Designers of energy systems often face challenges in balancing the trade-off between cost and reliability. In literature, several papers have presented mathematical models for optimizing the reliability and cost of energy systems. However, the previous models only addressed reliability implicitly, i.e., based on availability and maintenance planning. Others focused on allocation of reliability based on individual equipment requirements via non-linear models that require high computational effort. This work proposes a novel mixed-integer linear programming (MILP) model that combines the use of both input-output (I-O) modelling and linearized parallel system reliability expressions. The proposed MILP model can optimize the design and reliability of energy systems based on equipment function and operating capacity. The model allocates equipment with sufficient reliability to meet system functional requirements and determines the required capacity. A simple pedagogical example is presented in this work to illustrate the features of proposed MILP model. The MILP model is then applied to a polygeneration case study consisting of two scenarios. In the first scenario, the polygeneration system was optimized based on specified reliability requirements. The technologies chosen for Scenario 1 were the CHP module, reverse osmosis unit and vapour compression chiller. The total annualized cost (TAC) for Scenario 1 was 53.3 US$ million/year. In the second scenario, the minimum reliability level for heat production was increased. The corresponding results indicated that an additional auxiliary boiler must be operated to meet the new requirements. The resulting TAC for the Scenario 2 was 5.3% higher than in the first scenario.
Synthetic membranes are composed of thin sheets of polymeric macromolecules that can control the passage of components through them. Generally, synthetic membranes used in drug diffusion studies have one of two functions: skin simulation or quality control. Synthetic membranes for skin simulation, such as the silicone-based membranes polydimethylsiloxane and Carbosil, are generally hydrophobic and rate limiting, imitating the stratum corneum. In contrast, synthetic membranes for quality control, such as cellulose esters and polysulfone, are required to act as a support rather than a barrier. These synthetic membranes also often contain pores; hence, they are called porous membranes. The significance of Franz diffusion studies and synthetic membranes in quality control studies involves an understanding of the fundamentals of synthetic membranes. This article provides a general overview of synthetic membranes, including a brief background of the history and the common applications of synthetic membranes. This review then explores the types of synthetic membranes, the transport mechanisms across them, and their relevance in choosing a synthetic membrane in Franz diffusion cell studies for formulation assessment purposes.
Over the years, in vitro Franz diffusion experiments have evolved into one of the most important methods for researching transdermal drug administration. Unfortunately, this type of testing often yields permeation data that suffer from poor reproducibility. Moreover, this feature frequently occurs when synthetic membranes are used as barriers, in which case biological tissue-associated variability has been removed as an artefact of total variation. The objective of the current study was to evaluate the influence of a full-validation protocol on the performance of a tailor-made array of Franz diffusion cells (GlaxoSmithKline, Harlow, UK) available in our laboratory. To this end, ibuprofen was used as a model hydrophobic drug while synthetic membranes were used as barriers. The parameters investigated included Franz cell dimensions, stirring conditions, membrane type, membrane treatment, temperature regulation and sampling frequency. It was determined that validation dramatically reduced derived data variability as the coefficient of variation for steady-state ibuprofen permeation from a gel formulation was reduced from 25.7% to 5.3% (n = 6). Thus, validation and refinement of the protocol combined with improved operator training can greatly enhance reproducibility in Franz cell experimentation.
In the last two decades there have been dramatic changes in the epidemiology of Clostridium difficile infection (CDI), with increases in incidence and severity of disease in many countries worldwide. The incidence of CDI has also increased in surgical patients. Optimization of management of C difficile, has therefore become increasingly urgent. An international multidisciplinary panel of experts prepared evidenced-based World Society of Emergency Surgery (WSES) guidelines for management of CDI in surgical patients.
A combination of fifteen top quark mass measurements performed by the ATLAS and CMS experiments at the LHC is presented. The datasets used correspond to an integrated luminosity of up to 5 and 20 fb^{-1} of proton-proton collisions at center-of-mass energies of 7 and 8 TeV, respectively. The combination includes measurements in top quark pair events that exploit both the semileptonic and hadronic decays of the top quark, and a measurement using events enriched in single top quark production via the electroweak t channel. The combination accounts for the correlations between measurements and achieves an improvement in the total uncertainty of 31% relative to the most precise input measurement. The result is m_{t}=172.52±0.14(stat)±0.30(syst) GeV, with a total uncertainty of 0.33 GeV.
The first evidence for the Higgs boson decay to a Z boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision datasets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140 fb^{-1} for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is 2.2±0.7 times the standard model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.