Articles

Ultraviolet (UV) photo detectors have become essential for applications ranging from military and space exploration to environmental monitoring and secure communications. Traditional semiconductors such as silicon and GaN face limitations in achieving solar blindness, environmental stability, and cost-effective scalability. In contrast, metal oxide semiconductors—with their wide band gaps, chemical robustness, and highly tunable nanostructures—have emerged as promising candidates for next-generation UV sensing. This review highlights the role of nanostructure design and interface engineering in advancing metal oxide–based UV photo detectors. We examine how morphology, dimensionality, and defect states influence optical detection mechanisms, and how engineered interfaces—including Schottky contacts, heterojunctions, and hybrid architectures—enable improved responsivity, selectivity, and response speed. After presenting comparative performance benchmarks, we discuss emerging strategies such as alloying, hybrid integration, and flexible device architectures. Finally, we identify key challenges related to device stability, scalability, and reproducibility, and offer perspectives on the future of metal oxide nanotechnology for UV sensing.

Elliptic Curve Cryptography (ECC) has emerged as a fundamental element of contemporary digital security, providing strong protection with shorter key sizes and reduced computational requirements compared to traditional public-key systems like Rivest-Shamir-Adleman algorithm (RSA). Nevertheless, recent progress in machine learning, especially deep learning (DL), has presented new possibilities and challenges for ECC. Deep learning can be utilized both to enhance cryptographic calculations and to simulate potential attacks, including side-channel and fault-based vulnerabilities. This paper offers an in-depth performance evaluation of deep learning-based methods applied to ECC, concentrating on their efficacy, computational efficiency, and effects on cryptographic robustness. By examining recent research and experimental results, author assess various DL frameworks, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, in the context of ECC-related tasks.

The construction and demolition (C&D) waste is one of the biggest forms of waste in the world and it accounts for almost 30-40% of the total solid waste. The rising demands on the natural resources, urbanization and climate change have led to the cities adopting the linear model of consuming their resources to the circular construction of the cities. In this paper, the author includes a detailed overview of the global and regional practice on sustainable management and valorization of C&D waste, particularly in terms of urban infrastructure. It examines new models that combine recycling, industrial symbiosis, and life cycle assessment to bring about circularity in construction. The methodology of the study is a synthesis of the results of the empirical case studies and systematic literature reviews that were performed in 2018-2025. Findings reveal that successful segregation, implementation of controls and collaborating with stakeholders are key facilitators to material recovery and reuse. Energy recovery processes, re-use aggregates and geopolymer technologies are highly promising to decrease the embodied carbon and dependency on landfills. The analysis also indicates geographical voids especially in developing countries where informal sectors prevail in waste streams. Finally, sustainable C&D waste management makes materials more efficient, minimizes emissions of greenhouse gases, and increases the resilience of urban infrastructure, thus achieving the United Nations sustainable development goals (SDGs) 11 and 12.