The integration of solar energy into the global energy mix is more prevalent owing to its sustainability and ecological advantages. With the fast expansion of large-scale solar farms, the necessity for efficient and dependable maintenance and monitoring of solar modules has grown significantly. This study advocates for the utilization of unmanned aerial vehicles (UAVs) outfitted with thermal imagers and visible-light cameras as an efficient method for identifying flaws in solar photovoltaic (PV) modules. In contrast to conventional inspection techniques, UAV-based photography provides continuous, real-time surveillance, facilitating the rapid detection of potential defects such as cell malfunctions, fractures, shading, and string failures, which may result in substantial financial losses. The choice to prioritize thermal and visual imaging above mathematical modeling or optimization algorithms arises from the necessity of delivering practical, scalable inspection solutions that can be readily implemented in the field. Our review paper emphasizes instant functionality and practical insights for predictive maintenance, without necessitating complex computing resources or optimization tools. This study emphasizes the significance of early defect identification on solar farm efficiency and delineates a strategy for improving the reliability and cost-effectiveness of photovoltaic module inspections via UAV technology.

The integration of Generative AI (GenAI) technology in education is set to transform traditional teaching and learning models. The age of teacher-led instructional pedagogy, where teachers are the sole providers of knowledge and students being mere receivers, is long outdated. Teachers are confronted with the choice of embracing new Artificial Intelligence (AI) technologies or risking obsolescence, potentially falling behind a new generation of AI-skilled educators. To maximise the benefits, teachers and the new generation of AI tools must coexist in a collaborative relationship, underscoring a new model of pedagogy for the new age. This paper proposes a collaborative model where GenAI and teachers are envisioned to work together to optimise educational outcomes of current and future learners. Beginning with a review of recent trends of AI integration in education, we describe the proposed model that we envision will underscore the roles of teachers and GenAI technology in future teaching and learning scenarios. Innovatively, we categorise the degree of involvement—minor, medium, major—by teachers and GenAI in developing learners' cognitive, psychomotor, and affective skills, as informed by Bloom's taxonomy. This categorisation underpins how GenAI and human collaboration can effectively enhance various learner skills. Additionally, key issues including privacy, bias, emotional impacts, and job displacement are discussed, alongside opportunities for improving education quality and accessibility. We argue that aspects of our model have started to appear in educational scenarios in countries including Australia, USA, and China. We conclude by highlighting the implications and impacts of rapid and sustained advancements of AI will have on current and future educational policies and the frontiers for educational research.

This research work is organized in two sections. Performance comparison of single phase half bridge inverter and single phase full bridge inverter is done in the first section. It is observed the output current and output voltage of full bridge inverter is twice and generates less total harmonic distortion as compared to half bridge inverter. In the second section, performance comparison of Unipolar and Bipolar PWM is presented for single phase full bridge inverter with and without filter in MATLAB SIMULINK. The proper choice of Frequency Modulation Ratio (m_f) is discussed in detail for bipolar and unipolar PWM scheme. Non integer value of mf results in sub-harmonics which are nearly unfilterable and cause significant losses in any power system application. It is observed that Unipolar PWM has an advantage in terms of efficiency; it offers less switching losses and generates less EMI. Also, the switching frequency becomes twice and voltage amplitude is reduced to half in case of unipolar PWM. Therefore, total harmonic distortion (THD) content of output current and output voltage is decreased, thus improving the power quality and making filter easier.

Usually, railway accidents are caused by train derailment, the mechanical failure of tracks, such as broken rails often caused by lack of railway condition monitoring. Such monitoring could identify track surface faults, such as squats, that act as a catalyst for the track to crack and ultimately break. The research presented in this paper enables real-time identification of railway track faults using image processing techniques such as Canny edge detection and 2D discrete wavelet transformation. The Canny edge detection outperforms traditional track damage detection techniques including Axle Based Acceleration using Inertial Measurement Units and is as reliable as Fiber Bragg Grating. The Canny edge detection employed can identify squats in real-time owing to its specific threshold amplitude using a camera module mounted on a specially designed handheld Track Recording Vehicle (TRV). The 2D discrete wavelet transformation validates the insinuation of the Canny edge detector regarding track damage and furthermore determines damage severity, by applying high sub band frequency filter. The entire algorithm works on a Raspberry Pi 3 B+ utilizing an OpenCV API. When tested using an actual rail track, the algorithm proved reliable at determining track surface damage in real-time. Although wavelet transformation performs better than Canny edge detection in terms of determining the severity of track surface damage, it has processing overheads that become a bottleneck in real-time. To overcome this deficiency a very effective two-stage process has been developed.

This paper explores how generative artificial intelligence (GenAI) technologies, such as ChatGPT 4o and other AI-based conversational models, can be applied to flipped learning pedagogy to achieve enhanced learning outcomes for students. By applying Bloom’s taxonomy to intentionally align educational objectives to the key phases of flipped learning, our study proposes a model for assigning learning activities to pre-class, in-class, and post-class contexts that can be enhanced by the integration of GenAI. In the pre-class phase, GenAI tools can facilitate personalised content delivery, enabling students to grasp fundamental concepts at their own pace. During class, the interactions between students, teacher, and GenAI encourage collaborative learning and real-time feedback. Post-class activities utilise GenAI to reinforce knowledge, provide instant feedback, and support continuous learning through summarisation and content generation. Furthermore, our model articulates the synergies between the three key actors: interactions between students and teachers, learning support provided by GenAI to students, and use of GenAI by teachers to enhance their teaching strategies. These human–AI interactions fundamentally reshape the flipped learning experience, making it more adaptive, engaging, and supportive of the development of 21st-century skills such as critical thinking, collaboration, communication, and creativity.