Optimal production resource reallocation for CO2 emissions reduction in manufacturing sectors

To mitigate the effects of climate change, countries worldwide are advancing technologies to reduce greenhouse gas emissions. This paper proposes and measures optimal production resource reallocation using data envelopment analysis. This research attempts to clarify the effect of optimal production resource reallocation on CO2 emissions reduction, focusing on regional and industrial characteristics. We use finance, energy, and CO2 emissions data from 13 industrial sectors in 39 countries from 1995 to 2009. The resulting emissions reduction potential is 2.54 Gt-CO2 in the year 2009, with former communist countries having the largest potential to reduce CO2 emissions in the manufacturing sectors. In particular, basic material industry including chemical and steel sectors has a lot of potential to reduce CO2 emissions.

Choose wisely: Network, ontology and annotation resources for the analysis of Staphylococcus aureus omics data

Staphylococcus aureus (S. aureus) is a prominent human and livestock pathogen investigated widely using omic technologies. Critically, due to availability, low visibility or scattered resources, robust network and statistical contextualisation of the resulting data is generally under-represented. Here, we present novel meta-analyses of freely-accessible molecular network and gene ontology annotation information resources for S. aureus omics data interpretation. Furthermore, through the application of the gene ontology annotation resources we demonstrate their value and ability (or lack-there-of) to summarise and statistically interpret the emergent properties of gene expression and protein abundance changes using publically available data. This analysis provides simple metrics for network selection and demonstrates the availability and impact that gene ontology annotation selection can have on the contextualisation of bacterial omics data.

Analysis of authenticated encryption stream ciphers

Authenticated Encryption (AE) is the cryptographic process of providing simultaneous confidentiality and integrity protection to messages. AE is potentially more efficient than applying a two-step process of providing confidentiality for a message by encrypting the message and in a separate pass, providing integrity protection by generating a Message Authentication Code (MAC) tag. This paper presents results on the analysis of three AE stream ciphers submitted to the recently completed eSTREAM competition. We classify the ciphers based on the methods the ciphers use to provide authenticated encryption and discuss possible methods for mounting attacks on these ciphers.

Improving level crossing safety through enhanced data recording and reporting : The CRC for Rail Innovation’s Baseline Rail Level Crossing Video project

This paper describes the work being conducted in the baseline rail level crossing project, supported by the Australian rail industry and the Cooperative Research Centre for Rail Innovation. The paper discusses the limitations of near-miss data for analysis obtained using current level crossing occurrence reporting practices. The project is addressing these limitations through the development of a data collection and analysis system with an underlying level crossing accident causation model. An overview of the methodology and improved data recording process are described. The paper concludes with a brief discussion of benefits this project is expected to provide the Australian rail industry.

Technoeconomic analysis of renewable aviation fuel from microalgae,Pongamia pinnata, and sugarcane

Technoeconomic analysis of renewable aviatin fuels has not been widely considered, despite the increasing global attention that the field has received. We present three process models for production of aviation fuel from microalgae, Pongamia pinnata, and sugarcane molasses. The models and assumptions have been deposited on a wiki (http://qsafi.aibn.uq.edu.au) and are open and accessible to the community. Based on currently available long-term reputable technological data, this analysis indicates that the biorefinieries processing the microalgae, Pongamia seeds, and sugarcane feedstocks would be competitive with crude oil at $1343, $374, and $301/bbl, respectively. Sensitivity analyses of the major economic drivers suggest technological and market developments that would bring the corresponding figures down to $385, $255, and $168/bbl. The dynamic nature of the freely accessible models will allow the community to track progress toward economic competitiveness of aviation fuels from these renewable feedstocks.

Metaphors of big data

Metaphors are a common instrument of human cognition, activated when seeking to make sense of novel and abstract phenomena. In this article we assess some of the values and assumptions encoded in the framing of the term big data, drawing on the framework of conceptual metaphor. We first discuss the terms data and big data and the meanings historically attached to them by different usage communities and then proceed with a discourse analysis of Internet news items about big data. We conclude by characterizing two recurrent framings of the concept: as a natural force to be controlled and as a resource to be consumed.

Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis squared distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis squared distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis squared distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition.

Effects of resistance training on measures of muscular strength in people with Parkinson’s disease: A systematic review and meta-analysis

Objective The aim of this systematic review and meta-analysis was to determine the overall effect of resistance training (RT) on measures of muscular strength in people with Parkinson’s disease (PD). Methods Controlled trials with parallel-group-design were identified from computerized literature searching and citation tracking performed until August 2014. Two reviewers independently screened for eligibility and assessed the quality of the studies using the Cochrane risk-of-bias-tool. For each study, mean differences (MD) or standardized mean differences (SMD) and 95% confidence intervals (CI) were calculated for continuous outcomes based on between-group comparisons using post-intervention data. Subgroup analysis was conducted based on differences in study design. Results Nine studies met the inclusion criteria; all had a moderate to high risk of bias. Pooled data showed that knee extension, knee flexion and leg press strength were significantly greater in PD patients who undertook RT compared to control groups with or without interventions. Subgroups were: RT vs. control-without-intervention, RT vs. control-with-intervention, RT-with-other-form-of-exercise vs. control-without-intervention, RT-with-other-form-of-exercise vs. control-with-intervention. Pooled subgroup analysis showed that RT combined with aerobic/balance/stretching exercise resulted in significantly greater knee extension, knee flexion and leg press strength compared with no-intervention. Compared to treadmill or balance exercise it resulted in greater knee flexion, but not knee extension or leg press strength. RT alone resulted in greater knee extension and flexion strength compared to stretching, but not in greater leg press strength compared to no-intervention. Discussion Overall, the current evidence suggests that exercise interventions that contain RT may be effective in improving muscular strength in people with PD compared with no exercise. However, depending on muscle group and/or training dose, RT may not be superior to other exercise types. Interventions which combine RT with other exercise may be most effective. Findings should be interpreted with caution due to the relatively high risk of bias of most studies.

Studying relationships in project management through social network analysis

Successful project management depends upon forming and maintaining relationships between and among project team members and stakeholder groups. The nature of these relationships and the patterns that they form affect communication, collaboration and resource flows. Networks affect us directly, and we use them to influence people and processes. Social Network Analysis (SNA) can be an extremely valuable research tool to better understand how critical social networks develop and influence work processes, particularly as projects become larger and more complex. This chapter introduces foundational network concepts, helps you determine if SNA could help you answer your research questions, and explains how to design and implement a social network study. At the end of this chapter, the reader can: understand foundational concepts about social networks; decide if SNA is an appropriate research methodology to address particular questions or problems; design and implement a basic social network study.

Data driven modeling for power transformer lifespan evaluation

Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation’s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit (RTU) system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

The case for “n«all”: Why the Big Data revolution will probably happen differently in the mining sector

Big Data and predictive analytics have received significant attention from the media and academic literature throughout the past few years, and it is likely that these emerging technologies will materially impact the mining sector. This short communication argues, however, that these technological forces will probably unfold differently in the mining industry than they have in many other sectors because of significant differences in the marginal cost of data capture and storage. To this end, we offer a brief overview of what Big Data and predictive analytics are, and explain how they are bringing about changes in a broad range of sectors. We discuss the “N=all” approach to data collection being promoted by many consultants and technology vendors in the marketplace but, by considering the economic and technical realities of data acquisition and storage, we then explain why a “n « all” data collection strategy probably makes more sense for the mining sector. Finally, towards shaping the industry’s policies with regards to technology-related investments in this area, we conclude by putting forward a conceptual model for leveraging Big Data tools and analytical techniques that is a more appropriate fit for the mining sector.

Estimating interactive coefficients for analysing interactive failures

Engineering assets are often complex systems. In a complex system, components often have failure interactions which lead to interactive failures. A system with interactive failures may lead to an increased failure probability. Hence, one may have to take the interactive failures into account when designing and maintaining complex engineering systems. To address this issue, Sun et al have developed an analytical model for the interactive failures. In this model, the degree of interaction between two components is represented by interactive coefficients. To use this model for failure analysis, the related interactive coefficients must be estimated. However, methods for estimating the interactive coefficients have not been reported. To fill this gap, this paper presents five methods to estimate the interactive coefficients including probabilistic method; failure data based analysis method; laboratory experimental method; failure interaction mechanism based method; and expert estimation method. Examples are given to demonstrate the applications of the proposed methods. Comparisons among these methods are also presented.