Search Results (4)

Background: Compared to soybean oil intravenous fat emulsion (SO-IFE), use of mixed-oil IFE (MO-IFE) is associated with reduced rates of catheter-related bloodstream infections caused by coagulase-negative Staphylococcus species (CoNS) in pediatric patients receiving parenteral nutrition. Methods: Using an in vitro biofilm model, this study aimed to assess the impact of IFEs on biofilm formation among Staphylococcus species. S. aureus, S. capitis, S. epidermidis, S. haemolyticus, S. hominis, and S. lugdunensis were cultivated as biofilms in media supplemented with SO-IFE, MO-IFE, or fish oil IFE (IFE). Biomass was quantified by the crystal violet method, and follow-up planktonic growth assays assessed antimicrobial effects of IFEs. Results: Compared to SO-IFE, MO-IFE and FO-IFE significantly inhibited biofilm formation of S. aureus but did not impact planktonic growth. Contrary to clinical data, CoNS biofilm formation was not impacted by any of the IFEs tested. S. aureus biofilm inhibition in IFEs was further investigated by comparing differences following growth in SO-IFE supplemented with capric acid, docosahexaenoic acid (DHA), or eicosapenaenoic acid (EPA) to concentrations matching those of MO-IFE. Capric acid supplementation was associated with significant reduction in biofilm formation compared to SO-IFE alone. However, this was attributed to a bactericidal effect based on follow-up planktonic growth assays. Conclusions: These results suggest that biofilm formation in S. aureus is variably impacted by fatty acid composition in clinically relevant IFEs, with capric acid exhibiting bactericidal activity against tested isolates. Full article

In this paper, a stochastic multi-objective intelligent framework (MOIF) is performed for distribution network reconfiguration to minimize power losses, the number of voltage sags, the system’s average RMS fluctuation, the average system interruption frequency (ASIFI), the momentary average interruption frequency (MAIFI), and the system average interruption frequency (SAIFI) considering the network uncertainty. The unscented transformation (UT) approach is applied to model the demand uncertainty due to its being simple to implement and requiring no assumptions to simplify it. A human-inspired intelligent method named improved mountaineering team-based optimization (IMTBO) is used to find the decision variables defined as the network’s optimal configuration. The conventional MTBO is improved using a quasi-opposition-based learning strategy to overcome premature convergence and achieve the optimal solution. The simulation results showed that in single- and double-objective optimization some objectives are weakened compared to their base value, while the results of the MOIF indicate a fair compromise between different objectives, and all objectives are enhanced. The results of the MOIF based on the IMTBO clearly showed that the losses are reduced by 30.94%, the voltage sag numbers and average RMS fluctuation are reduced by 33.68% and 33.65%, and also ASIFI, MAIFI, and SAIFI are improved by 6.80%, 44.61%, and 0.73%, respectively. Also, the superior capability of the MOIF based on the IMTBO is confirmed compared to the conventional MTBO, particle swarm optimization, and the artificial electric field algorithm. Moreover, the results of the stochastic MOIF based on the UT showed the power loss increased by 7.62%, voltage sag and SARFI increased by 5.39% and 5.31%, and ASIFI, MAIFI, and SAIFI weakened by 2.28%, 6.61%, and 1.48%, respectively, compared to the deterministic MOIF model. Full article

The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue is to detect the borderline between water bodies (ocean, sea, lake or river) and land. This research aims to define a rapid and accurate methodological approach, based on the k-means algorithm, to classify the remotely sensed images in an unsupervised way to distinguish water body pixels and detect coastline. Landsat 8 Operational Land Imager (OLI) multispectral satellite images were considered. The proposal requires applying the k-means algorithm only to the most appropriate multispectral bands, rather than using the entire dataset. In fact, by using only suitable bands to detect the differences between water and no-water (vegetation and bare soil), more accurate results were obtained. For this scope, a new index based on the optimum index factor (OIF) was applied to identify the three best-performing bands for the purpose. The direct comparison between the automatically extracted coastline and the manually digitized one was used to evaluate the product accuracy. The results were very satisfactory and the combination involving bands B2 (blue), B5 (near infrared), and B6 (short-wave infrared-1) provided the best performance. Full article

Magneto-optical indicator films (MOIFs) are a very useful tool for direct studies of the spatial distribution of magnetic fields and the magnetization processes in magnetic materials and industrial devices such as magnetic sensors, microelectronic components, micro-electromechanical systems (MEMS), and others. The ease of application and the possibility for direct quantitative measurements in combination with a straightforward calibration approach make them an indispensable tool for a wide spectrum of magnetic measurements. The basic sensor parameters of MOIFs, such as a high spatial resolution down to below 1 μm combined with a large spatial imaging range of up to several cm and a wide dynamic range from 10 μT to over 100 mT, also foster their application in various areas of scientific research and industry. The history of MOIF development totals approximately 30 years, and only recently have the underlying physics been completely described and detailed calibration approaches been developed. The present review first summarizes the history of MOIF development and applications and then presents the recent advances in MOIF measurement techniques, including the theoretical developments and traceable calibration methods. The latter make MOIFs a quantitative tool capable of measuring the complete vectorial value of a stray field. Furthermore, various scientific and industrial application areas of MOIFs are described in detail. Full article