This study aims to explore the connotation of Daoist medicine culture and investigate its relationship with modern medicine. Exploring the connotation of Daoist medicine culture is beneficial for advocating a healthy lifestyle, improving people’s physical and mental health, promoting individual comprehensive development, and enhancing happiness. By drawing wisdom and experience from Daoist medicine, inheriting various medical methods such as herbal treatment, acupuncture, massage, and integrating the concept of integrated Chinese and Western medicine into modern medicine, not only can treatment effectiveness be improved, but also interdisciplinary communication and cooperation can be promoted, thus driving the innovation and development of medical knowledge.

COVID was initially detected in Wuhan City, Hubei Province, People's Republic of China, in late 2019, as reported by researchers. Subsequently, it rapidly disseminated to numerous nations at the beginning of 2020, ultimately manifested as a pandemic with worldwide prevalence. Regarded as one of the most severe pandemics in documented human history, this outbreak resulted in deaths and infection over a quite millions of individuals globally. Due to its airborne nature, the coronavirus can be transmitted through actions such as coughing, sneezing, talking, and similar activities. Enclosed spaces lacking sufficient airflow are more likely to facilitate the spread of air borne diseases. Wearing a face mask that can provide protection against airborne pollutants, considered as Standard Operation Procedures (SOPS) for COVID-19. It is crucial to monitor the implementation of preventive measures both within and outside the building or workplace in order to prevent the transmission of COVID-19. The main objective of this project is to develop a face mask and social distance detector. You Only Learn One Representation (YOLOR) was implemented as a most advanced end-to-end target identification approach to develop the proposed system. An online available facemask dataset was utilized. The developed system can track individuals wearing masks in real time and can also identify and highlight persons with a rectangular box if their social distance is violated. This proposed interactive framework enables constant monitoring both internally and externally, thereby enhancing the capacity to identify offenders and ensure the safety of all individuals involved.

Osteoid osteoma (OO) is a benign osteoblastic tumor of bone that usually affects children and young adults. They are usually located on metaphysis or diaphysis of long bones. Their clinical, anamnesis and radiological findings are typical. Intra-articular OO however has different properties due to its placement within joints. Sclerosis around the lesion is either minimal or non-existent, but synovitis can be seen in the joint. For this reason, they are usually diagnosed later. In this case series, we diagnosed three cases (2 ankles and 1 hip joint) that were diagnosed with osteochondral lesions previously and had in chronic pain which did not respond to several treatments in different centers with intra-articular OO and treated them with radiofrequency ablation using computerized tomography. Knowing the radiological properties of intra-articular OO and being aware of this condition during differential diagnosis of joint pain cases will be useful to diagnose this rare pathology.

Objective: Standardizing image acquisition protocols and image quality across cameras is an important need in imaging, in particular in multi-center clinical trials and the use of image analysis and machine learning algorithms. The objective of this study was to examine the effect of ordered subset expectation maximization (OSEM) reconstruction parameters on the quantitative image quality of cardiac perfusion SPECT images in different typical SPECT cameras and therefore assess the need to change the parameter values across cameras. Methods: The analysis was carried out by comparing the defect contrast-to-noise ratio (CNR) at 12 OSEM subset-iteration combinations. Eight frames were reconstructed using the SIMIND Monte Carlo Simulation package. An activity of 370 MBq (10mCi) and projection acquisition interval of 20 seconds per projection were used. Attenuation (AC) and scatter corrections (SC) were performed in this study for all images. Results: The 16-2 subset-iteration combination yielded the highest CNR and defect contrast values for both cameras. The difference between CNR values for two cameras was found to be close to 5%. Conclusions: Monte Carlo simulations can be useful to investigate how quantitative image quality behaves with respect to reconstruction parameters and correction algorithms in a controlled environment. In this study, the use of different camera brands did not seem to significantly affect the lesion detectability. Further simulations with more extended range of parameters and camera brands may be conducted in the future to quantify further the variability between different brands of cameras.

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 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.