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Predictive maintenance, facilitated by smart devices and cyber infrastructure, is used for essential equipment like power transformers, enhancing power grid stability and reducing operating costs. As part of predictive maintenance, machine learning (ML) methods are employed to predict the remaining useful life (RUL) of power transformers, which can be vulnerable to cyber-attacks, especially data contamination attacks. Hence, this work introduces false data injection (FDI) attacks in ML-based RUL prediction and investigates the impacts on lifetime prediction. Three different attack templates of FDI attacks are implemented to corrupt the input data of extreme gradient boosting (XGBoost), extra trees (ETs) and random forest (RF)-based lifetime predictor models, where single attack templates are found severe compared to a mixed attack template of FDI attacks. Also, adversarial training is presented as a countermeasure, where the adversarially trained XGBoost model outperforms the other two models under normal conditions and cyber-attacks. Experiment results indicate that the lifetime prediction errors of the proposed model in all scenarios can be maintained at about 6 and 3 in terms of RMSE and MAE, respectively. • Introducing cyber-attacks on ML-based power transformer lifetime predictors. • Investigating the severity of single and mixed models of FDI attacks on predictors. • Presenting a comparative analysis of predictors under normal cases and cyber-attacks. • Proposing an adversarial training-based countermeasure to mitigate the severity. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa) stands as a crucial staple food worldwide, especially in Bangladesh, where it ranks as the third-largest producer. However, intensified cultivation has made high-yielding rice varieties susceptible to various biotic stresses, notably sheath blight caused by Rhizoctonia solani, which inflicts significant yield losses annually. Traditional fungicides, though effective, pose environmental and health risks. To address this, nanotechnology emerges as a promising avenue, leveraging the antimicrobial properties of nanoparticles like silver nanoparticles (AgNPs). This study explored the green synthesis of AgNPs using Ipomoea carnea leaf extract and silver nitrate (AgNO3), and also examined their efficacy against sheath blight disease in rice. The biosynthesized AgNPs were characterized through various analytical techniques such as UV-vis spectrophotometer, X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Particle size analyzer, Zeta potential, Scanning Electron Microscope (SEM), Field Emission Scanning Electron Microscope (FESEM), Transmission Electron Microscope (TEM) for confirming their successful production and crystalline nature of nanoparticles. The results of UV-visible spectrophotometers revealed an absorption peak ranging from 421 to 434 nm, validated the synthesis of AgNPs in the solution. XRD, DLS, and TEM estimated AgNPs sizes were ~45 nm, 66.2nm, and 46.38 to 73.81 nm, respectively. SEM and FESEM demonstrated that the synthesized AgNPs were spherical in shape. In vitro assays demonstrated the significant inhibitory effects of AgNPs on mycelial growth of Rhizoctonia solani, particularly at higher concentrations and pH levels. Further greenhouse and field experiments validated the antifungal efficacy of AgNPs against sheath blight disease in rice, exhibiting comparable effectiveness to commercial fungicides. The findings highlight the potential of AgNPs as a sustainable and effective alternative for managing rice sheath blight disease, offering a safer solution amidst environmental concerns associated with conventional fungicides. [ABSTRACT FROM AUTHOR]

Microgrids are designed to utilize renewable energy resources (RER) that are revolutionary choices in reducing the environmental effect while producing electricity. The RER intermittency poses technical and economic challenges for the microgrid systems that can be overcome by utilizing the full potential of hybrid energy storage systems (HESS). A microgrid comprising of a solar photovoltaic panel, wind turbine, lead-acid battery, electrolyzer, fuel cell, and hydrogen (H2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$_{2}$$\end{document}) tank is considered for techno-economic feasibility and environmental impact assessment on a grid integration scenario. Mathematical functions are utilized to model the components for estimating annual hourly renewable generation and energy storage behavior. The load consumption model for 50 homes is generated using Gaussian distribution to incorporate the uncertainty. Optimal sizing of the microgrid components is determined using the particle swarm optimization (PSO) algorithm to minimize the upfront installation cost and levelized cost of energy (LCOE). Different energy storage penetration scenarios, e.g., 25%, 50%, 75%, and 100% for the microgrid system, are considered where 100% penetration level stands for maintaining the load demand using the available resources without depending on the grid energy supply. The lowest LCOE is found between 0.06 $/kWh and 0.11 $/kWh, and the highest annual GHG is reduced to half compared to the grid emission. GHG is imposed around 62.14 (tCO2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$_2$$\end{document}e/yr) - 73.57 (tCO2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$_2$$\end{document}e/yr) for Madrid and Seville, respectively. [ABSTRACT FROM AUTHOR]

The repair and regeneration of the injured tissues or organs is a major challenge for biomedicine, and the emerging 3D bioprinting technology as a class of promising techniques in biomedical research for the development of tissue engineering and regenerative medicine. Chitosan-based bioinks, as the natural biomaterials, are considered as ideal materials for 3D bioprinting to design and fabricate the various scaffold due to their unique dynamic reversibility and fantastic biological properties. Our review aims to provide an overview of chitosan-based bioinks for in vitro tissue repair and regeneration, starting from modification of chitosan that affect these bioprinting processes. In addition, we summarize the advances in chitosan-based bioinks used in the various 3D printing strategies. Moreover, the biomedical applications of chitosan-based bioinks are discussed, primarily centered on regenerative medicine and tissue modeling engineering. Finally, current challenges and future opportunities in this field are discussed. The combination of chitosan-based bioinks and 3D bioprinting will hold promise for developing novel biomedical scaffolds for tissue or organ repair and regeneration. [ABSTRACT FROM AUTHOR]

With technological developments, the resolution of the display systems is increasing, causing image data to increase. Several technologies are aiding in optimizing these data during transmission and storage while maintaining quality. Still, the internal data transmission for accessing the memory sub‐system during reading and writing cycles consumes significant power while requiring large memory for storage, which is inefficient, especially for portable displays. Hence, this work investigates the feasibility of reducing data through approximation for images in different formats and resolutions for human and computer vision based applications. Different image quality assessment metrics are utilized for performance evaluations with optimum image quality assessment selection according to application requirements. In addition, the feasibility analysis is conducted for three different applications as examples of computer vision. The experimental analysis highlights theoretical and industrial importance by showing that image data can be reduced from 25.0% to 62.5%, while satisfying requirements for HVS‐based and computer vision‐based applications. [ABSTRACT FROM AUTHOR]

Rice blast and bacterial blight diseases are the global threat for rice production. To develop blast and bacterial blight resistant lines, two blast resistance genes (Pi9 and Pb1) and three bacterial blight resistance genes (xa5, xa13 and Xa21) were pyramided in the background of premium quality rice variety BRRI dhan63 through marker-assisted backcross breeding. Pi9-US2, Pb1-US2 were used as donor parents for Pi9 and Pb1, respectively and for xa5, xa13 and Xa21, IRBB60 was used as a donor parent. Three crosses were made to pyramid these genes in BRRI dhan63. F1 to BC3F6 population was developed by continuous backcrossing, selfing and foreground selection. Segregation analysis (chi-square test) of genotypic and phenotypic data of 400 BC3F2 population revealed that the inheritance of blast and bacterial blight resistance followed the single gene Mendelian fashion. Finally, a total of 30 advanced lines were selected and out of them, 23 lines contained Xa4, xa5, Xa21, Pi9 and Pb1 genes, six lines contained Pi9, Xa4 and Xa21 genes and a single line contained Pi9, Xa4, xa5, xa13 and Xa21 genes. Disease score of 30 advanced lines ranged from 0 to 1 for both blast and bacterial blight disease whereas in susceptible BRRI dhan63 disease score ranged from 7 to 9. Among the advanced lines, BR(Path)13,811-BC3-13 had the highest yield (30.41 g) per hill followed by BR(Path)13,811-BC3-18 (29.37 g), BR(Path)13,811-BC3-40 (29.08 g) and BR(Path)13,811-BC3-29 (28.66 g). Markers of the present study could be employed in marker-assisted selection breeding and the pyramided lines of the present study could be released as resistant varieties or used as resistant donors in the breeding program to develop blast and bacterial blight resistant varieties. [ABSTRACT FROM AUTHOR]