Software reliability growth models based on local polynomial modeling with kernel smoothing

Software reliability growth models (SRGMs) are extensively employed in software engineering to assess the reliability of software before their release for operational use. These models are usually parametric functions obtained by statistically fitting parametric curves, using Maximum Likelihood estimation or Least–squared method, to the plots of the cumulative number of failures observed N(t) against a period of systematic testing time t. Since the 1970s, a very large number of SRGMs have been proposed in the reliability and software engineering literature and these are often very complex, reflecting the involved testing regime that often took place during the software development process. In this paper we extend some of our previous work by adopting a nonparametric approach to SRGM modeling based on local polynomial modeling with kernel smoothing. These models require very few assumptions, thereby facilitating the estimation process and also rendering them more relevant under a wide variety of situations. Finally, we provide numerical examples where these models will be evaluated and compared.

Physically based simulation of heterogeneous deformable models using XFEM

This paper addresses the problem of heterogeneous deformable model accuracy using the finite element methods (FEM). Classic FEM uses predefined shape functions for interpolation and does not account easily for regions of discontinuities. Extended finite element methods (XFEM) use enrichment functions to compensate for the change in an element degrees of freedom (DoFs) in deformable objects. The XFEM is an accurate and fast method as no remeshing is required. In this study we investigate the performance of XFEM and demonstrate how it may be applied to discontinuities of materials that exist in heterogeneous (piece-wise homogeneous) models. The results show realistic stress prediction compared to modeling the same objects with classic FEM.

M-Dimension : multi-characteristics based routing protocol in human associated delay-tolerant networks with improved performance over one dimensional classic models

Human associated delay-tolerant network (HDTN) is a new delay-tolerant network where mobile devices are associated with humans. It can be viewed from both their geographic and social dimensions. The combination of these different dimensions can enable us to more accurately comprehend a delay-tolerant network and consequently use this multi-dimensional information to improve overall network efficiency. Alongside the geographic dimension of the network which is concerned with geographic topology of routing, social dimensions such as social hierarchy can be used to guide the routing message to improve not only the routing efficiency for individual nodes, but also efficiency for the entire network.

We propose a multi-dimensional routing protocol (M-Dimension) for the human associated delay-tolerant network which uses the local information derived from multiple dimensions to identify a mobile node more accurately. Each dimension has a weight factor and is organized by the Distance Function to select an intermediary and applies multi-cast routing. We compare M-Dimension to existing benchmark routing protocols using the MIT Reality Dataset, a well-known benchmark dataset based on a human associated mobile network trace file. The results of our simulations show that M-Dimension has a significant increase in the average success ratio and is very competitive when End-to-End Delay of packet delivery is used in comparison to other multi-cast DTN routing protocols.

Building monotonicity-preserving fuzzy inference models with optimization-based similarity reasoning and a monotonicity index

In this paper, a novel approach to building a Fuzzy Inference System (FIS) that preserves the monotonicity property is proposed. A new fuzzy re-labeling technique to re-label the consequents of fuzzy rules in the database (before the Similarity Reasoning process) and a monotonicity index for use in FIS modeling are introduced. The proposed approach is able to overcome several restrictions in our previous work that uses mathematical conditions in building monotonicity-preserving FIS models. Here, we show that the proposed approach is applicable to different FIS models, which include the zero-order Sugeno FIS and Mamdani models. Besides, the proposed approach can be extended to undertake problems related to the local monotonicity property of FIS models. A number of examples to demonstrate the usefulness of the proposed approach are presented. The results indicate the usefulness of the proposed approach in constructing monotonicity-preserving FIS models.

Towards a semantic quality based approach for business process models improvement

Business process (BP) modeling aims at a better understanding of processes, allowing deciders to improve them. We propose to support this modeling with an approach encompassing methods and tools for BP models quality measurement and improvement. In this paper we focus on semantic quality. The latter is evaluated by aligning BP model concepts with domain knowledge. The alignment is conducted thanks to meta-models. We also define validation rules for checking the completeness of BP models. A medical case study illustrates the main steps of our approach.

A comparative study of the effect of web-based versus in-class textbook ethics instruction on accounting students' propensity to whistle-blow

The authors examined whether accounting students’ propensity to whistle-blow differed between those instructed through a web-based teaching module and those exposed to a traditional in-class textbook-focused approach. A total of 156 students from a second-year financial accounting course participated in the study. Ninety students utilized the web-based module whereas 66 students were instructed through a traditional teaching approach based on ethical problems presented in the textbook. Subsequently, when presented with a whistle-blowing situation, it was found that students exposed to a web-based ethics instruction module were more likely to whistle-blow than those students exposed to a traditional in-class textbook ethics instruction approach.

The effect of known cardiovascular risk factors on carotid-femoral pulse wave velocity in school-aged children: a population based twin study

Childhood cardiovascular risk factors affect vascular function long before overt cardiovascular disease. Twin studies provide a unique opportunity to examine the influence of shared genetic and environmental influences on childhood cardiovascular function. We examined the relationship between birth parameters, markers of adiposity, insulin resistance, lipid profile and blood pressure and carotid-femoral pulse wave velocity (PWV), a validated non-invasive measure of arterial stiffness in a healthy cohort of school-aged twin children. PWV was performed on a population-based birth cohort of 147 twin pairs aged 7-11 years. Fasting blood samples, blood pressure and adiposity measures were collected concurrently. Mixed linear regression models were used to account for twin clustering, within- and between-twin pair associations. There were positive associations between both markers of higher adiposity, insulin resistance, elevated triglycerides and PWV, which remained significant after accounting for twin birth-set clustering. There was a positive association between both diastolic and mean arterial blood pressure and PWV in within-pair analysis in dizygotic, but not monozygotic twins, indicating genetic differences evident in dizygotic not monozygotic twins may affect these associations. Increased blood pressure, triglycerides and other metabolic markers are associated with increased PWV in school-aged twins. These results support both the genetic and environmental contribution to higher PWV, as a marker of arterial stiffness, and reiterate the importance of preventing metabolic syndrome from childhood.

2nd generation concrete construction: carbon footprint accounting

Purpose – Construction contractors and facility managers are being challenged to minimize the carbon footprint. Life cycle carbon‐equivalent (CO2‐e) accounting, whereby the potential emissions of greenhouse gases due to energy expenditure during construction and subsequent occupation of built infrastructure, generally ceases at the end of the service life. However, following demolition, recycling of demolition waste that becomes incorporated into 2nd generation construction is seldom considered within the management of the carbon footprint. This paper aims to focus on built concrete infrastructure, particularly the ability of recycled concrete to chemically react with airborne CO2, thereby significantly influencing CO2‐e estimates.

Design/methodology/approach – CO2‐e estimates were made in accordance with the methodology outlined in the Australian National Greenhouse Accounts (NGA) Factors and were based on the energy expended for each life cycle activity from audited records. Offsets to the CO2‐e estimates were based on the documented ability of concrete to chemically react with airborne carbon dioxide (“carbonation”) and predictions of CO2 uptake by concrete and recycled concrete was made using existing predictive diffusion models. The author's study focused on a built concrete bridge which was demolished and recycled at the end of the service life, and the recycled concrete was utilized towards 2nd generation construction. The sensitivity of CO2‐e and carbonation estimates were tested on several different types of source demolition waste as well as subsequent construction applications using recycled concrete (RCA). Whole‐of‐life CO2‐e estimates, including carbonation of RCA over the 1st and 2nd generations, were estimated and contrasted with conventional carbon footprints that end at the conclusion of the 1st generation.

Findings – Following demolition, CO2 capture by RCA is significant due to the more permeable nature of the crushed RCA compared with the original built infrastructure. RCA also has considerably greater exposed surface area, relative to volume, than a built concrete structure, and therefore more highly exposed surface to react with CO2: it therefore carbonates more comprehensively. CO2‐e estimates can be offset by as much as 55‐65 per cent when including the contribution of carbonation of RCA built within 2nd generation infrastructure. Further offsets are achievable using blended fly ash or slag cement binders; however, this study has focused on concrete composed of 100 per cent OPC binders and the effects of RCA.

Originality/value – Construction project estimates of life cycle CO2‐e emissions should include 2nd generation applications that follow the demolition of the 1st generation infrastructure. Life cycle estimates generally end at the time of demolition. However, by incorporating the recycled concrete demolition waste into the construction of 2nd generation infrastructure, the estimated CO2‐e is significantly offset during the 2nd generation life cycle by chemical uptake of CO2 (carbonation). This paper provides an approach towards inclusion of 2nd generation construction applications into whole‐of‐life estimates of CO2‐e.

Accounting for habitat and seafloor structure characteristics on southern rock lobster (Jasus edwardsii) assessment in a small marine reserve

Where to place marine protected areas (MPAs) and how much area they should cover are some of the most basic questions when designing MPAs. Based on the theory of island biogeography, larger reserves are likely to protect more species and individuals but smaller reserves have been shown to positively influence populations. In this study, we assess a localised population of the ecologically and economically important southern rock lobster (Jasus edwardsii) inside and outside a small reserve. We used standardised fishery assessment trapping methods to sample J. edwardsii populations inside a reserve and an adjacent area outside the reserve. The population characteristics of the captured individuals were compared inside and outside the reserve using t tests (male size, female size,number of reproductive females, number of individuals and biomass), and we found that there were significantly greater numbers and larger individuals and biomass inside the reserve. However, many assessments of MPA effectiveness are confounded by differences in habitat. To account for possible differences in habitat, we collected multibeam bathymetry data to allow us to characterise seafloor structure and video data to assign each sampling location to a biotope class based on macroalgae assemblages. Then, using generalised linear models (GLMs), we assessed differences in populations while accounting for habitat. The GLMs revealed that there was still a significant difference in populations inside the reserve despite habitat differences inside and outside the reserve. We demonstrate a methodological approach to provide a baseline data set to assess MPA effectiveness through time and measure how habitat may respond to indirect consequences of fishing or other human impacts at the species or ecosystem level. We also highlight some of the limitations in sampling design and data availability common in MPA studies and resulting implications for assessment.

Improved models for estimating temporal changes in carbon sequestration in above-ground biomass of mixed-species environmental plantings

Plantings of mixed native species (termed 'environmental plantings') are increasingly being established for carbon sequestration whilst providing additional environmental benefits such as biodiversity and water quality. In Australia, they are currently one of the most common forms of reforestation. Investment in establishing and maintaining such plantings relies on having a cost-effective modelling approach to providing unbiased estimates of biomass production and carbon sequestration rates. In Australia, the Full Carbon Accounting Model (FullCAM) is used for both national greenhouse gas accounting and project-scale sequestration activities. Prior to undertaking the work presented here, the FullCAM tree growth curve was not calibrated specifically for environmental plantings and generally under-estimated their biomass. Here we collected and analysed above-ground biomass data from 605 mixed-species environmental plantings, and tested the effects of several planting characteristics on growth rates. Plantings were then categorised based on significant differences in growth rates. Growth of plantings differed between temperate and tropical regions. Tropical plantings were relatively uniform in terms of planting methods and their growth was largely related to stand age, consistent with the un-calibrated growth curve. However, in temperate regions where plantings were more variable, key factors influencing growth were planting width, stand density and species-mix (proportion of individuals that were trees). These categories provided the basis for FullCAM calibration. Although the overall model efficiency was only 39-46%, there was nonetheless no significant bias when the model was applied to the various planting categories. Thus, modelled estimates of biomass accumulation will be reliable on average, but estimates at any particular location will be uncertain, with either under- or over-prediction possible. When compared with the un-calibrated yield curves, predictions using the new calibrations show that early growth is likely to be more rapid and total above-ground biomass may be higher for many plantings at maturity. This study has considerably improved understanding of the patterns of growth in different types of environmental plantings, and in modelling biomass accumulation in young (<25 years old) plantings. However, significant challenges remain to understand longer-term stand dynamics, particularly with temporal changes in stand density and species composition.