The health and wellbeing effects of climate change events have gained much attention from decision makers and academia over the past decade. Using a systematic review approach, this paper aims to present an improved understanding of how different climate change events have impacted on people's health and/or wellbeing. A thorough review of 93 articles following a PRISMA and SALSA protocol revealed nine climate change events, of which heat waves and extreme ambient temperature were found to be closely associated with the most cited illnesses, including physical problems and failure of one's circulatory and respiratory systems. Age and gender are the critical factors among others that differentiate the effects of climate change on health. Although the formulation of heatwave response plans has been adopted in many countries, the findings of this study suggest that design for climate-adaptive built environments is of paramount importance. This paper provides insights into climate change adaption strategies from a health perspective. The findings can be used by disaster risk reduction (DRR) policymakers and practitioners to identify the areas to target in their climate change agenda in order to enhance the adaptive ability of communities.
Small target detection has been widely used in applications that are relevant to everyday life and have many real-time requirements, such as road patrols and security surveillance. Although object detection methods based on deep learning have achieved great success in recent years, they are not effective in small target detection. In order to solve the problem of low recognition rate caused by factors such as low resolution of UAV viewpoint images and little valid information, this paper proposes an improved algorithm based on the YOLOv5s model, called YOLOv5s-pp. First, to better suppress interference from complex backgrounds and negative samples in images, we add a CA attention module, which can better focus on task-specific important channels while weakening the influence of irrelevant channels. Secondly, we improve the forward propagation and generalisation of the network using the Meta-ACON activation function, which adaptively learns to adjust the degree of linearity or nonlinearity of the activation function based on the input data. Again, the SPD Conv module is incorporated into the network model to address the problems of reduced learning efficiency and loss of fine-grained information due to cross-layer convolution in the model. Finally, the detection head is improved by using smaller, smaller-target detection heads to reduce missed detections. We evaluated the algorithm on the VisDrone2019-DET and UAVDT datasets and compared it with other state-of-the-art algorithms. Compared to YOLOv5s, mAP@.5 improved by 7.4% and 6.5% on the VisDrone2019-DET and UAVDT datasets, respectively, and compared to YOLOv8s, mAP@.5 improved by 0.8% and 2.1%, respectively. For improving the performance of the UAV-side small target detection algorithm, it will help to enhance the reliability and safety of UAVs in critical missions such as military reconnaissance, road patrol and security surveillance.
Poor indoor environmental quality (IEQ) has been found to contribute significantly to productivity losses, with the extent of the contribution differing according to the type of office work in which workers are engaged. However, few studies focus specifically on the occupants of university office buildings where the work being undertaken involves a significant amount of academic research that is expected to require high levels of concentration, insight, creativity, and consistency than is needed in many other types of work. To develop a preliminary understanding of the IEQ factors affecting the productivity of people working in university office buildings, a pilot questionnaire was administered to postgraduate students to validate the IEQ factors that have been found to impact on productivity. To date, twelve postgraduate students from three different office buildings in The University of Auckland completed the questionnaire. The results showed that noise, temperature, air quality, and lighting were the factors most reported on with respect to effects on work productivity. The adopted IEQ factors in this questionnaire instrument is reliable. The findings from this study will help advance understanding of the IEQ factors affecting the productivity of workers in university office buildings, and provide insights for architects, building owners, office managers, and office users to help prevent or mitigate negative impacts on productivity by managing the IEQ conditions in workplaces. Future research will involve the analysis of data from staff as well as students to identify any possible differences that might exist between the two groups of workers engaged in academic research.
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