This study investigates the intricate relationship between a nation’s GDP growth rate and three key variables: the number of granted patents, research and development (R&D) expenditure, and education expenditure. The purpose of the research is to discern the impact of these factors on GDP growth rates. Drawing on theoretical frameworks, including Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS), and Canonical Correlation Regression (CCR) techniques, the paper employs a robust methodological approach to unveil insights into the dynamics of economic growth. Contrary to conventional assumptions, the results reveal a negative correlation between R&D expenditure and GDP growth rate. In contrast, the number of patents granted and education expenditure shows a positively significant effect on the GDP growth rate, underscoring the pivotal roles of intellectual property creation and education investment in fostering economic growth. The conclusion emphasizes the importance of a nuanced understanding of these relationships for policymakers. The research’s implications highlight the need for balanced investments in innovation and education. The originality and value of this study lie in its unique findings challenging established beliefs about the impact of R&D expenditure on economic growth.

When COVID-19 hit all the Asian countries, Indonesia issued various laws and regulations. This study investigates these laws that do not improve the country’s ability to increase its adaptive structuration and foresight-oriented investment. It analyzes all the new laws, which should be based on the requirements of both concepts. It considers that all the laws are intended to defend the Government of Indonesia’s economic performance (GoI). It means that all the established regulations were built on the premise that they only focused on national economic preservation, especially economic growth. In other words, this study stated that the absence of regulations containing adaptive restructuration and foresight-oriented investment would decrease the state’s agility. This absence potentially impacts Indonesia to zcategorize the future as the state’s political failure. It shows evidence that Indonesia could not enforce and empower its structural potential. This study indicates that Indonesia made no foresight-oriented investment to cover the disbursed costs due to the COVID-19 pandemic. Future policies should be improved by including growth opportunities to enhance Indonesia’s agility. This agility could finally be achieved when all the laws issued by the GoI do not contain the praxis.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

The objective of this work was to evaluate the effect of potassium concentrations applied via fertigation on the growth, yield and chemical composition of eggplant ‘Ciça’ in a distroferric red Latosol. The treatments were composed of five concentrations of K₂O (0, 36, 72, 108 and 144 kg ha^-1 supplied via fertigation), using potassium chloride as a source, divided into six applications. The irrigation system was of the drip type and irrigation management was done via a “Class A” evaporometer tank. Harvest started at 62 days after transplanting (DAT) and lasted for five months. The variables evaluated were: plant height, number of leaves, fresh fruit mass, number of fruits per plant, yield per plant, productivity and classification of the fruits according to their length and diameter. At 85 DAT, fruit were collected for characterization as to the percentage of lipids, proteins and fibers. Although the potassium fertigation in cover provided a reduction in the production and productivity, the concentrations of 36 kg ha^-1 and 72 kg ha^-1 of K₂O applied via fertigation, increased the physical-chemical characteristics of the fruits.

By carrying out a laboratory experiment, the influence of priming methods, including ZnSO4, BSN, and hydropriming was evaluated on the seed germination of hybrid AS71 corn. Then, the main and interaction effects of the priming methods, planting dates, and weed interference levels were surveyed on the vegetative growth traits, yield, and yield components of corn in a field experiment. Based on the lab experiment, although the maximum germination percentage (100%) was observed in the treated plots by hydropriming 22 h after treatment (HAT), the greatest seedling vigor index (122.99) was recorded with treated seeds by ZnSO4 (0.03 mg L–1) at 8 HAT. The greatest emergence index was observed in the treated plots by hydropriming on both planting dates of June 1 and 11. The interaction of planting dates and weed interference levels revealed that the highest emergence index (14%–17%) occurred in the weed-free plots on both planting dates. BSN recorded the greatest corn 1000-grain weight that was significantly higher than the control plots by 28%. Furthermore, BSN enhanced the corn grain yield compared with the control plots by 63% and 24.9% on the planting dates of June 1 and 11, respectively. BSN, as a nutri-priming approach, by displaying the highest positive effects in boosting the corn grain yield in both weedy and weed-free plots as well as both planting dates, could be a recommendable option for growers to improve the crop yield production.

This research aims to examine in more depth the changes resulting from the Job Creation Law, which impact the level of business friendliness in Indonesia, and how to analyze these changes to improve the business environment to be more conducive to carrying out business activities. This research uses normative legal research methods and is analytical descriptive research. There have been several changes since the emergence of the Job Creation Law, such as the establishment of a limited liability company. Changes to the Job Creation Law could improve the Indonesian economy. However, juridically, this regulation gives authority to the central government to manage micro and small businesses, contrary to the principle of decentralization, which prioritizes the provision of resources to local governments.