The study examines the impact of COVID-19 on the economies of Gulf Corporation Council (GCC) member states. The event study methodology was used to analyze Cumulative Abnormal Return (CAR) of GCC member states’ stock indexes: Kuwait Stock Exchange Index (KSE), Dubai Financial Market Index (DFM), Saudi Arabia Tadawul Index (TASI), Qatar Exchange Index (QE), Bahrain All Share Index (BHB), Oman’s Muscat Stock Exchange Index (MSM), Abu Dhabi Stock Exchange Index (ADX) while the S&P GCC Composite Index was used as a reference. Data obtained from 28 July 2019 to 27 July 2020, and 1 March 2020, designated as the event day, abnormal returns (AR) and cumulative average abnormal returns (CAARs) were examined across various time intervals. The findings reveal significant market reactions to the pandemic, characterized by fluctuations in abnormal returns and CAARs. Statistically significant abnormal returns and CAARs during certain time periods underscore the dynamic nature of market responses to the COVID-19 event. These results provide valuable insights for policymakers and market participants seeking to understand and navigate the economic implications of the pandemic on GCC economies. The study recommends that other GCC states, particularly Oman, consider the policies undertaken by Qatar, UAE, and Saudi Arabia, to avoid a long economic crisis.

The current study aims to determine the post COVID-19 adoption rates, the variation of the adoption by regions, and the effects of communication technologies on higher education with focus on students’ engagement and faculty satisfaction. The present research uses the convergent parallel design which is a form of mixed-methods research design. First, the study searched for 18 relevant articles using key search terms including “post-COVID-19 education”, “e-learning tools”, “communication technologies” and “higher education”. The qualitative analysis, however, shows that the technological strategies have to be in line with the preparedness of the people, the need to address challenges such as the lack of face-to-face contact and how technologies such as augmented reality and simulation-based learning can be used. Quantitative analysis shows that teleconferencing tools (β = 0.45, p < 0.001) and cloud computing (β = 0.38, p < 0.003) have positive impact on engagement and satisfaction. The one-way ANOVA results show that there is a difference in the adoption rates across the regions while the MCAs score for communication challenges is 60%. From the descriptive statistics it can be seen that there is a very high adoption rate of cloud computing (Mean = 89.7%, Standard Deviation = 3.1%) and teleconferencing tools (Mean = 84.9%, Standard Deviation = 4.5%). The Structural Equation Modeling (SEM) shows the domino effect of teleconferencing on engagement (β = 0.60, p < 0.001), satisfaction (β = 0.75, p < 0.002) and collaboration efficiency (β = 0.55, p < 0.001). Thus, the current study establishes the fact that there is a need to provide equal opportunities and technology which is adaptable to improve the students’ engagement and satisfaction in various learning institutions.

This research aims to determine the strategy of the Jakarta Provincial Government in increasing the resilience and growth of small and medium enterprises (SMEs) within a collaborative governance framework post-COVID-19. This study explores the effectiveness of SMEs and facilities in accessing financing and fostering collaborative partnerships between SMEs, government agencies, and financial institutions by utilizing USAID’s Theory of Change (TOC). This research uses a qualitative approach supported by in-depth interviews and Focus Group Discussions to enrich the insights of SME stakeholders, large companies, and SME actors and assess the impact of their roles. The results of this research highlight the critical role of SME Cooperative Banks (SCB) in improving SMEs’ access to credit and financial services, including collaborative governance frameworks and partnerships between SMEs, government agencies, and banks, which were identified as necessary to improve policy coherence and encourage conducive SME business environment conditions. The main findings of this research underscore the importance of the SCB model, demonstrating its potential to improve SME resilience and economic sustainability. This SCB model enriches the TOC indicators introduced by USAID. The study identifies gaps in digital infrastructure and market access that hinder SME growth and recommends targeted interventions to address these challenges. This study shows that SCB offers a promising pathway to increase the resilience and growth of SMEs in Indonesia, especially if accompanied by effective collaborative governance strategies. These initiatives can encourage inclusive economic development and strengthen the role of SMEs as drivers of the local economy. Recommendations include expanding the SCB model to other regions, encouraging digitalization, facilitating market access, advocating for a supportive policy framework, and integrating these strategies to advance the principles of USAID’s Theory of Change, fostering sustainable SME development and economic resilience.

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.

Ecological environment damage events will destroy or damage the balance between animal and plant habitats and ecosystems, and even pose a threat to China’s ecological security. However, at present, there are some problems in the identification and evaluation of forest ecosystem damage, such as imperfect evaluation system, insufficient quantitative evaluation methods, imperfect damage compensation management system, and lack of analysis of the overall damage of the interaction between human activities and forest ecosystem. Based on the damaged object, the system involves a total of four first-class indicators, including physical damage, mental damage, economic forest fruit loss, forest by-products loss, processing and manufacturing loss, forest tourism loss, scientific research literature and history loss, soil conservation loss, water conservation loss, wind prevention and sand fixation loss, carbon fixation and oxygen release loss, atmospheric purification loss. There are 14 secondary indicators of emergency treatment fee and investigation and evaluation fee, as well as 22 tertiary indicators, and the value quantification method of each indicator is clarified by using market value method, alternative cost method, shadow engineering method, recovery cost method and other methods. The article also discusses the management system of forest ecosystem damage from the two aspects of forestry technology department and judicial administration department. The purpose is to provide reference for the quantification and standardization of forest ecosystem damage assessment technology and the improvement of management system.

The temporomandibular joint (TMJ) is considered a bicondylar diarthrosis type joint. Imaging evaluation is a fundamental part of its assessment, which should include both bony and soft tissue characteristics and the relationship between them. Magnetic resonance imaging (MRI) represents the gold standard for the study of soft tissues; however, up to now, its main application continues to be the visualization of the articular disc. For this reason, the present article aimed to point out the information available in the literature regarding the visualization of the joint capsule in MRI and to evaluate it as an independent structure.