Estimating postprandial glucose fluxes using hierarchical Bayes modelling

A new stochastic computational method was developed to estimate the endogenous glucose production, the meal-related glucose appearance rate (R(a meal)), and the glucose disposal (R(d)) during the meal tolerance test. A prior probability distribution was adopted which assumes smooth glucose fluxes with individualized smoothness level within the context of a Bayes hierarchical model. The new method was contrasted with the maximum likelihood method using data collected in 18 subjects with type 2 diabetes who ingested a mixed meal containing [U-(13)C]glucose. Primed [6,6-(2)H(2)]glucose was infused in a manner that mimicked the expected endogenous glucose production. The mean endogenous glucose production, R(a meal), and R(d) calculated by the new method and maximum likelihood method were nearly identical. However, the maximum likelihood gave constant, nonphysiological postprandial endogenous glucose production in two subjects whilst the new method gave plausible estimates of endogenous glucose production in all subjects. Additionally, the two methods were compared using a simulated triple-tracer experiment in 12 virtual subjects. The accuracy of the estimates of the endogenous glucose production and R(a meal) profiles was similar [root mean square error (RMSE) 1.0±0.3 vs. 1.4±0.7μmol/kg/min for EGP and 2.6±1.0 vs. 2.9±0.9μmol/kg/min for R(a meal); new method vs. maximum likelihood method; P=NS, paired t-test]. The accuracy of R(d) estimates was significantly increased by the new method (RMSE 5.3±1.9 vs. 4.2±1.3; new method vs. ML method; P<0.01, paired t-test). We conclude that the new method increases plausibility of the endogenous glucose production and improves accuracy of glucose disposal compared to the maximum likelihood method.     

Mastering the structure of PLA foams made with extrusion assisted by supercritical CO2

In this study, the outcome of operating conditions of extrusion assisted by supercritical CO₂ for the manufacture of poly(lactic acid) foams was investigated. It was found that the temperature before and inside the die was the most prominent parameter to tune the foam properties. Foam porosity as high as 96% could be obtained (for die temperature between 109 and 112 °C), representing a total expansion exceeding 30. In this temperature range, low crystallinity (≈6%) was induced giving foams with high radial expansion i.e., large diameters and open porosity. At 112 °C, the CO₂ was able to greatly expand the foams, providing 73% of its potential blowing effect. On the other hand, a low die temperature (below a die temperature of 107 °C) induces a significantly higher level of crystallinity resulting in foams with closed‐porosity and a large longitudinal expansion due to higher strength of the polymer melt. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45067.     

Optimization of Power Consumption in 4G LTE Networks Using a Novel Barebones Self-adaptive Differential Evolution Algorithm

The power consumption of wireless access networks is an important issue. In this paper, the power consumption of Long Term Evolution (LTE) base stations is optimized. We consider the city of Ghent, Belgium with 75 possible LTE base station locations. We optimize the network towards two objectives: the coverage maximization and the power consumption minimization. We propose a new Barebones Self-adaptive Differential Evolution. The results of the proposed method indicate the advantages and applicability of our approach.     

Root cause analysis during process development using Joint-Y PLS

The original purpose of the JYPLS model was to aid product transfer and scale-up. This work demonstrates that the JYPLS model is also well suited to analyze data from a process in development where the number and type of sensors and sensor locations are also being decided. The specific application in this case was to determine the root cause for a bias found during the development of a multivariate calibration model for a Near Infrared (NIR) instrument. The calibration model was built in parallel with the development of the manufacturing process itself. Each time the calibration model was tested with data from a new batch run, a non-explained bias was observed. Laboratory results on samples gathered from the process show that the NIR signal itself was biased and not the actual concentration of the constituent. Data on the processing conditions were collected for all the batches and included in the JYPLS model along with NIR and lab results. The loadings and scores from the JYPLS model were interpreted to isolate the root cause of the observed drift in the calibration model. This work discusses the general approach and presents the sequence of diagnostics used to analyze the different campaigns during process development. This should be applicable to other scenarios in process development where the data has the same architecture.     

Understanding life cycle social impacts in manufacturing: A processed-based approach

Developing sustainable products and processes is growing in importance due to increasing regulation, consumer interest, access to information, and competitive forces. In order to adequately evaluate the sustainability of products and processes, there is a need to consider the impacts from all three pillars of sustainability – society, environment, and economics. There are substantial challenges to identifying and understanding the social impacts associated with manufacturing activities. This paper provides a framework for characterizing the social impacts of manufacturing throughout the life cycle of a product or process. Social impacts occur on various scales in manufacturing, from the level of a unit process to the level of the enterprise. Additionally, manufacturing activities impact consumers, communities, and larger political/spatial realms. This paper identifies key characteristics of social impacts associated with manufacturing that should be considered to more effectively address the social dimension of sustainability for products and processes. Examples involving a typical manufacturing process – welding – are presented to illustrate the utility of the framework.     

Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry

ABSTRACT

The physical properties of a snowpack strongly influence the emissions from the substratum, making snow property retrievals feasible by means of the surface brightness temperature observed by passive microwave sensors. Depending on the spatial resolution observed, time series records of daily snow coverage and critical snowpack properties such as snow depth (SD) and snow water equivalent (SWE) could be helpful in applications ranging from modelling snow variations for water resources management in a catchment to global climatologic studies. However, the challenge of including spaceborne SWE products in operational hydrological and hydroclimate modelling applications is very demanding with limited uptake by these systems, mostly attributed to insufficient SWE estimation accuracy. The root causes of this challenge include the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process caused by uncertainties with the forward emission modelling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of developed and adopted retrieval methodologies can provide the clarity needed to enhance our knowledge in this field. In this paper, we review snow depth and SWE retrieval methods using PM observations, taking only dry snow retrieval processes into consideration. Snow properties using PM observations can be modelled by purely empirical relations based on underlying physical processes, and SD and SWE can be estimated by regression-based approaches. Snow property retrievals have been refined gradually throughout four decades use of PM observations in tandem with better understanding of physical processes, inclusion of better snowpack parameterizations, improved uncertainty analysis frameworks, and applying better inversion algorithms. Studying available methods, we conclude that snowpack parameterization is key to accurate retrieval. By improving retrieval algorithm architectures to better capture dynamic snowpack evolution processes, SWE estimates are likely to improve. We conclude that this challenge can be addressed by coupling emission models and land surface models or integrating weather-driven snowpack evolution into emission models and performing inversion in a Bayesian framework.