This review focuses on ferrites, which are gaining popularity with their unique properties like high electrical resistivity, thermal stability, and chemical stability, making them suitable for versatile applications both in industry and in biomedicine. This review is highly indicative of the importance of synthesis technique in order to control ferrite properties and, consequently, their specific applications. While synthesizing the materials with consideration of certain properties that help in certain methods of preparation using polyol route, green synthesis, sol-gel combustion, or other wise to tailor make certain properties shown by ferrites, this study also covers biomedical applications of ferrites, including magnetic resonance imaging (MRI), drug delivery systems, cancer hyperthermia therapy, and antimicrobial agents. This was able to inhibit the growth of all tested Gram-negative and positive bacteria as compared with pure ferrite nanoparticles without Co, Mn or Zn doping. In addition, ferrites possess the ability to be used in environmental remediation; such as treatment of wastewater which makes them useful for high-surface-area and adsorption capacity due heavy metals and organic pollutants. A critical analysis of functionalization strategies and possible applications are presented in this work to emphasize the capability of nanoferrites as an aid for the advancement both biomedical technology and environmental sustainability due to their versatile properties combined with a simple, cost effective synthetic methodology.

This study offers a new perspective on measuring the impact of village funds (DD) on rural development. Using a mixed-method approach, the qualitative analysis reveals that, like previous rural development programs, the DD program struggles to implement inclusive methods for capturing community aspirations and evaluating outcomes. Despite rural infrastructure improvement, many villagers feel they have not fully benefited and do not view it as offering economic opportunities. The econometric model confirms the qualitative findings, indicating no significant DD influence on the village development index (IPD). Instead, effective governance factors like Musdes, regulations, and leadership are essential for the IPD improvement. Thus, enhancing village governments’ institutional capacity is crucial for increasing the DD effectiveness. The paper recommends several measures: training village officials in financial management and project planning, providing guidelines for the DD allocation and usage, creating robust monitoring-evaluation systems, developing communication strategies, and fostering partnerships with local NGOs and universities.

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.

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.