This study investigated the relationship between telecommunications development, trade openness and economic growth in South Africa. It determined explicitly if telecommunications development and trade openness directly impact economic growth or whether telecommunications strengthen or weaken the link between trade openness and economic growth using the ARDL bounds test methodology. The findings reveal that both telecommunications development indicators and trade openness significantly and positively impact South Africa’s GDP in the short and long terms. The study also found that control variables like internet usage and gross fixed capital formation significantly and positively influence GDP. Conversely, inflation was found to consistently affect GDP negatively and significantly. The findings from the ARDL cointegration analysis affirm a long-run economic relationship between the independent variables and GDP. The study also established that telecommunications development slightly distorts trade in the foreign trade-GDP nexus in South Africa. Despite this, the negative interaction effect is not substantial enough to overshadow the positive impact of trade openness on economic growth. From a policy perspective, the study recommends that South African policymakers prioritise enhancing local goods’ competitiveness in global markets and reducing trade barriers. It also advocates for improving the accessibility and affordability of telecommunications technologies to foster economic development.

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.

Distributed biomass energy technology has strong adaptability to the types of raw materials, flexible project scale, can meet the needs of special users, better economy in small scale, easier commercial development, in line with the characteristics of biomass resources and China’s national conditions. The distributed utilization of biomass energy mainly includes biomass briquette fuel and biogas. The key technologies include biomass briquette fuel processing and combustion, large and medium-sized biogas engineering technology, biomass gasification pyrolysis and gas utilization. At present, China’s distributed biomass energy technology is mainly in the stage of technological improvement and application demonstration. It is expected that by 2030, most of the key technologies will be basically mature and have the conditions for industrialization. The main development direction of China’s distributed biomass energy industry is the replacement of traditional coal-fired gas, urban/rural clean living energy supply, and rural ecological environmental protection. The pollution caused by burning coal/fuel oil, and at the same time centering on the national new urbanization strategy, provide sustainable clean energy for the construction of new rural areas, and improve the level of rural ecological and environmental protection. At present, the main bottleneck restricting the development of distributed biomass energy industry is economy and reliability. The state should increase investment in technological innovation and policy support, convert the environmental and social benefits of biomass energy into cost benefits, and promote biomass energy. The development of the industry can be distributed and utilized.

The fresh dried pollen of grape and seedless grape varieties were used as the research material. Each cultivar was stored at room temperature, 5 C, 0 C, -18 C and -40 C respectively. Sucrose 200g / L + boric acid 50mg / L + agar 8g / L as the nutrient substrate, and the comparative study on the germination and culture of the nuclear breed and the seedless cultivar. The results showed that the pollen viability decreased with the increase of storage time at different temperatures. The pollen viability decreased at -18 C and -40 , and the pollen viability decreased at the same temperature The There was a significant difference in pollen viability between different cultivars at the beginning of storage, and the pollen viability of the kernel breed was higher than that of the seedless grape.

This paper considers the problems surrounding the implementation of road infrastructure plans in a policy perspective. As the main pillar of regional connectivity, road networks provide the link across national markets, foster strong and sustainable economic growth, help meeting people’s basic needs, and promote trade and competiveness. It is argued that planning, implementing, and managing good transportation infrastructures poses a series of challenges that require competence, good governance, and the availability of funds. Such problems become more complex when road projects encompass different states and become transnational. The regional dimension of connectivity involves both opportunities and risks; a cooperative attitude by all parties is viewed as the best ingredient to achieve a positive balance. Since most countries cannot still rely on domestic resources, the paper stresses the role of virtuous policies in directing capital flows from abroad towards the infrastructural projects of Southeast Asia.