can potentially improve patronage on mid-tier transit. The established factors of urban precinct design like stop amenities and improved accessibility and density around stations, are combined with new smart technology systems like advanced intelligent transport systems and real-time transport information for travellers, as well as new transport technologies such as micro-mobility and Mobility on Demand. Also explored are new processes with funding and development models that properly leverage land value capture, public private partnerships, and other entrepreneurial development approaches that are still largely not mainstreamed. All were found to potentially work, especially if done together, to help cities move into greater mid-tier transit.