Copolymerization of unsaturated polyester with styrene in inverted emulsion

Copolymerization of unsaturated polyester with styrene in water-in-oil (W/O) type emulsion was carried out with the use of various basic compounds as emulsifiers. It was found that a stable, gellike W/O type emulsion of unsaturated polyester resin is formed only when pK_a's of the bases are above 6 and their concentrations are higher than some critical value. In these conditions, water can be dispersed in emulsion up to 900% to the resin. By polymerization, the stable W/O type emulsion is transformed to a white solid copolymer which is dry to the touch and which contains 90–95% of initially added water. It was confirmed that the basic compounds react with the carboxylic group of the polyester to form at the water-resin interface polyester salts, which act as true emulsifying agents. The stabilization mechanism of the emulsion at various concentrations of the polyester salt was investigated, mainly by microscopic observations, and an interpretation of the critical value of emulsifier concentration is proposed.     

Propagation Delay of an RC-Chain With a Ramp Input

In this brief, two simple semi-analytical models which allow the estimation of the propagation delay of an RC-chain with a linear input are presented. The closed-form models can be used to evaluate the propagation delay of wires in modern VLSI and ULSI processes. The two approximations, a continuous function and a piecewise function, exhibit a maximum error lower than 15% at the end of the chain. The models have been validated extensively through circuit simulations. In particular, 1000 different RC-chains have been considered and simulated demonstrating the accuracy of the proposed models with respect to the most widely used Elmore delay metric     

Modelling Sewer Systems Costs with Multiple Linear Regression

The aim of this paper is the establishment and validation of cost functions for the various assets of sewer systems, namely gravity and raising pipes, manholes and pumping-stations. Costs are defined as a function of the main physical characteristics of the assets, such as, the pipe material and diameter, excavation depth and percentage of pavement (for sewer pipes), the manhole depth (for manholes) and flow rate, pump head and pump power (for pumping stations,). A four-step methodology was followed: 1) data collection, processing and analysis, 2) present value calculation, 3) key parameters identification and cost functions estimation, and 4) cost functions validation. Cost and infrastructure data for construction contracts of sewer systems managed by Águas de Portugal (AdP) were analyzed. Cost functions were estimated based on multiple linear regression analysis and compared with the ones obtained in previous studies.     

Evaluation of Reliability Indicators for WDNs with Demand-Driven and Pressure-Driven Models

Reliability of water distribution networks (WDNs) has received much attention in recent years due to progressive aging of infrastructures and climate change. Several reliability indicators, focusing on hydraulic aspects rather than water quality, have been proposed in literature. Reliability is generally assessed resorting to well established methods coupling hydraulic simulations and stochastic techniques that describe the WDNs hydraulic performance and component availability respectively. Two main algorithms are employed to simulate WDNs: the demand driven approach (DDA) that disregards the physical relationship between actual water demand and nodal pressure, and the pressure driven approach (PDA) that explicitly incorporates it. In this paper, we show how the choice of hydraulic solver may affect reliability indicators. We modify existing quantitative indicators at nodal and network level, and define novel indicators to consider water quality aspects. These indicators are evaluated for three example WDNs; discrepancies between results obtained with the two approaches depend on network size, feeding scheme and skeletonization. Results suggest to use with caution the DDA for reliability assessment at both local and global level.     

An efficient LMI approach for the quadratic stabilisation of a class of linear,uncertain, time-varying systems

The purpose of this article is to provide a numerically efficient method for the quadratic stabilisation of a class of linear, discrete-time, uncertain, time-varying systems. The considered class of systems is characterised by an interval time-varying (ITV) matrix and constant sensor and actuator matrices. It is required to find a linear time-invariant (LTI) static output feedback controller yielding a quadratically stable closed-loop system independently of the parameter variation rate. The solvability conditions are stated in terms of linear matrix inequalities (LMIs). The set of LMIs includes the stability conditions for the feedback connection of a unique suitably defined extreme plant with an LTI output controller and the positivity of a closed-loop extremal matrix. A consequent noticeable feature of the article is that the total number of LMIs is independent of the number of uncertain parameters. This greatly enhances the numerical efficiency of the design procedure.     

Thermal conductivity of ME771 glass-epoxy laminate from millikelvin temperatures to 4 K

Permaglas ME771 is a glass-epoxy laminate which is suitable for use at cryogenic temperatures. We have measured the thermal conductivity of a sample of this material between 64 mK and 4.2 K in the direction parallel to the reinforcing fibres, enabling us to make a comparison with the better known material G-10CR. The thermal conductivity follows the form that would be expected for such a material, and is similar to that of G-10CR, which has a similar (room temperature) tensile strength. We comment on some confusion that has arisen over the difference between G-10CR, a material specifically produced for cryogenic use, and G-10, the more common equivalent.     

Model-Supported Impact Assessment for the Water Sector in Central Germany Under Climate Change—A Case Study

Climate change challenges water managers and researchers to find sustainable management solutions, in order to avoid undesirable impacts on water resources, environment and water-dependent sectors. Needed are projections into the future for the main driving forces, the resulting pressures on water resources, and quantification of the impacts. Modeling studies can play an important role in investigating, quantifying, and communicating possible impacts of climate change, with account of uncertainty of the results. However, climate change related impacts and a need for adaptation still play a minor role in current river basin management plans that have to comply e.g. with the EU Water Framework Directive (WFD). One important reason is that climate impact assessment is generally done in research institutes, while management plans are designed by practitioners working in national and regional environmental agencies and water supply companies. Knowledge transfer from science to practice and visa versa is often missing. In the present study, we propose a methodology and a case study for model-supported decision making in the water sector applicable to a participatory water resources planning process. The methodology is applied in a case study investigating climate change impacts on water resources. The case study area is the German State of Saxony-Anhalt, where the task was to develop a climate change impact assessment including possible adaptation measures as basis for a federal adaptation directive.     

Application of near (NIR) infrared and mid (MIR) infrared spectroscopy as a rapid tool to classify extra virgin olive oil on the basis of fruity attribute intensity

A sensory analysis of 112 virgin olive oils was performed by a fully trained taste panel. The samples were divided in “defective” and “not defective” on the basis of their olfactory attributes. Then, the “not defective” samples were classified into “low”, “medium” and “high” according to the fruity aroma intensity perceived by assessors. All samples were also analysed by FT-NIR and FT-IR spectroscopy and processed by classification methods (LDA and SIMCA). The results showed that NIR and MIR spectroscopy coupled with statistical methods are an interesting technique compared with traditional sensory assessment in classifying olive oil samples on the basis of the fruity attribute. The prediction rate varied between 71.6% and 100%, as average value. The spectroscopic methods, combined with chemometric strategies, could represent a reliable, cheap and fast classification tool, able to draw a complete fingerprint of a food product, describing its intrinsic quality attributes, that include its sensory attributes.