Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

service-learning activities are effective in higher education programmes. During the COVID-19 period, universities have implemented this methodology and students have improved their social and practical skills. The purpose of this article is to analyse the role of higher education in the process of adapting teaching based on experiences designed for students to serve the community. This research presents the results of a specific service-learning experience of 35 students from the Teamworking programme during the academic years 2020–21 (online) and 2021–22 (face-to-face), in collaboration with the San Juan de Dios Foundation in Madrid, which provides care for people with disabilities. Students evaluated the experience through a quantitative study based on a questionnaire previously developed by Folgueiras (2013), divided into four dimensions. Students also provided some feedback, explaining that this experience changed their perception of people with disabilities, considering the personal value of contributing to social inclusion through service learning. The results show that through the Folgueiras model, students have strengthened their social skills and competences, and through an applied training project that offers the opportunity to build a real relationship through different activities, where learning was at the centre of the interaction between students and young people with disabilities. In conclusion, although the evaluation was positive in terms of the students’ professional and human development, this project requires continuous improvement in the long term, since the subjectivity of human relationships follows a dynamic course with variables that are sensitive to time and individual experience.

In the present and future of education, fostering complex thinking, especially in the context of the Sustainable Development Goals (SDGs), is critical to lifelong learning. This study aimed to analyze learning scenarios within the framework of a model that promotes complex thinking and integrated design analysis, to identify the contributions of linking design models to the SDGs. The research question was: How does the open educational model of complex thinking link to the SDGs and scenario design? The analysis examined a pedagogical approach that introduced 33 participants to the instructional design of real-life or simulated situations to develop complex thinking skills. The categories of analysis were the model components, the SDGs, and scenario designs. The findings considered (a) innovative design capacity linked to SDG challenges, (b) linking theory and practice to foster complex thinking, and (c) the critical supporting tools for scenario design. The study intends to be of value to academic, social, and business communities interested in mobilizing complex thinking to support lifelong learning.

Primary reason for interpretation the paper was the creation of a starting position for setting up e-learning in the structures of the executive forces of the Slovak Republic, which absent in the current dynamic environment. Problems with education arose mainly in connection with the global problem of Europe, such as the influence of illegal migrants, and it was necessary to retrain a large number of police officers in a short time. We reflect on the combined model of LMS Moodle and proctored training through MS TEAMS and their active use in practice. We focused on the efficiency in the number of participants in individual trainings and costs per participant according to the field of training. We compared the processed data with the costs of the pilot introduction of analytical organizational unit providing e-learning and interpreted the positive results in the application of e-learning compared to conventional (face-to-face) educational activities. As a basic (reference) comparative indicator, the costs of educational activities of selected organizational unit of state institution represented by own educational organizations and the number of trained employees for the periods in question were chosen. To measure effectiveness, we set financial—cost KPIs. Our findings clearly demonstrated that it is possible to significantly optimize costs when changing the current form of ICT education to e-learning. The implementation of another educational activities form of education, e-learning, within public institutions, according to the results of the analysis, can simplify and at the same time make education processes more efficient in the context of individual subjects of the Ministry of the Interior of the Slovak Republic.