RESUMEN
Electricity theft presents a significant financial burden to utility companies globally, amounting to trillions of dollars annually. This pressing issue underscores the need for transformative measures within the electrical grid. Accordingly, our study explores the integration of block chain technology into smart grids to combat electricity theft, improve grid efficiency, and facilitate renewable energy integration. Block chain's core principles of decentralization, transparency, and immutability align seamlessly with the objectives of modernizing power systems and securing transactions within the electricity grid. However, as smart grids advance, they also become more vulnerable to attacks, particularly from smart meters, compared to traditional mechanical meters. Our research aims to introduce an advanced approach to identifying energy theft while prioritizing user privacy, a critical aspect often neglected in existing methodologies that mandate the disclosure of sensitive user data. To achieve this goal, we introduce three distributed algorithms: lower-upper decomposition (LUD), lower-upper decomposition with partial pivoting (LUDP), and optimized LUD composition (OLUD), tailored specifically for peer-to-peer (P2P) computing in smart grids. These algorithms are meticulously crafted to solve linear systems of equations and calculate users' "honesty coefficients," providing a robust mechanism for detecting fraudulent activities. Through extensive simulations, we showcase the efficiency and accuracy of our algorithms in identifying deceitful users while safeguarding data confidentiality. This innovative approach not only bolsters the security of smart grids against energy theft, but also addresses privacy and security concerns inherent in conventional energy-theft detection methods.
RESUMEN
Plants integrate and monitor low temperature signals to cope with the continual variations in their environment. Arabidopsis thaliana cold responsive-element binding factor 3 (AtCBF3) plays its role in various cellular activities by modulating multiple genes induced under chilling stress. In this work, AtCBF3 transcription was remarkably induced following chilling stress. AtCBF3-overexpressors namely AtCBF3-Rio Grande, AtCBF3-Moneymaker, and AtCBF3-Roma showed defensible response to various levels of chilling stress, while their isogenic wild type plants indicated hypersensitive response to chilling stress. Detailed photosynthetic studies revealed that AtCBF3 gene has harmonious influences on the expression of a large set of genes by virtue of improved stomatal conductance, transpiration rate, intercellular CO2 concentration, and photosynthetic rate compared to wild type plants. The AtCBF3 lines limited the water status-mediated hypersensitive response by lowering leaf osmotic potential due to overexpression of AtCBF3 under chilling stress. Biochemical analyses followed by phenotypic studies demonstrated that AtCBF3 plants exhibited membrane stability and lush green appearance by limiting membrane ions leakage and malondialdehyde contents and by accumulating more proline, soluble sugars, chlorophyll contents, carotenoid contents, and antioxidant enzymes relative to wild type plants. Hence, with a several lines of evidence, these findings support that tomato transgenic plants overexpressing Arabidopsis CBF3 show enhanced chilling tolerance.