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 Akit tribe fishermen on Rupat Island, Riau, Indonesia, are a remote indigenous community with a low level of education. They have experienced cultural acculturation after the influx of outsiders and the government built road infrastructure to break the isolation. The government also provides internet facilities to speed up the process of modernizing communications between them. The research aim is to analyze the role of government support as a mediator in the influence of education and acculturation on communication modernization among Akit fishermen. The research used a survey method, involving 165 of the 763 Akit fishermen as respondents. This number determine used the Sample Size Calculator technique. Respondents were selected using a purposive random sampling technique. The variables studied consisted of education, acculturation, government support (as mediator), and communication modernization. Data collection was carried out through a closed questionnaire containing statements, which were measured with a 5-point Likert scale. The data were analyzed using the Structural Equation Modeling method with the help of SmartPLS 4 software. The research results show that acculturation and government support have a positive and significant influence on communication modernization, while education plays a negative influence. Government support as a mediator plays a positive and significant role in the influence of education on communication modernization, while it does not play any role in the influence of acculturation. The most implication of this research is that the government must further increase its role in organizing the acculturation process for Akit fishermen to accelerate the communication modernization process.

This research endeavors to assess the legal requirements for the operation of mediation and conciliation centers in the UAE based on Federal Law No. 17 of 2016 and its amendment in 2021 No. 5. It is structured into three main sections: the first establishes and defines these centers, the second defines conciliation procedures and the third considers the preceding. The aim is to identify the legal procedures associated with mediation and conciliation centers within the UAE judicial systems and their function in providing solutions for civil and business litigations with the most efficiency and minor financial investments. It also calls for using other forms of conflict adjudication before adopting the legal approach. The conclusions and recommendations indicate the necessity of further improving the Mediation and Conciliation Centers Law due to the necessity of legislative shifts, which would contribute to the UAE’s leading position in legislation related to centers for mediation and conciliation.

The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.

In order to create the possibility of economic breakthrough development, remove economic institutional bottlenecks, release resources, and develop the economy quickly and sustainably in Vietnam in the coming time, it is impossible not to mention solutions to improve the quality, create breakthroughs in training and fostering talents. This is one of the important solutions in the context that the Party and State require the application and development of science and technology more and more extensively in all fields and all sectors in Vietnam. The article focuses on researching the the political basis, legal basis, and practical basis for training, fostering, attracting and employing talents in Vietnam. Meanwhile, statistics on undergraduate and postgraduate training in the period of 2016–2022, the training level of the workforce and the Global Talent Competitiveness Index show that Vietnam has not achieved many positive changes in training, fostering, attracting and employing talents as expected. The article is approached from many different aspects, including the perspective of leaders and managers at the head of state agencies, the perspective of businesses and the perspective of the university teaching staff and scientific research workers themselves. On that basis, the article points out the key contents that need addressing so as to build solutions to improve quality, create breakthroughs in training, fostering, attracting and employing talents in Vietnam in the context of international integration and science and technology development. The main contributions of the article focus on the identification of the concept of “talent”, the criteria for determining “talent” and the renewal of awareness of policies and laws on training, fostering, attracting, employing, introducing and recommending talents.

This financial modelling case study describes the development of the 3-statement financial model for a large-scale transportation infrastructure business dealing with truck (and some rail) modalities. The financial modelling challenges in this area, especially for large-scale transport infrastructure operators, lie in automatically linking the operating activity volumes with the investment volumes. The aim of the paper is to address these challenges: The proposed model has an innovative retirement/reinvestment schedule that automates the estimation of the investment needs for the Business based on the designated age-cohort matrix analysis and controlling for the maximum service ceiling for trucks as well as the possibility of truck retirements due to the reduced scope of tracking operations in the future. The investment schedule thus automated has a few calibrating parameters that help match it to the current stock of trucks/rolling stock in the fleet, making it to be a flexible tool in financial modelling for diverse transport infrastructure enterprises employing truck, bus and/or rail fleets for the carriage of bulk cargo quantifiable by weight (or fare-paying passengers) on a network of set, but modifiable, routes.

The objective of the research is twofold. The study examines the role of public finance in promoting sustainable development in SSA. Secondly, the study investigates the optimal level of public finance beyond which public finance crowds out investment and hinders sustainable development in SSA. The study adopts a battery of econometric techniques such as the traditional ordinary least square (OLS) estimation technique, Driscoll-Kraay covariance matrix estimator, and the dynamic panel threshold model. The study found that an increase in public debts lead to a decline in sustainable development. In contrast, the results show that increase in spending on health and education, and tax can engender sustainable development in SSA. Further, we uncover the optimal levels of public spending on health and education, and public debts that engenders sustainable development in SSA. One main implication of the findings is that governments across SSA needs to reduce public debts levels and increase public spending on health and education to within the threshold levels established in this study to aid sustainable development in SSA.