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.

Nothofagus pumilio forests constitute the most economically important forest stand in southern Argentina and Chile. Total volume stocking and volumetric yield vary according to site quality, degree of occupation, growth stage and forest history of the stand. The objective of this work was to evaluate the stocking and the productive potential in quantity and quality of products for the sawmilling industry, using three harvesting systems (short logs, long logs and complete shafts) in the protection cut of a N. pumilio forest of site quality III in Tierra del Fuego (Argentina). The trials were conducted in an irregular mature forest with two strata and abundant regeneration (3.0 ha; RDI 93.8–113.4%). Total volumes varied between 726.5 and 850.3 m³∙ha^-1, with a volume/basal area ratio of 11.8 to 12.1 m³∙m^-2. The harvesting rates obtained were: 45.5% for complete logs, 21.3% for long logs and 22.4% for short logs. A model was used to estimate the timber volume for each system, where full shafts resulted in a significant increase in timber volume. Considering new alternatives in the planning of harvesting in forest management for N. pumilio forests, such as the system of complete shafts, allows obtaining higher harvesting rates, increasing the benefits for the forestry company and minimizing the damage to the forest, due to the shorter distance of the machinery in the forest harvesting.

To analyze the effect of an increase in the quantity or quality of public investment on growth, this paper extends the World Bank’s Long-Term Growth Model (LTGM), by separating the total capital stock into public and private portions, with the former adjusted for its quality. The paper presents the LTGM public capital extension and accompanying freely downloadable Excel-based tool. It also constructs a new infrastructure efficiency index, by combining quality indicators for power, roads, and water as a cardinal measure of the quality of public capital in each country. In the model, public investment generates a larger boost to growth if existing stocks of public capital are low, or if public capital is particularly important in the production function. Through the lens of the model and utilizing newly-collated cross-country data, the paper presents three stylized facts and some related policy implications. First, the measured public capital stock is roughly constant as a share of gross domestic product (GDP) across income groups, which implies that the returns to new public investment, and its effect on growth, are roughly constant across development levels. Second, developing countries are relatively short of private capital, which means that private investment provides the largest boost to growth in low-income countries. Third, low-income countries have the lowest quality of public capital and the lowest efficient public capital stock as a share of GDP. Although this does not affect the returns to public investment, it means that improving the efficiency of public investment has a sizable effect on growth in low-income countries. Quantitatively, a permanent 1 ppt GDP increase in public investment boosts growth by around 0.1–0.2 ppts over the following few years (depending on the parameters), with the effect declining over time.

The electro-magnetic (EM) waves transmitted through a thin object with fine structures are observed by a microsphere located above the thin object. The EM radiation transmitted through the object produces both evanescent waves, which include information on the fine structures of the object (smaller than a wavelength), and propagating waves, which include the large image of the object (with dimensions larger than a wavelength). The super-resolutions are calculated by using the Helmholtz equation. According to this equation, evanescent waves have an imaginary component of the wavevector in the z direction, leading the components of the wavevector in the transversal directions to become very large so that the fine structures of the object can be observed. Due to the decay of the evanescent waves, only a small region near the contact point between the thin object and the microsphere is effective for producing the super resolution effects. The image with super-resolution can be increased by a movement of the microsphere over the object or by using arrays of microspheres. Both propagating and evanescent waves arrive at the inner surface of the microsphere. A coupling between the transmitted EM waves and resonances produced in the dielectric sphere, possibly obtained by the Mie method, leads to a product of the EM distribution function with the transfer function. While this transfer function might be calculated by the Mie method, it is also possible to use it as an experimental function. By Fourier transform of the above product, we get convolution between the EM spatial modes and those of the transfer function arriving at the nano-jet, which leads the evanescent waves to become propagating waves with effective very small wavelengths and thus increase the resolution.

Increasing water consumption has increased using of synthetic nutritional methods for enriching groundwater resources. Artificial feeding is a method that can save excess water for using in low level water time in underground. The purpose of this study is to evaluate the performance of the flood dispersal and artificial feeding system in the Red Garden of Shahr-e-Daghshan and improving, saving    quality of the groundwater table in the area. In order to investigate the performance of these plans, an area of 1570 km² was considered in the Southern of Shah-Reza. The statistics data from 5 years before the design of the plans (1986-2002) related to flood control fluctuations in 20 observation wells and many indicator Qanat were surveyed in this area. The annual fluctuations in the level of the station show a rise in the level of the station after the depletion of the plan. Dewatering of the first and second turns, with an increase of more than one meter above groundwater level, has had the highest impact on the level of groundwater table in the region. Reduced permeability at sediment levels, wasted flood through evaporation and wasteful exploitation of groundwater resources, cause to loss of the impact on the increase in the level and quality of groundwater in the area, especially in the dry, drought season and recent high droughts.

This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.