Effective harvesting strategies are crucial for maximizing annual catch and ensuring the sustainability of lobster (Homarus americanus) farming. This paper presents a nonlinear objective programming model to optimize harvesting intensity based on lobster life cycle dynamics and harvesting characteristics. We model the population dynamics of 1-4 year-old lobsters using differential equations to account for natural mortality, spawning, and harvesting effects. Solving the model with LINGO 12.0, we determine that the optimal harvesting intensity coefficient is 17.36, which maximizes annual catch to 3.88 × 10¹⁰ grams. Results indicate that maintaining harvesting intensity around this optimal value balances economic benefits and population stability, ensuring sustainable farm operations.

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.

The goal of the project is to investigate and discover tree species abundant in the Mekong Delta Vietnam, and find out species to develop land in southern coastal of Vietnam and based on research to applicated for food and medicinal on part of forest trees. Mekong Delta a amount of alluvium sediments flows from upstream China to Vietnam by the river branches, then get out the Sea. This sediments accumulated gradually elevation the new land. The coastal where mangrove forests with a rich ecosystem of plants and animals. Over time, these forests change, with different plant species succeeding each other. This aims of this study to finding plant species, classification of forest types based on ecological regions, assessement the biodiversity of tree species, and compare the abundance communities, measuring the growth of the forest in these regions. In 2023, a comprehensive survey was conducted by using a systematic approach. Research content and methods. The content is to investigate the situation of woody plant species in mangrove forests in sub-regions with different ecological characteristics. The number of survey plots have done depend on the density of the forest, Base on the width of the forest range, the number of survey plots in sub region set up from 10 to 15 plots. In total, 68 plots have done established in the erea, the area of plot is 100 square meters (10 m x 10 m). Using the statistical software in forestry to survey and analysis data. The results of research is to find the number of species in each ecological region and growth situation, in which the important thing is to evaluate the adaptation of species in each sub-region to propose wich species to choose as the main species in aforestation the fastest land on sea. The result provided a complete picture of the tree species composition, distribution, and community structure characteristics in each ecological sub-region. The result of survey showed in the sub-region one is seven species. In the sub region two is eleven species. In the sub region three is eight species. In the region four is ten species. The total species of the mangrove forest in the Western Mekong Delta have 16 species from 11 plant families have been identified. Among these species have 6 dominant species include Avicennia oficinali),Avicennia alba, Rhizophora apiculata, Excoecaria agallocha, Someratia caseolaris, and Bruguiera yipamoriza. From the investigation have been found two species grow on the best on new land were Avicennia officinalis and Avicennia alba this findings show they can develope on the original new land for the shore of the Western Mekong Delta. The survey results also calculated the average of the height, diameter (D1.3), canopy, health of the nature mangrove tree for each sub region and total region. From these results showed the division of foresty structure, the structure of height, diameter (D1.3), canopy, heathy of the sub region and total region in the Western Mekong Delta. Suggestions after discovering during the investigation that there are Avicennia officinalis and Avicennia alba are two species that can implement development plants to expand natural land by planting on suitable sea surface areas for Mekong Delta of Vietnam. In addition, referring to research documents on these adapted species can exploit food and medicinal herbs in discovering the level biodiversity distribution abundance of these species. This finding can help Vietnam by mearsures using the species Aviecennia be discovered will promote sea reclamation faster instead of letting the natural law of sea reclamation follow.