Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems

Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management

Reliability and Reproducibility of Landmark Identification in Unilateral Cleft Lip and Palate Patients: Digital Lateral Vis-A-Vis CBCT-Derived 3D Cephalograms

Background: The aim of the retrospective observational study was to compare the precision of landmark identification and its reproducibility using cone beam computed tomography-derived 3D cephalograms and digital lateral cephalograms in unilateral cleft lip and palate patients. Methods: Cephalograms of thirty-one (31) North Indian children (18 boys and 13 girls) with a unilateral cleft lip and palate, who were recommended for orthodontic treatment, were selected. After a thorough analysis of peer-reviewed articles, 20 difficult-to-trace landmarks were selected, and their reliability and reproducibility were studied. These were subjected to landmark identification to evaluate interobserver variability; the coordinates for each point were traced separately by three different orthodontists (OBA, OBB, OBC). Statistical analysis was performed using descriptive and inferential statistics with paired t-tests to compare the differences measured by the two methods. Real-scale data are presented in mean ± SD. A p-value less than 0.05 was considered as significant at a 95% confidence level. Results: When comparing, the plotting of points posterior nasal spine (PNS) (p < 0.05), anterior nasal spine (ANS) (p < 0.01), upper 1 root tip (p < 0.05), lower 1 root tip (p < 0.05), malare (p < 0.05), pyriforme (p < 0.05), porion (p < 0.01), and basion (p < 0.05) was statistically significant. Conclusion: In patients with a cleft lip and palate, the interobserver identification of cephalometric landmarks was significantly more precise and reproducible with cone beam computed tomography -derived cephalograms vis-a-vis digital lateral cephalograms

A sensor platform for non-invasive remote monitoring of older adults in real time

The population of older adults is increasing across the globe; this growth is predicted to continue into the future. Most older adults prefer to live in their own home, but many live alone without immediate support. Living longer is often coupled with health and social problems and difficulty managing daily activities. Therefore, some level of care is required, but this is costly. Technological solutions may help to mitigate these problems by recognising subtle changes early and intervening before problems become unmanageable. Understanding a personâ s usual behaviour when carrying out Activities of Daily Living (ADL) makes it possible to detect and respond to anomalies. However, current commercial and research monitoring systems do not offer an analysis of ADL and are unable to detect subtle changes. To address this gap, we propose the STRETCH (Socio-Technical Resilience for Enhancing Targeted Community Healthcare) sensor platform that is comprised of non-invasive sensors and machine learning techniques to recognise changes and allow early interventions. The paper discusses design principles, modalities, system architecture, and sensor network architecture

Microbial diversity, genomics, and phage–host interactions of cyanobacterial harmful algal blooms

ABSTRACT The occurrence of cyanobacterial harmful algal blooms (cyanoHABs) is related to their physical and chemical environment. However, less is known about their associated microbial interactions and processes. In this study, cyanoHABs were analyzed as a microbial ecosystem, using 1 year of 16S rRNA sequencing and 70 metagenomes collected during the bloom season from Lake Okeechobee (Florida, USA). Biogeographical patterns observed in microbial community composition and function reflected ecological zones distinct in their physical and chemical parameters that resulted in bloom “hotspots” near major lake inflows. Changes in relative abundances of taxa within multiple phyla followed increasing bloom severity. Functional pathways that correlated with increasing bloom severity encoded organic nitrogen and phosphorus utilization, storage of nutrients, exchange of genetic material, phage defense, and protection against oxidative stress, suggesting that microbial interactions may promote cyanoHAB resilience. Cyanobacterial communities were highly diverse, with picocyanobacteria ubiquitous and oftentimes most abundant, especially in the absence of blooms. The identification of novel bloom-forming cyanobacteria and genomic comparisons indicated a functionally diverse cyanobacterial community with differences in its capability to store nitrogen using cyanophycin and to defend against phage using CRISPR and restriction-modification systems. Considering blooms in the context of a microbial ecosystem and their interactions in nature, physiologies and interactions supporting the proliferation and stability of cyanoHABs are proposed, including a role for phage infection of picocyanobacteria. This study displayed the power of “-omics” to reveal important biological processes that could support the effective management and prediction of cyanoHABs.IMPORTANCECyanobacterial harmful algal blooms pose a significant threat to aquatic ecosystems and human health. Although physical and chemical conditions in aquatic systems that facilitate bloom development are well studied, there are fundamental gaps in the biological understanding of the microbial ecosystem that makes a cyanobacterial bloom. High-throughput sequencing was used to determine the drivers of cyanobacteria blooms in nature. Multiple functions and interactions important to consider in cyanobacterial bloom ecology were identified. The microbial biodiversity of blooms revealed microbial functions, genomic characteristics, and interactions between cyanobacterial populations that could be involved in bloom stability and more coherently define cyanobacteria blooms. Our results highlight the importance of considering cyanobacterial blooms as a microbial ecosystem to predict, prevent, and mitigate them