The present work shows an application of the Chan-Vese algorithm for the semi-automatic segmentation of anatomical structures of interest (lungs and lung tumor) in 4DCT images of the thorax, as well as their three-dimensional reconstruction. The segmentation and reconstruction were performed on 10 CT images, which make up an inspiration-expiration cycle. The maximum displacement was calculated for the case of the lung tumor using the reconstructions of the onset of inspiration, the onset of expiration, and the voxel information. The proposed method achieves appropriate segmentation of the studied structures regardless of their size and shape. The three-dimensional reconstruction allows us to visualize the dynamics of the structures of interest throughout the respiratory cycle. In the future, it is expected to have more evidence of the good performance of the proposed method and to have the feedback of the clinical expert, since the knowledge of the characteristics of anatomical structures, such as their dimension and spatial position, helps in the planning of Radiotherapy (RT) treatments, optimizing the radiation dose to cancer cells and minimizing it in healthy organs. Therefore, the information found in this work may be of interest for the planning of RT treatments.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.

Vietnam has experienced an impressive period of economic growth since implementing an export-oriented economic policy. Vietnam’s international economic integration is deepening, and the output of the export sector has been continuously improved with a double-digit growth rate in recent years, especially in Ho Chi Minh City. Hence, the purpose of this paper is to study the impact of trade liberalization on export intensity of Vietnamese exporters as well as the moderating role of the location. In this study, data was collected from 80 exporters listing in Vietnam stock markets from 2007 to 2022. Further, regression test was carried out by applying GMM model. The results show that trade liberalization outcomes have a positive impact on export intensity. We, however, do not find enough evidence of the moderating effect of the location factor. These findings support Resource-based View theory, and trade liberalization policy. The findings imply that Vietnam government should continue to implement trade liberalization policy to support export sector growth.

Employee retention is a critical concern for organizations in today’s dynamic labor market. This paper introduces a novel framework, integrating “absolute potential of the employee” and “risk associated with leaving the employee”, to address this challenge. Findings from the study suggest that this framework can effectively assist organizations in strategizing retention techniques. The research methodology employed an exploratory research design and collected data from 576 employees across various sectors. The results indicate significant implications for organizational risk assessment and employee retention strategies.

Aiming at the current problems of poor dynamic reconstruction of UAV aerial remote sensing images and low image clarity, the dynamic reconstruction method of UAV aerial remote sensing images based on compression perception is proposed. Construct a quality reduction model for UAV aerial remote sensing images, obtain image feature information, and further noise reduction preprocessing of UAV aerial remote sensing images to better improve the resolution, spectral and multi-temporal trends of UAV aerial remote sensing images, and effectively solve the problems of resource waste such as large amount of sampled data, long sampling time and large amount of data transmission and storage. Maximize the UAV aerial remote sensing images sampling rate, reduce the complexity of dynamic reconstruction of UAV aerial remote sensing images, and effectively obtain the research requirements of high-quality image reconstruction. The experimental results show that the proposed dynamic reconstruction method of UAV aerial remote sensing images based on compressed sensing is correct and effective, which is better than the current mainstream methods.

The performance of five cauliflower cultivars in conventional and alternative phytosanitary management—without the use of synthetic pesticides—was evaluated. Two experiments were conducted at Epagri, Ituporanga Experimental Station in February 2018 and 2019. A randomized block design with four repetitions was adopted, with twenty plants of each cultivar as plots. The seedlings were transplanted on millet and mucuna straw at a spacing of 0.5 m × 0.8 m. We evaluated agronomic yield, inflorescence quality, pest damage and plant diseases, especially bacterial and fungal rots. The cauliflower hybrids Vera, Verona and Serena stood out in productivity and quality, being the most indicated for sowing in off-season crops, in the Alto Vale do Itajaí region. The most productive cultivars were less damaged by bacterial diseases and defoliating caterpillars and without interference of whitefly infestation on yield. The results also reveal that it is possible to control pests and diseases with phytosanitary products of lower toxicity, i.e., with lower residues of synthetic pesticides.