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.

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.

Lettuce (Lactuca sativa L.) is the main leafy vegetable grown in Brazil. Its productivity and quality are limited by the growing season, the nearby environment and the type of cultivar adopted. The objective of this work was to verify at different times of the year the best planting environment for lettuce cultivation in a semi-humid tropical climate. For this purpose, an experiment was set up in three different seasons (October–November 2014, January–March, May–July 2015). The experimental design was randomized blocks, in a 3 × 3 × 2 factorial arrangement, consisting of three seasons, three cultivars (cvs. Vera^®, Tainá^® and Rafaela^®) and two growing environments (low tunnel with beds protected with mulching consisting of soil protection with plastic fabric covering, and beds without protection or conventional cultivation) and four replicates per treatment. Plant biomass, stem length, head diameter, number of leaves per head and crop productivity were evaluated as response parameters. The results showed that the May–July period favored biomass production, head diameter and productivity. Despite the similarity between varieties, the variety Vera^® is more productive in biomass, number of leaves per head, stem length and productivity. The low tunnel planting system with mulching is adequate under the conditions evaluated for lettuce cultivation. This system in the May–July period favors a superior development in the characteristics biomass, head diameter and productivity, if compared to conventional cultivation during the October–November period.

Introduction: Growth, yield and quality of okra (Abelmoschus esculentus (L.) Moench) are related to fertilizer application, being nitrogen (N) the most outstanding, due to its direct relationship with photosynthesis and vegetative growth of the plant. Objective: The objective was to evaluate the agronomic and productivity characteristics of okra as a function of N dose. Materials and methods: The study was conducted at the experimental area of Campus Gurupi, the Universidad Federal de Tocantins (UFT), Brazil, in two planting periods (autumn/winter and spring/summer). The experimental design used was randomized block design (RBD) with six treatments (50, 100, 150, 150, 200 and 250 kg N ha^-1) and four replications. Urea was used as a source of N. The characteristics evaluated were: productivity, average fruit mass, height and plant chlorophyll index. Results: Productivity and plant height were superior in the fall/winter crop. Mean fruit mass and chlorophyll index were not influenced by planting time. For productivity, a linear response was obtained with increasing dose up to the limit of the N dose used (250 kg ha^-1), with a mean value higher than 14 t of fruit. Mean mass and plant height responded linearly to increasing N dose. Nitrogen affected the chlorophyll index, with maximum values of 45.96 and 47.19, observed in the two evaluation periods. Conclusion: Planting time and N content in the soil interacted with plant height, being favorable in the period without precipitation. N influenced all the characteristics, demonstrating the importance of nitrogen fertilization in the development of okra plants.

This paper provides a comprehensive review of equity trading simulators, focusing on their performance in assuring pre-trade compliance and portfolio investment management. A systematic search was conducted that covered the period of January 2000 to May 2023 and used keywords related to equity trade simulators, portfolio management, pre-trade compliance, online trading, and artificial intelligence. Studies demonstrating the use of simulators and online platforms specific to portfolio investment management, written in English, and matching the specified query were included. Abstracts, commentaries, editorials, and studies unrelated to finance and investments were excluded. The data extraction process included data related to challenges in modern portfolio trading, online stock trading strategies, the utilization of deep learning, the features of equity trade simulators, and examples of equity trade simulators. A total of 32 studies were included in the systematic review and were approved for qualitative analysis. The challenges identified for portfolio trading included the subjective nature of the inputs, variations in the return distributions, the complexity of blending different investments, considerations of liquidity, trading illiquid securities, optimal portfolio execution, clustering and classification, the handling of special trading days, the real-time pricing of derivatives, and transaction cost models (TCMs). Portfolio optimization techniques have evolved to maximize portfolio returns and minimize risk through optimal asset allocation. Equity trade simulators have become vital tools for portfolio managers, enabling them to assess investment strategies, ensure pre-trade compliance, and mitigate risks. Through simulations, portfolio managers can test investment scenarios, identify potential hazards, and improve their decision-making process.

Taxus cuspidata Sieb. ET. Zucc. is a taxus of Taxaceae, a rare third-order relict species distributed in northeastern China, and a wild endangered plant species protected by national level I. Taxol (paclitaxel, trade name taxol) and cephalomannine (cephalomannine) are all diterpenoids contained in the genus Taxus, with broad-spectrum anti-tumor activity and unique anti-cancer mechanism. In this study, the distribution of paclitaxel and cephalomannine in the leaves of Taxus cuspidata in different parts and different growth stages was discussed. The results showed that the content of two substances in the leaves of the majority of the crowns was lower than that of the biennial and tertiary there were no significant differences in the contents of two substances in the two-year and three-year-old

foliage. There was no significant difference in the contents of the two layers in the three levels of the noodles, and

the content of the male was slightly higher than that of the dark. The content of paclitaxel in the leaves of natural

northeast yew was the highest at dormancy period, and the content of flowering and fruit was not much different. The

content of Cephalotaxin was the highest in dormancy period, and that of cephalosporin the content of paclitaxel and

cephalomannine in each plant were significantly different. There was significant difference between the two plants.