This research analyzes the relationship between political stability, renewable energy utilization, economic progress, and tourism in Indonesia from 1990 to 2020. We employ advanced econometric techniques, including the Fourier Bootstrap Autoregressive Distributed Lag (ARDL) approach and Fourier Toda-Yamamoto causality testing, to ensure the robustness of our results while accounting for smooth structural changes in the data. The analysis uncovers a long-term equilibrium relationship between tourism and its fundamental determinants. Our research reveals significant positive impacts of political stability and renewable energy consumption on tourism in Indonesia. A stable political environment creates a favorable climate for tourism development, instilling confidence in both domestic and international tourists. Promoting renewable energy usage aligns with sustainable tourism practices, attracting environmentally conscious travelers. Furthermore, our findings demonstrate a bi-directional causal relationship between these variables over time. Changes in political stability, renewable energy consumption, and economic growth profoundly influence the tourism sector, while the growth of tourism itself can also stimulate economic development and foster political stability. Our findings underscore the need for environmentally sustainable and politically stable tourism policies. Indonesia’s tourism sector can grow sustainably with renewable energy and stability. Policymakers can develop strategies with tourism, political stability, renewable energy, and economic prosperity in mind.

We develop a relatively cheap technology of processing a scrap in the form of already used tungsten-containing products (spirals, plates, wires, rods, etc.), as well not conditional tungsten powders. The main stages of the proposed W-scrap recycling method are its dispersing and subsequent dissolution under controlled conditions in hydrogen peroxide aqueous solution resulting in the PTA (PeroxpolyTungstic Acid) formation. The filtered solution, as well as the solid acid obtained by its evaporation, are used to synthesize various tungsten compounds and composites. Good solubility of PTA in water and some other solvents allows preparing homogeneous liquid charges, heat treatment of which yield WC and WC–Co in form of ultradispersed powders. GO (Graphene Oxide) and PTA composite is obtained and its phase transition in vacuum and reducing atmosphere (H₂) is studied. By vacuum-thermal exfoliation of GO–PTA composite at 170–500℃ the rGO (reduced GO) and WO_2.9 tungsten oxide are obtained, and at 700℃—rGO–WO₂ composite. WC, W₂C and WC–Co are obtained from PTA at high temperature (900–1000℃). By reducing PTA in a hydrogen atmosphere, metallic tungsten powder is obtained, which was used to obtain sandwich composites with boron carbide B₄C, W/B₄C, and W/(B₄C–W), as neutron shield materials. Composites of sandwich morphology are formed by SPS (Spark-Plasma Sintering) method.

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 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.

The quest for quality postgraduate research productivity through education is on the increase. However, in the context of the African society, governance structures and policies seem to be impacting on the quality level of the provided education. Hence, this conceptual study explored the roles of governance structures and policies in enhancing and ensuring quality postgraduate education programmers in African institutions of higher learning. To this end, various relevant literature was reviewed. The findings showed amongst others that governance structures and policies affect the quality of education provided. Meanwhile, other factors such as curriculum, foreign influence, lack of resources, training, amongst others contribute to the quality of education provided. The study concludes that there is need for the current structures of governance and the designed and implemented policies for postgraduate education to be reviewed and adjusted towards ensuring the desired transformation.

This study aims to use dialectical thinking to explore the impacts and responses of Artificial Intelligence (AI) empowerment on students’ personalized learning. The effect of AI empowerment on student personalization is dissected through a literature review and empirical cases. The study finds that AI plays a significant role in promoting personalized learning by enhancing students’ learning effectiveness through intelligent recommendation, automated feedback, improving students’ independent learning ability, and optimizing learning paths, however, the wide application of AI also brings problems such as technological dependence, cheating in exams, weakening of critical thinking ability, educational fairness, and data privacy protection to students. The study proposes recommendations to strengthen technology regulation, enhance the synergy between teachers and AI, and optimize the personalized learning model. AI-enabled personalized learning is expected to play a greater role in improving learning efficiency and educational fairness.