Recent publications
The realization of a sustainable energy transition in Southeast Asia will require the overcoming of the current high reliance on fossil fuels in the energy mix and the steady and rapid growth in energy demand in the region. To achieve an economically, socially, and environmentally sustainable energy transition in the region, it is vital to utilize all available renewable energy sources to the greatest extent possible. It is therefore essential to gain an understanding of how citizens in each country perceive the available renewables. However, the majority of existing literature in this region has been constrained by a narrow focus on a comparison between fossil and renewable energy. Furthermore, while previous research has predominantly concentrated on single-country analyses, there are significant implications that could be disseminated across ASEAN countries. In light of the aforementioned limitations of existing literature, this paper aims to make a contribution by undertaking a comparative analysis of public preferences for renewable energy sources in eight major urban areas in seven Southeast Asian countries. The findings of this study indicate that, while climate change (or global warming) is perceived as a significant issue, it is often regarded as a secondary concern compared to other environmental issues. This is despite the fact that many of these issues are closely interlinked with and would be exacerbated by climate change. Furthermore, the findings suggest that solar energy is the most preferred renewable source across all urban areas surveyed. In contrast, bioenergy (or biomass) is generally the least preferred source of energy in all cities. There is considerable variation in perceptions of wind, hydro, and geothermal energy, with a high degree of variability between and within countries. These similarities and differences in preferences for renewable energy sources appear to be associated with varying levels of knowledge or familiarity with each source, which is consistent with differing willingness to pay for each source. These findings highlight the necessity for policies that address this apparent lack of understanding of how the transition to a sustainable energy system and each renewable energy technology can help mitigate the impact of environmental problems highlighted in each society.
The ongoing global pandemic has underscored the importance of effective preventive measures such as wearing face masks in public spaces. In this paper, we propose a deep learning-based approach for real-time face mask detection to aid in enforcing mask-wearing protocols. Our system utilizes convolutional neural networks (CNNs) to automatically detect whether individuals in images or video streams are wearing masks or not. The proposed system consists of three main stages: face detection, face mask classification, and real-time monitoring. Firstly, faces are localized in the input image or video frame using a proposed face detection model. Then, the detected faces are fed into a proposed CNN model for mask classification, which determines whether each face is covered with a mask or not. Finally, the system will provide real-time monitoring and alerts authorities or stakeholders about non-compliance with mask-wearing guidelines. We evaluate the performance of our system on publicly available datasets and demonstrate its effectiveness in accurately detecting face masks in various scenarios. Additionally, we discuss the challenges and limitations of deploying such a system in real-world settings, including issues related to privacy, bias, and scalability. Overall, our proposed face mask detection system offers a promising solution for automated monitoring and enforcement of face mask policies, contributing to public health efforts in mitigating the spread of contagious diseases.
A novel deep learning-based face mask detection system is designed to enhance public safety in various environments. With the ongoing global health concerns, the need for efficient and accurate methods to identify individuals wearing or not wearing face masks has become crucial. By utilizing convolutional neural networks (CNNs) and transfer learning techniques, proposed model achieves impressive accuracy while maintaining high-speed processing capabilities. This paper outlines the architecture, training process, and performance evaluation of the proposed deep learning-based face mask detection system, highlighting its promising role in contributing to a safer and healthier society. The development of a vision-based safety system, the transfer of a small YOLO object detection model, and the creation of a CNN-based classification model are the key objectives of this study. According to experimental findings, proposed system is capable of real-time face mask detection and classification with an accuracy of over 98%.
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