This study aims to analyze the current situation of inheritance taxation in Spain and evaluate the legitimacy crisis surrounding the decision of whether to tax mortis causa transfers, as well as the scope and conditions under which such taxation should occur. The Inheritance and Donations Tax (IDT) frequently sparks debate, and this paper aims to analyze its evolution since its transfer to the Autonomous Communities, tracing its development to the present day. A thorough examination is essential to clarify its significance within a modern tax system, its role in the new system of regional financing, and the reforms necessary for its potential continuation, while also assessing the level of public dissatisfaction it provokes. The methodology employed in this paper involved a review of the existing literature, current legislation, and available scientific-academic resources relevant to the topic. The approach is predominantly theoretical and intentionally cross-disciplinary, aimed at enhancing accessibility and comprehension.

This study aims to examine the role of automotive industry development in the regional growth of Hungarian counties. Through word frequency analysis, the counties were grouped, and their unique characteristics were highlighted. Some counties already play a prominent role in the domestic automotive industry hosting established Original Equipment Manufacturers (OEMs), a significant number of automotive suppliers and high R&D and innovation potential. Another group includes counties that currently lack a significant automotive industry and did not identify it as a key focus area for future development. Additionally, an intermediate group has also emerged, including counties where the automotive industry is either in its early stages of investment, or such development is prioritized in regional planning documents. The study details the direction of automotive development in counties where the industry plays a significant role, focusing on labor market characteristics and human resource development. The findings have significant implications for the future of the automotive industry in these counties, underlining the urgent and immediate need for well-managed and well-established human resource development and ensuring effective partnership to realize its full potential in the automotive industry.

“This paper’s purpose l is to determine whether certain firm-specific factors have an influence on the catering theory of dividend in the MENA region.” The catering theory of dividend related to the dividend policy by the different companies used in our paper to explain the decision by managers. The sample includes 600 non-financial firms listed stocks in the Stock Exchange of 6 countries from MENA region during the years 2010–2019. Catering theory explains why managers initiate (continue) to distribute dividends. A high dividend premium encourages managers to increase the level of dividend payment and explains why firms pay dividends or do not pay them thereafter. Investors should increase their demand for dividends to push managers to comply. Investors show their preference for dividend to self control, satisfaction and increase their profit. “This could be the catering incentive of the firm to decide to pay dividends”. Even although the result Investor preference for dividend is explained by different factors related to the firms characteristics from each firms is different from markets, it can be the evidence supporting the catering theory of dividend, not only in well-developed markets, but also in emerging markets such as our country.

The article examines the appearance of various unfortunate situations and tragic events in modern Kazakh novels that arise due to human and natural ecology problems. The research’s primary goal is to analyze human and natural ecology issues based on contemporary Kazakh novels. We have chosen A. Nurpeyisov’s novel “The Last Duty” as our research material, which focuses on issues of human and natural ecology, and we will discuss the large-scale issues concerning the fate of human, nature, and society as a collective. The research topic’s practical significancelies in examining Kazakh novels that address crucial issues like safeguarding the ecological environment and preserving the green earth, which directly impact the destiny and future of humanity. It also aims to highlight their role in advancing societal development, elevating human values, and safeguarding our spiritual heritage. The research method involves mentioning the names of Kazakh novels that specifically and indirectly focus on the topic of human and natural ecology and summarizing their common features. The article also employed research methods such as analysis, comparison, and discussion. The novelty of the research result: Here are some relevant points. First, in the article, the core topic of the problem of human and natural ecology, which is common to all humanity in modern Kazakh novels, was highlighted. Second, analyzing the three characters, Zhadiger, Pakizat and Azim, which reveal the actual idea of the novel “The Last Duty,” the writer’s stylistic features and skillful aspects were also mentioned during the analysis of the character image through deep psychological analysis, landscape description, clear image, and artistic language, and theoretical conclusions and analyses were presented.

The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.