Green synthesis is an attractive and eco-friendly approach to generate potent antibacterial silver nanoparticles (Ag-NPs). Such particles have long been used to fight bacteria and represent a promising tool to overcome the emergence of antibiotic-resistant bacteria. In this study, green synthesis of Ag-NPs was attempted using plant extracts of Aloe vera, Portulaca oleracea and Cynodon dactylon. The identity and size of Ag-NPs was characterized by ultraviolet–visible spectrophotometer and scanning electron microscopy. Monodispersed Ag-NPs were produced with a range of different sizes based on the plant extract used. The bactericidal activity of Ag-NPs against a number of human pathogenic bacteria was determined using the disc diffusion method. The results showed that Gram positive bacteria were more susceptible than Gram negative ones to these antibacterial agents. The minimum inhibitory concentration was determined using the 96-well plate method. Finally, the mechanism by which Ag-NPs affect bacteria was investigated by SEM analysis. Bacteria treated with Ag-NPs were seen to undergo shrinkage and to lose their viability. This study provides evidence for a cheap and effective method for synthesizing potent bactericidal Ag-NPs and demonstrates their effectiveness against human pathogenic bacteria.

To comply with electric power grid automation strategies, new cyber-security protocols and protection are required. What we now experience is a new type of protection against new disturbances namely cyber-attacks. In the same vein, the impact of disturbances arising from faults or cyber-attacks should be surveyed by network vulnerability criteria alone. It is clear that the diagnosis of vulnerable points protects the power grid against disturbances that would inhibit outages such as blackouts. So, the first step is determining the network vulnerable points, and then proposing a support method to deal with these outages. This research proposes a comprehensive approach to deal with outages by determining network vulnerable points due to physical faults and cyber-attacks. The first point, the network vulnerable points against network faults are covered by microgrids. As the second one, a new cyber-security protocol named multi-layer security is proposed in order to prevent targeted cyber-attacks. The first layer is a cyber-security-based blockchain method that plays a general role. The second layer is a cyber-security-based reinforcement-learning method, which supports the vulnerable points by monitoring data. On the other hand, the trend of solving problems becomes routine when no ambiguity arises in different sections of the smart grid, while it is far from a big network’s realities. Hence, the impact of uncertainty parameters on the proposed framework needs to be considered. Accordingly, the unscented transform method is modeled in this research. The simulation results illustrate that applying such a comprehensive approach can greatly pull down the probability of blackouts.

Chemical pesticides have an immense role in curbing the infection of plant viruses and soil-borne pathogens of high valued crops. However, the usage of chemical pesticides also contributes to the development of resistance among pathogens. Hence, attempts were made in this study to identify a suitable bacterial antagonist for managing viral and fungal pathogens infecting crop plants. Based on our earlier investigations, we identified Bacillus amyloliquefaciens VB7 as a potential antagonist for managing Sclerotinia sclerotiorum infecting carnation, tobacco streak virus infecting cotton and groundnut bud necrosis infecting tomato. Considering the multifaceted action of B. amyloliquefaciens VB7, attempts were made for whole-genome sequencing to assess the antiviral activity against tomato spotted wilt virus infecting chrysanthemum and antifungal action against Fusarium oxysporum f. sp. cubense (Foc). Genome annotation of the isolate B. amyloliquefaciens VB7 was confirmed as B. velezensis VB7 with accession number CP047587. Genome analysis revealed the presence of 9,231,928 reads with an average read length of 149 bp. Assembled genome had 1 contig, with a total length of 3,021,183 bp and an average G+C content of 46.79%. The protein-coding sequences (CDS) in the genome was 3090, transfer RNA (tRNA) genes were 85 with 29 ribosomal RNA (rRNA) genes and 21 repeat regions. The genome of B. velezensis VB7 had 506 hypothetical proteins and 2584 proteins with functional assignments. VB7 genome had the presence of flagellin protein FlaA with 987 nucleotides and translation elongation factor TU (Ef-Tu) with 1191 nucleotides. The identified ORFs were 3911 with 47.22% GC content. Non ribosomal pepide synthetase cluster (NRPS) gene clusters in the genome of VB7, coded for the anti-microbial peptides surfactin, butirosin A/butirosin B, fengycin, difficidin, bacillibactin, bacilysin, and mersacidin the Ripp lanthipeptide. Antiviral action of VB7 was confirmed by suppression of local lesion formation of TSWV in the local lesion host cowpea (Co-7). Moreover, combined application of B. velezensis VB7 with phyto-antiviral principles M. Jalapa and H. cupanioides increased shoot length, shoot diameter, number of flower buds per plant, flower diameter, and fresh weight of chrysanthemum. Further, screening for antifungal action of VB7 expressed antifungal action against Foc in vitro by producing VOC/NVOC compounds, including hexadecanoic acid, linoelaidic acid, octadecanoic acid, clindamycin, formic acid, succinamide, furanone, 4H-pyran, nonanol and oleic acid, contributing to the total suppression of Foc apart from the presence of NRPS gene clusters. Thus, our study confirmed the scope for exploring B. velezensis VB7 on a commercial scale to manage tomato spotted wilt virus, groundnut bud necrosis virus, tobacco streak virus, S. sclerotiorum, and Foc causing panama wilt of banana.

The impressive furtherance of communication technologies has exhorted industrial companies to link-up these developments with their own abilities with the target of efficiency enhancement through smart supervision and control. With this in mind, the blockchain platform is a prospective solution for merging communication technologies and industrial infrastructures, but there are several challenges. Such obstacles should be addressed to effectively adopt this technology. One of the most recent challenges relative to adopting blockchain technology is the energy consumption of miners. Thus, providing an accurate approach that addresses the underlying cause of the problem will carry weight in the future. This work addresses managing the energy consumption of miners by using the advantage of distributed generation resources (DGRs). Along the same vein, it appears that achieving the optimal solution requires executing the modified reconfirmation of DGRs and miners (indeed, mining pool systems) in the smart grid. In order to perform this task, this article utilizes the Intelligent Priority Selection (IPS) method since this method is up to snuff for corporative allocation. In order to find practical solutions for this problem, the uncertainty is also modeled as a credible index highly correlated with the load and generation. All in all, it can be said that the outcome of this research study can help researchers in the field of enhancement of social welfare by using the proposed technology.

Renewable energy-based distributed generators (DGs) are gaining more penetration in modern grids to meet the growing demand for electrical energy. The anticipated techno-economic benefits of these eco-friendly resources require their judicious and properly sized allocation in distribution networks (DNs). The preeminent objective of this research is to determine the sizing and optimal placing of DGs in the condensed DN of a smart city. The placing and sizing problem is modeled as an optimization problem to reduce the distribution loss without violating the technical constraints. The formulated model is solved for a radial distribution system with a non-uniformly distributed load utilizing the selective particle swarm optimization (SPSO) algorithm. The intended technique decreases the power loss and perfects the voltage profile at the system’s nodes. MATLAB is used for the simulation, and the obtained results are also validated by the Electrical Transient Analysis Program (ETAP). Results show that placing optimally sized DGs at optimal system nodes offers a considerable decline in power loss with an improved voltage profile at the network’s nodes. Distribution system operators can utilize the proposed technique to realize the reliable operation of overloaded urban networks.

The biogenic synthesis of silver nanoparticles (AgNPs) using Aloe fleurentiniorum extract and antibacterial activity of the synthesized AgNPs were investigated. Examinations using ultraviolet–visible spectrophotometry, fourier transform infrared spectroscopy, energy-dispersive X-ray spectroscopy, and transmission electron microscopy confirmed the efficacy of AgNP biosynthesis using A. fleurentiniorum extract. Purely prepared AgNPs were spherical and moderately sized. The antibacterial activity of AgNPs was evaluated through the agar well diffusion assay using Staphylococcus aureus, methicillin-resistant S. aureus, Escherichia coli, and Salmonella typhimurium. The biosynthesized AgNPs consistently displayed strong antibacterial activity.