A methodology for evaluating the reliability and risk of structures under complex service environments

The theoretical basis and numerical implementation of NESSUS (Numerical Evaluation of Stochastic Structures Under Stress), a computer code for probabilistic structural analysis of aerospace components, are described, with an emphasis on the use of NESSUS for reliability and risk assessment. Topics addressed include the structure of probabilistic models of fatigue-crack initiation, risk/cost evaluation, fatigue-fracture analysis, and fatigue-crack initiation. Numerical results from typical applications are presented in graphs and briefly characterized. The usefulness of NESSUS predictions for establishing inspection and retirement schedules and for component certification is indicated

Learner-Centred Design and Evaluation of Web-Based E-Learning Environments

Designing E-learning is a combination of pedagogical design, usability and information architecture. E-learning environments should have intuitive interfaces and clear information design, allowing learners to focus on learning. However, there is often a mismatch between what an on-line educator thinks the learner would learn, and what a learner thinks he will, and then has learned from the course. In addition, there is sometimes a mismatch between how an educator wants to teach and what is represented on the interface by the instructional designers. Such mismatches affect the learner's experience and his motivation for E-learning. In this paper, we will first discuss the source and nature of these mismatches. Next, we will discuss whether usability techniques in the HCI literature are appropriate for evaluating E-learning environments for the learner experience. We will then propose a combination of requirements elicitation and usability techniques for learner-centred design and evaluation of Web-based E-learning environments. The proposed methodology is based on our experience of conducting empirical studies for evaluating user-system interactions in E-Commerce contexts

Integrating driving and traffic simulators for the study of railway level crossing safety interventions: a methodology

Safety at Railway Level Crossings (RLXs) is an important issue within the Australian transport system. Crashes at RLXs involving road vehicles in Australia are estimated to cost $10 million each year. Such crashes are mainly due to human factors; unintentional errors contribute to 46% of all fatal collisions and are far more common than deliberate violations. This suggests that innovative intervention targeting drivers are particularly promising to improve RLX safety. In recent years there has been a rapid development of a variety of affordable technologies which can be used to increase driver’s risk awareness around crossings. To date, no research has evaluated the potential effects of such technologies at RLXs in terms of safety, traffic and acceptance of the technology. Integrating driving and traffic simulations is a safe and affordable approach for evaluating these effects. This methodology will be implemented in a driving simulator, where we recreated realistic driving scenario with typical road environments and realistic traffic. This paper presents a methodology for evaluating comprehensively potential benefits and negative effects of such interventions: this methodology evaluates driver awareness at RLXs , driver distraction and workload when using the technology . Subjective assessment on perceived usefulness and ease of use of the technology is obtained from standard questionnaires. Driving simulation will provide a model of driving behaviour at RLXs which will be used to estimate the effects of such new technology on a road network featuring RLX for different market penetrations using a traffic simulation. This methodology can assist in evaluating future safety interventions at RLXs

Human response to vibration in residential environments (NANR209), technical report 3 : calculation of vibration exposure

The Technical Report 3 describes the research undertaken to develop a methodology by which human exposure to vibration in residential environments can be calculated. That work has carried out by the University of Salford supported by the Department of environment food and rural affairs (Defra). The overall aim of the project is to derive exposure-response relationships for human vibration in residential environments. This document in particular focuses on the methods used to calculate vibration exposure from measured vibration signals due to different sources. The main objective of this report is to describe the different approaches used for calculating the different source-specific exposure. Reported here are findings obtained and a description of the feasibility of the methods used for evaluating exposure for different sources. In addition, an evaluation of the uncertainty related to the exposure calculation is considered

Methodology for evaluating the safety level of current accepted design solutions for limiting fire spread between buildings

External fire spread between buildings is internationally considered as a major concern for buildings in dense urban environments. While design guidelines differ between countries, the fundamental methods currently used for limiting the risk of fire spread between buildings are generally limited to specifying the minimum required separation distance for a given unprotected façade area, or conversely, limiting the maximum allowable unprotected façade area for a given separation distance. The safety level associated with the current design guidelines is however unknown, making the implementation of innovative, safer and more cost-effective design solutions difficult. In order to assess the safety target implicitly incorporated in currently accepted design solutions, a methodology is developed for evaluating the annual probability of reaching unacceptable radiation intensities at the opposite façade. As a case study, the methodology is applied to a design which is in agreement with the current UK requirements specified in BR 187. This case study exposes inconsistencies in the current design guidelines, indicating the need for developing explicit safety targets

A methodology for collecting valid software engineering data

An effective data collection method for evaluating software development methodologies and for studying the software development process is described. The method uses goal-directed data collection to evaluate methodologies with respect to the claims made for them. Such claims are used as a basis for defining the goals of the data collection, establishing a list of questions of interest to be answered by data analysis, defining a set of data categorization schemes, and designing a data collection form. The data to be collected are based on the changes made to the software during development, and are obtained when the changes are made. To insure accuracy of the data, validation is performed concurrently with software development and data collection. Validation is based on interviews with those people supplying the data. Results from using the methodology show that data validation is a necessary part of change data collection. Without it, as much as 50% of the data may be erroneous. Feasibility of the data collection methodology was demonstrated by applying it to five different projects in two different environments. The application showed that the methodology was both feasible and useful

Scalable aggregation predictive analytics: a query-driven machine learning approach

We introduce a predictive modeling solution that provides high quality predictive analytics over aggregation queries in Big Data environments. Our predictive methodology is generally applicable in environments in which large-scale data owners may or may not restrict access to their data and allow only aggregation operators like COUNT to be executed over their data. In this context, our methodology is based on historical queries and their answers to accurately predict ad-hoc queries’ answers. We focus on the widely used set-cardinality, i.e., COUNT, aggregation query, as COUNT is a fundamental operator for both internal data system optimizations and for aggregation-oriented data exploration and predictive analytics. We contribute a novel, query-driven Machine Learning (ML) model whose goals are to: (i) learn the query-answer space from past issued queries, (ii) associate the query space with local linear regression & associative function estimators, (iii) define query similarity, and (iv) predict the cardinality of the answer set of unseen incoming queries, referred to the Set Cardinality Prediction (SCP) problem. Our ML model incorporates incremental ML algorithms for ensuring high quality prediction results. The significance of contribution lies in that it (i) is the only query-driven solution applicable over general Big Data environments, which include restricted-access data, (ii) offers incremental learning adjusted for arriving ad-hoc queries, which is well suited for query-driven data exploration, and (iii) offers a performance (in terms of scalability, SCP accuracy, processing time, and memory requirements) that is superior to data-centric approaches. We provide a comprehensive performance evaluation of our model evaluating its sensitivity, scalability and efficiency for quality predictive analytics. In addition, we report on the development and incorporation of our ML model in Spark showing its superior performance compared to the Spark’s COUNT method

Office design for the multi-generational knowledge workforce

Purpose – The purpose of this paper is to evaluate the impact the workplace can have on knowledge working for a multi-generational workforce. Design/methodology/approach – A case study analysis is undertaken of Leeds City Council (LCC) workplace in the UK. Findings – The findings from the study show that in the context of LCC there are some key differences between the generations regarding knowledge working preferences for formal/informal meeting spaces. In other aspects, such as knowledge sharing, the generations appear to agree on key aspects such as mentoring and team-based working environments. Practical implications – Corporate real estate managers can use the research findings to assist them in providing a range of workplace settings to enhance multi-generational interaction. Originality/value – This paper fills a gap in current research by evaluating workplace preferences based on generational differences.</p

Current state of ASoC design methodology

This paper gives an overview of the current state of ASoC design methodology and presents preliminary results on evaluating the learning classifier system XCS for the control of a QuadCore. The ASoC design methodology can determine system reliability based on activity, power and temperature analysis, together with reliability block diagrams. The evaluation of the XCS shows that in the evaluated setup, XCS can find optimal operating points, even in changed environments or with changed reward functions. This even works, though limited, without the genetic algorithm the XCS uses internally. The results motivate us to continue the evaluation for more complex setups