Revamping the power grid into a smart grid and modernizing it with advanced metering infrastructure are essential steps in addressing ongoing energy challenges. Smart meters play a pivotal role in power grid modernization by providing real-time energy-related data which fuels the control activities of modern grid. While the advantages of smart meters are evident, their deployment necessitates a comprehensive redesign of the grid architecture, involving smart end devices for monitoring and communication networks for efficient data exchange. Yet, achieving cost-effective and widespread adoption of these technologies poses a challenge, particularly in developing and underdeveloped nations due to high capital costs, technological constraints and uneconomical deployment strategies. Moreover, the prevailing research often advocates a complete transition to new smart meters to achieve 'smartness,' neglecting the potential of existing metering infrastructure upgrades. To address these concerns, this study proposes and simulates the design of a low-cost Smart Network Meter. Notably, this meter upgrades the existing meter infrastructure while validating a cost-effective deployment strategy. Furthermore, a consumer opinion survey was also conducted to compelling evidence supporting the adoption of the proposed low-cost smart metering solution.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636099 | PMC |
http://dx.doi.org/10.1038/s41598-023-44149-9 | DOI Listing |
Bot Stud
January 2025
Crop Science Division, Taiwan Agricultural Research Institute, Ministry of Agriculture, Taichung, 413, Taiwan.
Background: Rice is a staple food for the global population. However, extreme weather events threaten the stability of the water supply for agriculture, posing a critical challenge to the stability of the food supply. The use of technology to assess the water status of rice plants enables the precise management of agricultural water resources.
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December 2024
Professor, Department of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ 85721, USA.
Smart meters such as advanced metering infrastructure (AMI) can significantly improve identifying realistic sized leaks in water distribution networks (WDNs). However, to date, detection/localization methods for AMI systems are extremely limited. In this study, to examine the benefits of using AMIs for leak detection within distribution network, a three-dimensional (3D) convolutional neural network (CNN) deep learning (DL) model is proposed that can account for temporally and spatially distributed information of pressures.
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January 2025
Sustainability/Net-Zero Office, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
Anal Chim Acta
January 2025
MOE Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection Technology for Food Safety, College of Chemistry, Fuzhou University, Fuzhou, 350116, PR China. Electronic address:
Flap endonuclease 1 (FEN1) plays a vital role in cancer by modulating DNA repair mechanisms, inducing genomic instability, and serving as a promising biomarker for cancer diagnosis and prognosis. In this work, we present the development of a novel DNAzyme signal amplification-directed point-of-care sensing system (Dz-PGM) for the sensitive and specific detection of FEN1. The Dz-PGM system utilizes DNAzyme signal amplification in conjunction with a personal glucose meter (PGM) for reporting, capitalizing on a biochemical cascade initiated by FEN1 recognition.
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December 2024
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40127 Bologna, Italy.
Temporal parameters are crucial for understanding running performance, especially in elite sports environments. Traditional measurement methods are often labor-intensive and not suitable for field conditions. This study seeks to provide greater clarity in parameter estimation using a single device by comparing it to the gold standard.
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