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The research progress of AlGaN ultraviolet light-emitting diodes (UV LEDs) is explored in this paper. Firstly, the significance and application areas of AlGaN UV LEDs are introduced, emphasizing their wide-ranging applications in fields such as disinfection, medical devices, and sensors. Subsequently, an overview of the material science and manufacturing processes used in the production of AlGaN UV LEDs is provided, including crystal growth techniques and fabrication processes. Next, the design, structure, and operating principles of different types of AlGaN UV LEDs are described, with a focus on the devices optical performance, electrical characteristics, and thermal management effects. Finally, the latest research findings are summarized, and the challenges and future development directions facing current technology are discussed. This paper aims to provide readers with a comprehensive understanding of the research field of AlGaN UV LEDs and serve as a reference for future research and applications.

With the increasing global demand for safe drinking water, the search for effective and economical water treatment technologies has become an urgent need. Ultraviolet (UV) light sterilization is a proven method for eliminating bacteria in water, but traditional UV systems often rely on mercury lamps, which pose potential environmental and health risks. In recent years, the development of UV-LED technology has offered a new solution for water treatment. This article provides a detailed explanation of the working principles, technological advantages, and applications of UV-LEDs in water treatment. By comparing them with traditional UV systems, UV-LEDs not only demonstrate lower energy consumption and longer lifespan, but also avoid the use of toxic mercury. Despite current challenges in cost and technological maturity, the environmental friendliness and efficiency potential of UV-LED technology indicate a promising market outlook. This article aims to provide a comprehensive review of UV-LED technology for practitioners and researchers in the water treatment industry, and to explore its future development directions.

While universities routinely use student data to monitor and predict student performance, there has been limited engagement with student and staff views, social and ethical issues, policy development, and ethical guidance. We reviewed peer-reviewed and grey-literature articles of 2007 to 2018 describing the perspectives of staff and students in tertiary education on the use of student-generated data in data analytics, including learning analytics. We used an ethics framework to categorize the findings. There was considerable variation but generally low awareness and understanding amongst students and staff about the nature and extent of data collection, data analytics, and use of predictive analytics. Staff and students identified potential benefits but also expressed concerns about misinterpretation of data, constant surveillance, poor transparency, inadequate support, and potential to impede active learning. This review supports the contention that consideration of ethical issues has failed to keep pace with the development of predictive analytics in the tertiary sector.

Recently, the low-rank representation (LRR) model has been widely used in the field of remote sensing image denoising due to its excellent noise suppression capability. However, those low-rank-based methods always discard important edge details as residuals, leading to a common issue of blurred edges in denoised results. To address this problem, we take a new look at low-rank residuals and try to extract edge information from them. Therefore, a hierarchical denoising framework was combined with a low-rank model to extract edge information from low-rank residuals within the edge subspace. A prior knowledge matrix was designed to enable the model to learn necessary structural information rather than noise. Also, such traditional model-driven approaches require multiple iterations, and the solutions may be very complex and computationally intensive. To further enhance the noise suppression performance and computing efficiency, a hierarchical low-rank denoising model based on deep unrolling (HLR-DUR) was proposed, integrating deep neural networks into the hierarchical low-rank denoising framework to expand the information capture and representation capabilities of the proposed shallow model. Sufficient experiments on optical images, hyperspectral images (HSI), and synthetic aperture radar (SAR) images showed that HLR-DUR achieved state-of-the-art (SOTA) denoising results. [ABSTRACT FROM AUTHOR]

Aims and Objectives: To investigate the use of physical restraints in aged care facilities(ACFs)and analyse its associated risk factors. Background: Physical restraints have been widely used in ACFs worldwide, but they can cause physical and mental harm to older people. It is important to regulate the use of physical restraint. Design: A cross‐sectional observational and correlational multicentre study. Methods: By convenience sampling method, we selected eight ACFs in four representative regions of Hunan province, China, for this study. The ACF‐related information was obtained by interviewing the managers and reviewing records. We conducted investigation and observation on the elderly in the ACFs to understand the use of physical restraints at three different times: 9:30–11:30, 16:00–18:00 and 19:30–21:30 on a working day. The STROBE checklist was followed for this cross‐sectional study. Results: This study found that the utilisation rate of physical restraints was 23.2%. The critical risk factors affecting the use of physical restrains include the following: (1) the ratio of nursing staff to the elderly residents; (2)whether there is a dementia care unit at the facility; (3) the number of elderly residents in each room; (4) the elderly residents' age, degree of education, marital status, care dependence and cognitive impairment; (5) whether the elderly has suffered from a stroke or senile dementia; (6) whether the elderly carries medical catheters. Conclusion: There is a lack of standardisation in the use of physical restraints in ACFs of central China. Chinese ACFs should develop guidelines and reduction measures to standardise the use of physical restraints, basing on the key factors affecting the use of physical restraints. Relevance to clinical practice: The use of physical restraints in ACFs is threatening the safety of the elderly residents. Understanding the implementation of physical restraint in ACFs can provide reference for reducing the use of physical restraint. [ABSTRACT FROM AUTHOR]