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.

Peru is a country open to the world economy and to national and foreign investments; therefore, economic activities of an industrial, commercial and service nature in general are developed. It also has a wide variety of natural resources, which is why the state has chosen to apply differentiated treatment in the tax field to certain types of business activities by granting certain “benefits” and “incentives”. However, due to a lack of knowledge about tax legislation, they are not used adequately. In this context, the objective was to analyze the level of knowledge of the legislation, tax and its impact on the development of their operations in formal business aquaculture in the ring circumlacustrine of the region in 2021. It was developed under a descriptive correlational design with a sample of 80 circumlacustrine ring aquaculture companies. The results indicated that there is a low level of knowledge about tax legislation on the part of the owners of aquaculture companies, which negatively affects the development of their formal operations in the circumlacustrine ring of the Puno region. As a consequence, it has a negative impact on the formalization of companies since they do not know about the benefits and tax incentives and even less about the tax regimes to which they are subject as taxpayers; therefore, aquaculture companies are in the informality category in a high percentage.

The study focused on investigating the effects of varying levels of HA (HA₁ = 0, HA₂ = 25, HA₃ = 50, HA₄ = 75, and HA₅ = 100) on Red Dragon, Red Prince, and Red Meat varieties of red radish. This analysis aimed to unravel the relationship between different levels of HA and their impact on the growth and productivity of red radish genotypes. The findings revealed that the Red Prince genotype attained the utmost plant height of 24.00 cm, an average of 7.50 leaves per plant, a leaf area of 23.11 cm², a canopy cover of 26.76%, a leaf chlorophyll content of 54.60%, a leaf fresh weight of 41.16 g, a leaf dry weight of 8.20 g, a root length measuring 9.73 cm, a root diameter of 3.19 mm, a root fresh weight of 27.60 g, a root dry weight of 6.75 g, and a remarkable total yield of 17.93 tons per hectare. The implications of this study are poised to benefit farmers within the Dera Ismail Khan Region, specifically in the plain areas of Pakistan, by promoting the cultivation of the Red Prince variety.

The xanthorrhiza species of the genus Arracacia belongs to the Apiaceae family and is known for its ability to generate tuberous reservoir roots that are harvested annually and marketed fresh in South American countries such as Colombia, Brazil, Venezuela, Peru, Bolivia and Ecuador. In Colombia, arracacha is planted mainly in 15 departments and the regional cultivars are differentiated by the color of the leaves, petiole and tuberous root, the best known being amarilla común or paliverde, yema de huevo, and cartagenera. There are studies that have characterized regional materials by applying a limited number of descriptors, but they do not allow knowing the morphology and phenotypic differentiation of each one; therefore, their definition and characterization constitute a support in breeding programs that allow the efficient use of the genetic potential and increase the knowledge about the diversity of cultivars. Phenotypic characterization and description of three cultivars was performed during two production cycles (2016 and 2018) in two phases (vegetative and productive) applying 74 morphological variables (42 qualitative and 32 quantitative) organized in seven groups of variables: plant, leaf, leaflet, petiole, propagule, stock and tuberous root. A factorial analysis for mixed data (FAMD) was performed, which incorporated a multivariate analysis with all variables and identified 11 discriminant variables, 8 qualitative and 3 quantitative, which can be used in processes of characterization of arracacha materials. A morphological description of each cultivar was made, which means that this is the first complete characterization study of regional arracacha materials in Colombia.

The purpose of the article is to present the results of analysis of newly industrialized countries in the context of sustainable development. The study took place within the framework of the Kaldor’s structural-economic model of the gross domestic product and the energy flow model, using the socio-economic systems power changes analyzing method. Within the context of the approach, an invariant coordinate system in energy units is considered, the necessary conditions for sustainable development are formulated, and the main parameters for assessing the potential for growth and development are determined. The article focuses on key issues regarding new concepts of sustainable development and methodology for assessing sustainable development using the concept of socioeconomics useful power for the countries of the newly industrialized economy a group of emerging countries that have made in short time period a qualitative transition in socio-economic development. Based on a new definition of sustainable development in energy units, development trends are formulated for the selected countries during 20 years for the period 2000–2019. Results of the study can be used to planning for the transition to sustainable development. The data of the Central Statistical Office of European Union, the World Bank and the United Nations Organization were used for calculations. Initial interpretation of the calculated data has been done for the largest newly industrialized countries Brazil, India and China in terms of the gross domestic product in the period 1990–2019. For comparison, data on USA are presented as countries with advanced economy.