Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

The use of Mahalanobis squared distance–based novelty detection in statistical damage identification has become increasingly popular in recent years. The merit of the Mahalanobis squared distance–based method is that it is simple and requires low computational effort to enable the use of a higher dimensional damage-sensitive feature, which is generally more sensitive to structural changes. Mahalanobis squared distance–based damage identification is also believed to be one of the most suitable methods for modern sensing systems such as wireless sensors. Although possessing such advantages, this method is rather strict with the input requirement as it assumes the training data to be multivariate normal, which is not always available particularly at an early monitoring stage. As a consequence, it may result in an ill-conditioned training model with erroneous novelty detection and damage identification outcomes. To date, there appears to be no study on how to systematically cope with such practical issues especially in the context of a statistical damage identification problem. To address this need, this article proposes a controlled data generation scheme, which is based upon the Monte Carlo simulation methodology with the addition of several controlling and evaluation tools to assess the condition of output data. By evaluating the convergence of the data condition indices, the proposed scheme is able to determine the optimal setups for the data generation process and subsequently avoid unnecessarily excessive data. The efficacy of this scheme is demonstrated via applications to a benchmark structure data in the field.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

Hot spot identification (HSID) aims to identify potential sites—roadway segments, intersections, crosswalks, interchanges, ramps, etc.—with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset.

Multimedia : a necessary step in the evolution of research funding applications

Multimedia communication capabilities are rapidly expanding, and visual information is easily shared electronically, yet funding bodies still rely on paper grant proposal submissions. Incorporating modern technologies will streamline the granting process by increasing the fidelity of grant communication, improving the efficiency of review, and reducing the cost of the process.

Using communicative ecology theory to scope the emerging role of social media in the evolution of urban food systems

Urban agriculture plays an increasingly vital role in supplying food to urban populations. Changes in Information and Communications Technology (ICT) are already driving widespread change in diverse food-related industries such as retail, hospitality and marketing. It is reasonable to suspect that the fields of ubiquitous technology, urban informatics and social media equally have a lot to offer the evolution of core urban food systems. We use communicative ecology theory to describe emerging innovations in urban food systems according to their technical, discursive and social components. We conclude that social media in particular accentuate fundamental social interconnections normally effaced by conventional industrialised approaches to food production and consumption.

Vertical alignment of process models – how can we get there?

There is a wide variety of drivers for business process modelling initiatives, reaching from business evolution and process optimisation over compliance checking and process certification to process enactment. That, in turn, results in models that differ in content due to serving different purposes. In particular, processes are modelled on different abstraction levels and assume different perspectives. Vertical alignment of process models aims at handling these deviations. While the advantages of such an alignment for inter-model analysis and change propagation are out of question, a number of challenges has still to be addressed. In this paper, we discuss three main challenges for vertical alignment in detail. Against this background, the potential application of techniques from the field of process integration is critically assessed. Based thereon, we identify specific research questions that guide the design of a framework for model alignment.

Optimal Bayesian experimental design for models with intractable likelihoods using indirect inference applied to biological process models

This paper addresses the problem of determining optimal designs for biological process models with intractable likelihoods, with the goal of parameter inference. The Bayesian approach is to choose a design that maximises the mean of a utility, and the utility is a function of the posterior distribution. Therefore, its estimation requires likelihood evaluations. However, many problems in experimental design involve models with intractable likelihoods, that is, likelihoods that are neither analytic nor can be computed in a reasonable amount of time. We propose a novel solution using indirect inference (II), a well established method in the literature, and the Markov chain Monte Carlo (MCMC) algorithm of Müller et al. (2004). Indirect inference employs an auxiliary model with a tractable likelihood in conjunction with the generative model, the assumed true model of interest, which has an intractable likelihood. Our approach is to estimate a map between the parameters of the generative and auxiliary models, using simulations from the generative model. An II posterior distribution is formed to expedite utility estimation. We also present a modification to the utility that allows the Müller algorithm to sample from a substantially sharpened utility surface, with little computational effort. Unlike competing methods, the II approach can handle complex design problems for models with intractable likelihoods on a continuous design space, with possible extension to many observations. The methodology is demonstrated using two stochastic models; a simple tractable death process used to validate the approach, and a motivating stochastic model for the population evolution of macroparasites.

Structural evolution of nanocrystalline silicon thin films synthesized in high-density, low-temperature reactive plasmas

Silicon thin films with a variable content of nanocrystalline phase were deposited on single-crystal silicon and glass substrates by inductively coupled plasma-assisted chemical vapor deposition using a silane precursor without any hydrogen dilution in the low substrate temperature range from 100 to 300 °C. The structural and optical properties of the deposited films are systematically investigated by Raman spectroscopy, x-ray diffraction, Fourier transform infrared absorption spectroscopy, UV/vis spectroscopy, scanning electron microscopy and high-resolution transmission electron microscopy. It is shown that the structure of the silicon thin films evolves from the purely amorphous phase to the nanocrystalline phase when the substrate temperature is increased from 100 to 150 °C. It is found that the variations of the crystalline fraction fc, bonded hydrogen content CH, optical bandgap ETauc, film microstructure and growth rate Rd are closely related to the substrate temperature. In particular, at a substrate temperature of 300 °C, the nanocrystalline Si thin films of our interest feature a high growth rate of 1.63nms-1, a low hydrogen content of 4.0at.%, a high crystalline fraction of 69.1%, a low optical bandgap of 1.55eV and an almost vertically aligned columnar structure with a mean grain size of approximately 10nm. It is also shown that the low-temperature synthesis of nanocrystalline Si thin films without any hydrogen dilution is attributed to the outstanding dissociation ability of the high-density inductively coupled plasmas and effective plasma-surface interactions during the growth process. Our results offer a highly effective yet simple and environmentally friendly technique to synthesize high-quality nanocrystalline Si films, vitally needed for the development of new-generation solar cells and other emerging nanotechnologies.

Determination of urinary thymidine glycol using affinity chromatography, HPLC and post-column reaction detection : a biomarker of oxidative DNA damage upon kidney transplantation

Reactive oxygen species are generated during ischaemia-reperfusion of tissue. Oxidation of thymidine by hydroxyl radicals (HO) leads to the formation of 5,6-dihydroxy-5,6-dihydrothymidine (thymidine glycol). Thymidine glycol is excreted in urine and can be used as biomarker of oxidative DNA damage. Time dependent changes in urinary excretion rates of thymidine glycol were determined in six patients after kidney transplantation and in six healthy controls. A new analytical method was developed involving affinity chromatography and subsequent reverse-phase high-performance liquid chromatography (RP-HPLC) with a post-column chemical reaction detector and endpoint fluorescence detection. The detection limit of this fluorimetric assay was 1.6 ng thymidine glycol per ml urine, which corresponds to about half of the physiological excretion level in healthy control persons. After kidney transplantation the urinary excretion rate of thymidine glycol increased gradually reaching a maximum around 48 h. The excretion rate remained elevated until the end of the observation period of 10 days. Severe proteinuria with an excretion rate of up to 7.2 g of total protein per mmol creatinine was also observed immediately after transplantation and declined within the first 24 h of allograft function (0.35 + 0.26 g/mmol creatinine). The protein excretion pattern, based on separation of urinary proteins on sodium dodecyl sulphate-polyacrylamide gel electrophorosis (SDS-PAGE), as well as excretion of individual biomarker proteins, indicated nonselective glomerular and tubular damage. The increased excretion of thymidine glycol after kidney transplantation may be explained by ischaemia-reperfusion induced oxidative DNA damage of the transplanted kidney.

Evolution of the docklands food hub : a design-led innovation approach to food sovereignty through local food systems

Access to nutritious, safe and culturally appropriate food is a basic human right (Mechlem, 2004). Food sovereignty defines this right through the empowerment of the people to redefine food and agricultural systems, and through ecologically sustainable production methods. At the heart of the food sovereignty movement are the interests of producers, distributors and consumers, rather than the interests of markets and corporations, which dominate the current globalized food system (Hinrichs, 2003). Food sovereignty challenges designers to enable people to innovate the food system. We are yet to develop economically viable solutions for scaling projects and providing citizens, governments and business with tools to develop and promote projects to innovate food systems and promote food sovereignty (Meroni, 2011; Murray, Caulier-Grice and Mulgan, 2010). This article examines how a design-led approach to innovation can assist in the development of new business models and ventures for local food systems: this is presented through an emerging field of research ‘Design-Led Food Communities’. Design-Led Food Communities enables citizens, governments and business to innovate local food projects through the application of design. This article reports on the case study of the Docklands Food Hub Project in Melbourne, Australia. Preliminary findings demonstrate valued outcomes, but also a deficiency in the design process to generate food solutions collaboratively between government, business and citizens.

The contribution of ISO 14067 to the evolution of global greenhouse gas standards—A review

Due to the increasing recognition of global climate change, the building and construction industry is under pressure to reduce carbon emissions. A central issue in striving towards reduced carbon emissions is the need for a practicable and meaningful yardstick for assessing and communicating greenhouse gas (GHG) results. ISO 14067 was published by the International Organization for Standardization in May 2013. By providing specific requirements in the life cycle assessment (LCA) approach, the standard clarifies the GHG assessment in the aspects of choosing system boundaries and simulating use and end-of-life phases when quantifying carbon footprint of products (CFPs). More importantly, the standard, for the first time, provides step-to-step guidance and standardized template for communicating CFPs in the form of CFP external communication report, CFP performance tracking report, CFP declaration and CFP label. ISO 14067 therefore makes a valuable contribution to GHG quantification and transparent communication and comparison of CFPs. In addition, as cradle-to-grave should be used as the system boundary if use and end-of-life phases can be simulated, ISO 14067 will hopefully promote the development and implementation of simulation technologies, with Building Information Modelling (BIM) in particular, in the building and construction industry.

Structure evolution characterization of Anyang anthracites via H2O2 oxidization and HF acidification

The structural characteristics of the raw coal (AY), the H2O2 oxidized coals (AY–H2O2) and the HF acidized AY–H2O2 (AY–H2O2–HF) were investigated by SEM, X-ray diffraction, Raman and FTIR spectroscopy. The results indicate that the derivative coals show an obvious increase in the aromaticity, crystalline carbon content and hydroxyl content, especially the AY–H2O2–HF. The stacking layer number of crystalline carbon decreases and the aspect ratio (La/Lc) remarkably increases for AY–H2O2 and AY–H2O2–HF. The crystalline layers become much thinner. The particle size of AY–H2O2–HF in width significantly decreases from 1 μm to less than 100 nm. The combination of H2O2 oxidization and HF acidification is effective to reduce the size of the aromatic layers and to increase the reactivity of derivative coals. The process can help us obtain the superfine crystalline carbon materials like graphite structure.

Evaluating and predicting overall process risk using event logs

Companies standardise and automate their business processes in order to improve process eff ciency and minimise operational risks. However, it is di fficult to eliminate all process risks during the process design stage due to the fact that processes often run in complex and changeable environments and rely on human resources. Timely identification of process risks is crucial in order to insure the achievement of process goals. Business processes are often supported by information systems that record information about their executions in event logs. In this article we present an approach and a supporting tool for the evaluation of the overall process risk and for the prediction of process outcomes based on the analysis of information recorded in event logs. It can help managers evaluate the overall risk exposure of their business processes, track the evolution of overall process risk, identify changes and predict process outcomes based on the current value of overall process risk. The approach was implemented and validated using synthetic event logs and through a case study with a real event log.

Adaptive aneuploidy protects against thiol peroxidase deficiency by increasing respiration via key mitochondrial proteins

Aerobic respiration is a fundamental energy-generating process; however, there is cost associated with living in an oxygen-rich environment, because partially reduced oxygen species can damage cellular components. Organisms evolved enzymes that alleviate this damage and protect the intracellular milieu, most notably thiol peroxidases, which are abundant and conserved enzymes that mediate hydrogen peroxide signaling and act as the first line of defense against oxidants in nearly all living organisms. Deletion of all eight thiol peroxidase genes in yeast (∆8 strain) is not lethal, but results in slow growth and a high mutation rate. Here we characterized mechanisms that allow yeast cells to survive under conditions of thiol peroxidase deficiency. Two independent ∆8 strains increased mitochondrial content, altered mitochondrial distribution, and became dependent on respiration for growth but they were not hypersensitive to H2O2. In addition, both strains independently acquired a second copy of chromosome XI and increased expression of genes encoded by it. Survival of ∆8 cells was dependent on mitochondrial cytochrome-c peroxidase (CCP1) and UTH1, present on chromosome XI. Coexpression of these genes in ∆8 cells led to the elimination of the extra copy of chromosome XI and improved cell growth, whereas deletion of either gene was lethal. Thus, thiol peroxidase deficiency requires dosage compensation of CCP1 and UTH1 via chromosome XI aneuploidy, wherein these proteins support hydroperoxide removal with the reducing equivalents generated by the electron transport chain. To our knowledge, this is the first evidence of adaptive aneuploidy counteracting oxidative stress.

Wear particle analysis and the evolution of the plastically deformed layer on Australian rail steel

Particle analysis methodology is presented, together with the morphology of the wear debris formed during rolling contact fatigue. Wear particles are characterised by their surface topography and in terms of wear mechanism. Rail-wheel materials are subjected to severe plastic deformation as the contact loading progresses, which contributes to a mechanism of major damage in head-hardened rail steel. Most of the current methodologies involve sectioning of the rail-wheel discs to trace material damage phenomena such as crack propagation and plastic strain accumulation. This paper proposes methodology to analyse the development of the plastically deformed layer by sectioning wear particles using the focussed ion beam (FIB) milling method. Moreover, it highlights the processes of oxidation and rail surface delamination during unlubricated rolling contact fatigue.

Using the evolution of consumer products to inform design

The development of a new consumer product and its release to market is typically an expensive and risky process. It is estimated that up to 80% of all new products fail in the marketplace (Savoia, 2014). The consequences of failure can be ruinous for a manufacturer both financially and in terms of brand reputation. So even small improvements in success prediction have the potential to save money, effort and brand reputation. This paper proposes an approach where the history and evolution of a product is mapped and analyzed. The results of the analysis can then be used to inform design decisions. This paper will also demonstrate the similarities between biological evolution and the evolution of consumer products. Using the existing structure and terminology of biological evolution allows us to focus on the aspects of innovations that have led to success and those that have led to failure. This paper uses the case study of the wristwatch and its development over 100 years. The analysis of this leads to recommendations for contemporary “smartwatches.”