Rural tourism, which offers authentic cultural and nature-based experiences, is increasingly recognized as a vital tool for sustainable development. Ethiopia, with its rich rural landscapes and cultural heritage, holds immense potential for rural tourism, but the sector remains underdeveloped. This study assesses the facilitating conditions and challenges of rural tourism in Ethiopia using a mixed-methods approach. Results indicate that Ethiopia’s economic growth, improved rural infrastructure, large rural population, higher ethnic and religious diversity index, and 11 UNESCO World Heritage Sites provide strong foundations for rural tourism. However, significant challenges such as inadequate infrastructure, limited marketing, restricted access to financing, ethnic conflicts, environmental degradation, and insufficient stakeholder cooperation hinder its growth. To address these barriers, the study proposes a model encompassing strategic investments in infrastructure, enhancing marketing and promotion, access to finance initiatives, conflict resolution strategies, sustainable tourism practices, enhancing stakeholder coordination, and supportive policy frameworks. By employing these strategies, Ethiopia can harness the full potential of its rural tourism sector, contributing to economic development and community well-being while promoting cultural preservation and environmental sustainability. Also, the proposed model is highly applicable to other developing economies that share similar contexts. Besides, given the importance of the seven fundamental pillars of the model, it remains relevant across tourism types like coastal destinations.

The National Fitness Program Plan (2021–2025) (hereinafter referred to as the Plan) proposes to perfect the public service system for sports and fitness by 2025, make national sports and fitness more convenient, and advocate providing intelligent services for national fitness campaign. With the development of the Internet era, modern information technologies such as big data, the Internet of Things, and artificial intelligence have been introduced into sports affairs, providing technical support for the optimization of the public service system for sports and fitness. Therefore, in the context of a national fitness campaign, intelligent sports service is an important link for promoting national fitness in various regions. Relevant workers should attach importance to promoting “physical fitness” with “intelligence” in the process of advancing national fitness program, and actively creating intelligent public services for national fitness. Focusing on the integration of modern information technology and sports affairs, with the implementation of the Plan as the research background, the construction of intelligent sports parks as the starting point, this article outlines the construction plan of intelligent sports parks based on the connotation summary of national fitness program and intelligent sports. At the same time, it analyzes the issues that intelligent sports parks need to pay attention to in providing public services for national fitness, and proposes countermeasures for the high-quality development of national fitness services in intelligent sports parks.

This case study employs the Asset-Based Community Development (ABCD) theory as a conceptual framework, utilizing semi-structured interviews combined with focus group discussions to uncover the driving forces influencing rural revitalization and sustainable development within communities. ABCD is considered a transformative approach that emphasizes achieving sustainable development by mobilizing existing resources within the community. Conducted against the backdrop of rural revitalization in China, the study conducts on-site investigations in Yucun, Zhejiang Province. Through the analysis of Yucun’s community development and asset utilization practices, the study reveals successful experiences in various aspects, including community construction, industrial development, cultural heritage preservation, ecological conservation, organizational management, and open economic thinking. The results indicate that Yucun’s sustainable development benefits from its unique resources, leveraging policy advantages, collective financial organizations, and open economic thinking, among other factors. These elements collectively drive rural revitalization in Yucun, leading to sustainable development.

This research, with a qualitative approach, is based on a literature review and a press analysis related to mergers, acquisitions and dissolutions of Higher Education Institutions in South America. Our findings evidence a gap in the academic literature for analyzing and understanding these processes. The literature on the subject is scarce; however, the press has recorded them in a constant way. While in the past this phenomenon was mainly among public universities, currently it is a fundamentally private trend. The main reasons to carry out this process by Higher Education Institutions are those related to geographic expansion or positioning (for merger processes), absorption and concentration of institutions by groups of interest (for merger processes, acquisition) and, the crisis resulting from the financial-administrative management of the institutions, as well as the non-compliance with national and international quality standards designed by accreditation agencies and institutions (for dissolution processes). On the contrary of some literature results, in any of the processes the search for prestige or reputation by the institutions was detected as a reason.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

The COVID-19 pandemic has brought life changing conditions to families that require coping strategies in order to survive and achieve family well-being. This study aims to analyze differences between single earner and dual earner families during the COVID-19 pandemic and to analyze the factors that influence subjective family well-being. The research design used was a cross sectional study with sample collection through non-probability sampling. Data collection was carried out by filling out questionnaires online. The number of respondents involved in the study was 2084 intact families with children residing in DKI Jakarta, West Java, and Banten Provinces. Reliability and validity tests were conducted. The results of the independent t-test showed that dual-earner families experienced better life changes and a higher level of subjective family well-being than single-earner families and had lower economic pressure and lower economic coping than single earner families. The SEM analysis found that life changes affected economic coping negatively and subjective family well-being positively. Family income influenced economic coping negatively and subjective family well-being positively. Finally, it was found that economic coping had no effect on subjective family well-being.