ObjectivesThe goal of this research was to evaluate how material curl, package structure and handling of pouches containing medical devices affect rates of contact between non-sterile surfaces and sterile devices during aseptic transfer.MethodsOne hundred and thirty-six individuals with practical experience in aseptic technique were recruited. Participants were asked to present the contents of four different pouch designs (a standard, one designed to curl in, another to curl out and one that incorporated a tab) using two transfer techniques. During the first block of trials “standard technique” was used; participants presented using their typical methods to the sterile field. Trials in the second block employed “modified technique”; participants were instructed to grab the package at the top center and present package contents using a single, fluid motion. The outside of the pouch and the backs of the participants’ hands were coated using a simulated contaminant before each trial. The simulant was undetectable in the visible spectrum, but fluoresced under a black light. The dependent variable was recorded in a binary fashion and analyzed using a generalized linear mixed model.ResultsParticipants were between 20–57 and the averaged year 5.1 years of experience in aseptic technique. The data analysis was based on generalized linear mixed effects (GLMM) model, which accommodates the repeated measurements within the same participant. The effect of the pouch design was significant (P‹0.001), but the effect of aseptic technique did not suggest significance (P = 0.088). Specifically, pouches designed with the material curled outward resulted in significantly fewer contacts with non-sterile surfaces than the other styles, including the inward, tab, and standard styles; this was true regardless of the used aseptic technique, standard (P = 0.0171, P = 0.0466, P = 0.0061, respectively) or modified (P‹0.0001 for all comparisons)).ConclusionResults presented here contribute to a growing body of knowledge that investigates packaging as a potential route of contamination for sterile devices during aseptic presentation. Specifically, we provide insights regarding how both package design and opening technique can be informed in ways that build safety into the healthcare system.

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Investments in wind power occur everywhere in the world. The value of these investments for integration in an electricity generation system cannot be determined in the same way as conventional electricity sources due to the variable and relative unpredictable nature of wind power. Wind power can only to some limited extend be centrally dispatched. To look at the long term value of investments in wind power, the term capacity credit can be used. It defines the level of conventional generation that can be replaced by wind power generation. Using four adequacy indices, namely Loss-of-load Expectancy (LOLE), Loss of Energy Expectation (LOEE), Loss-of-load Frequency (LOLF) and Expected Interruption Cost (EIC), the Peak load Carrying Capability (PLCC) is established for different sizes andlocations of wind power in a system. The PLCC can be seen as a way to quantify the capacity credit of wind power since it determines how much the load can be increased for a given level of wind power investment, while maintaining the system reliability. The adequacy indices are found to vary depending on size and location of wind power investments, therefore causing the PLCC to change accordingly. A Monte Carlo approach is used for determining the indices. Expected and unexpected outages of system elements are simulated and evaluated against system load. Wind power data are generated through Markov chains, based on actual meteorological data from Belgian weather measurement sites, thereby preserving the same statistical properties as the original data.

The aim of this analysis is to characterize the behaviour of the solar power in order to predict the response that different levels of penetration of PV installations might have in the grid. In order to obtain the most realistic approach, measurements from existing PV installations in Belgium have been used. These measurements are used to create new values for solar power series in order to simulate the random character of the solar behaviour. A Markov chains based algorithm is created to generate simulated solar power series.

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