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The effectiveness of deployment policies to promote the uptake of residential rooftop solar photovoltaic systems ultimately hinges on the behaviour of households who decide to accept or reject the technology. Over the past years, research has strived to understand, designate significant predictors, model the behaviour of heterogeneous households, and predict the diffusion rate by putting a wide range of approaches in place. Inspired by compiling a comprehensive database of home solar adoption studies, the present study systematically reviews the adopted theories, methods and approaches used within 199 original quantitative, qualitative, statistical, and non-statistical articles covering households’ attitudes, awareness, tendencies, knowledge, motives, willingness, intentions, and adoption decisions. The study provides a critical analysis of investigations on the adoption of solar photovoltaics, solar home systems, and solar photovoltaics coupled with battery energy storage systems. The outcome of the review revealed 108 future and 91 retroactive studies that exploit 10 key dependent variables by means of 52% primary (empirical data), 34% secondary (available sources), and 13% covering both modes of data collection. The sensitivity of these dependent variables was tested by 36 intervention variables that seek to capacitate effective managerial policies. The complexity of the individual decision was comprehended by 13 forms of behavioural theories, with the top-ranked two being the diffusion of innovation and the theory of planned behaviour. The literature showcased a total of 170 quantitative, 20 qualitative, and nine mixed-method studies, with statistical and non-statistical techniques being applied 139 and 86 times, respectively. Regression analysis was the most commonly used statistical analysis method, followed by spatial analysis for non-statistical models. At the heart of the predictive methods for analysing the diffusion rate of these solar technologies, nuances of 25 agent-based models and their social networks were examined in depth. The review further revealed 14 spectra of household categories as well as 12 typologies of household comparisons.

A complex systems model is necessary to holistically address the end-of-life (EoL) solar panel waste problem. However, there is a significant challenge in communicating such a model to stakeholders. Serious games can overcome this challenge by simplifying a complex model via a user-friendly interface. It enables stakeholders to experiment with different decisions and understand their long-term impacts in a safe environment. In this paper, a serious game called R3SOLVE was designed from a previously developed system dynamics (SD) model. The goal of the game is to achieve certain collection and recovery outcomes through a mix of decisions ranging from product stewardship strategies, landfill regulation, technological investment, promotional effort, reuse strategy, and infrastructure improvement. The game has a single player mode, where a player can access all decisions, and a multiplayer (turn-based) mode, where two players with different roles work collaboratively to achieve the desired outcome. Rewards and penalties also exist in the game to promote players’ extrinsic motivation to use critical thinking. Both game modes have been tested in separate workshops to identify bugs and issues regarding goal clarity and in-game information. Future directions to conduct stakeholder workshops and the evaluation approach are also suggested at the end of this paper.

Water utility companies in urban areas face two major challenges: ensuring there is enough water for everyone during prolonged drought and maintaining adequate water pressure during the hours of peak demand. These issues can be overcome by applying data analytics and machine learning to the data gathered from digital water meters. For water conservation and demand management strategies to be effective, utility companies need to gain a better understanding of consumer behaviours, habits and routines. To accomplish this goal, we adapted a clustering approach to reveal residential water consumption patterns within metered data. In the experiment, we used two data sets (engineered features data set as well as the times of use and weighted probabilities of use data set) based on the data collected over 10 months from 306 households in Melbourne, Australia. For the engineered features data set, first, we identified the number of optimal clusters. We then performed extensive experiments to find the best clustering approach in terms of performance evaluation and clustering quality. We chose the hierarchical agglomerative clustering technique based on the nature of the data and the objective of the study. We observed that for the engineered features data set, k-means is the best performing clustering technique after considering performance metrics. For the other data set, we found that the number of clusters varies based on the type of water-consumption event, type of day (i.e., weekday or weekend), profiling interval and probability of use. In addition, we observed that insight into tap-water usage could be used to determine the population’s adaptation of hygiene practices in an unprecedented time, such as the COVID-19 pandemic. Finally, we recommend that future clustering studies also employ aligned socio-demographic data and other key features.

This work examines the effect of energy storage systems (ESSs) operation on the voltage stability and quality of the local power system. The variation of these two voltage dimensions is expressed in a collective manner by the novel voltage stability and quality index ( V S Q I ). For the calculation of the V S Q I , a complete voltage stability curve is required, and the coordinates associated with voltage stability and quality are identified. The distance between the abovementioned coordinates, that collectively indicates the effect of ESSs operation on the two voltage dimensions, is represented by the V S Q I . The process for V S Q I identification is repeated for each operating scenario, with the studied cases including grid operation prior to ESSs utilisation, grid support performed by a community level (CL) ESS, and grid operation with distributed (DIST) ESSs. The ESSs are powered by renewable energy sources (RESs), and a future scenario of 70% photovoltaic (PV) penetration was examined. Other ESSs applications presented in this work, include time-shifting of the energy produced by RESs, peak load shaving, and reducing the loading levels of transmission lines and of the substation transformer. The above stack of ESSs applications, highlights the multifunctional role of ESSs as support to the grid. A real low voltage (LV) distribution network located in south-east Queensland, Australia was employed for the study.

Most accidents in the aviation, maritime, and construction industries are caused by human error, which can be traced back to impaired mental performance and attention failure. In 1596, Du Laurens, a French anatomist and medical scientist, said that the eyes are the windows of the mind. Eye tracking research dates back almost 150 years and it has been widely used in different fields for several purposes. Overall, eye tracking technologies provide the means to capture in real time a variety of eye movements that reflect different human cognitive, emotional, and physiological states, which can be used to gain a wider understanding of the human mind in different scenarios. This systematic literature review explored the different applications of eye tracking research in three high-risk industries, namely aviation, maritime, and construction. The results of this research uncovered the demographic distribution and applications of eye tracking research, as well as the different technologies that have been integrated to study the visual, cognitive, and attentional aspects of human mental performance. Moreover, different research gaps and potential future research directions were highlighted in relation to the usage of additional technologies to support, validate, and enhance eye tracking research to better understand human mental performance.

© 2018 Elsevier Ltd Advanced metering technologies coupled with informatics creates an opportunity to form digital multi-utility service providers. These providers will be able to concurrently collect a customers’ medium-high resolution water, electricity and gas demand data and provide user-friendly platforms to feed this information back to customers and supply/distribution utility organisations. Providers that can install low-cost integrative systems will reap the benefits of derived operational synergies and access to mass markets not bounded by historical city, state or country limits. This paper provides a vision of the required transformative process and features of an integrated multi-utility service provider covering the system architecture, opportunities and benefits, impediments and strategies, and business opportunities. The heart of the paper is focused on demonstrating data modelling processes and informatics opportunities for contemporaneously collected demand data, through illustrative examples and four informative water-energy nexus case studies. Finally, the paper provides an overview of the transformative R&D priorities to realise the vision.

Current practice for the design of an urban water system usually relies on various models that are often founded on a number of assumptions on how bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters and advanced data analytics allow for a new era of using the continuous ‘big data’ generated by these meter fleets to create an intelligent system for urban water management to overcome this problem. The aim of this research is to provide infrastructure planners with a detailed understanding of how granular data generated by an intelligent water management system (Autoflow©) can be utilised to obtain significant efficiencies throughout different stages of an urban water cycle, from supply, distribution, customer engagement, and even wastewater treatment.

Long-term sustainable wind energy deployment faces an array of challenges due to various complex interconnected impediment factors. These inherent endogenous and exogenous uncertainties preclude obtaining an accurate future trend, which in turn complicates the design of the good policy. This study employs Fuzzy Cognitive Maps (FCMs) to semi-quantitatively explore the scenarios of wind energy deployment in Iran. The FCM-based framework was built using participatory workshops and a subsequent questionnaire survey to identify 26 influential factors shaping the dynamics of the system. The developed scenarios originated from the latest narratives of real-world geopolitical variations. The findings demonstrated that the sector is governed by six major groupings of factors predominated by economic and political concepts with strong interconnections between them. Five key concepts, including two economic, one legal, and two political, were ascertained that contribute to the stability of the system. Of the four scenarios, only one optimistic trajectory expects an acceleration in the deployment. Another scenario projects that there will not be any considerable change between the future scheme and the current state of development. However, the other scenarios envisage a substantially slower growth than the current trend.

Workplace innovation enables the development and improvement of products, processes and services leading simultaneously to improvement in organisational performance. This study has the purpose of examining the factor structure of workplace innovation. Survey data, extracted from the 2014 APS employee census, comprising 3,125 engineering professionals in the Commonwealth of Australia’s departments were analysed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA returned a two-factor structure explaining 69.1% of the variance of the construct. CFA revealed that a two-factor structure was indicated as a validated model (GFI = 0.98, AGFI = 0.95, RMSEA = 0.08, RMR = 0.02, IFI = 0.98, NFI = 0.98, CFI = 0.98, and TLI = 0.96). Both factors showed good reliability of the scale (Individual creativity:α= 0.83, CR = 0.86, and AVE = 0.62; Team Innovation:α= 0.82, CR = 0.88, and AVE = 0.61). These results confirm that the two factors extracted for characterising workplace innovation included individual creativity and team innovation.

The complexity of patient‐specific implants combined with the current limited expertise in reliability engineering and manufacturability in the additive manufacturing (AM) sector is posing a number of quality performance challenges. Worldwide medical device regulatory bodies are facing increasing pressure to devise adequate standards to ensure long‐term patient safety and product performance. The implementation of the Quality by Design (QbD) system to titanium 3D‐printed bone implants offers a proven system to ensure that products are designed and manufactured correctly from the beginning without errors. This article reports on the development of a failure mode, effects, and criticality analysis (FMECA) coupled with a 3D risk assessment approach. This integrated approach is based on a questionnaire performed with three industry firms and three university research groups with significant experience and expertise in medical device product development and/or research in this field. Research outcomes include a FMECA form containing 137 failure modes with AM materials, AM machine general, fabrication, electron beam melting machine, finishing, and design being as the most sensitive process areas in terms of product quality. We subsequently propose corresponding preventive and corrective strategies for risk mitigation. The approach forms part of the QbD system being developed by the authors specifically for additive manufactured titanium patient‐specific implants.

This article discusses a procedure of mixed methods sequential explanatory design used to conduct sequential QUAN → QUAL mixed methods study. The methodological procedures are explained using a mixed methods study of innovation in the Australian Public Service (APS). The sequential explanatory design incorporated quantitative and qualitative approaches in two consecutive phases within one study. The quantitative method (i.e. questionnaire survey) was conducted in the first stage, followed by the qualitative approach using thematic analysis. The questionnaire survey data to be complemented by an archival analysis approach under a two-phase analysis. The archival analysis provided fresh context to understand the innovation process in the APS. The findings from both phases of the study were then examined and combined to draw the conclusions. This study made several contributions to the body of knowledge related to research methodology by not only adopting a quantitative-dominant mixed method approach, but also by employing integrated methods used for a deeper understanding of the impact of socio-psychological constructs on workplace innovation and career satisfaction.

Unlike subtractive manufacturing technologies, additive manufacturing (AM) can fabricate complex shapes from the macro to the micro scale, thereby allowing the design of patient-specific implants following a biomimetic approach for the reconstruction of complex bone configurations. Nevertheless, factors such as high design variability and changeable customer needs are re-shaping current medical standards and quality control strategies in this sector. Such factors necessitate the urgent formulation of comprehensive AM quality control procedures. To address this need, this study explored and reported on a variety of aspects related to the production and the quality control of additively manufactured patient-specific implants in three different AM companies. The research goal was to develop an integrated quality control procedure based on the synthesis and the adaptation of the best quality control practices with the three examined companies and/or reported in literature. The study resulted in the development of an integrated quality control procedure consisting of 18 distinct gates based on the best identified industry practices and reported literature such as the Food and Drug Administration (FDA) guideline for AM medical devices and American Society for Testing and Materials (ASTM) standards, to name a few. This integrated quality control procedure for patient-specific implants seeks to prepare the AM industry for the inevitable future tightening in related medical regulations. Moreover, this study revealed some critical success factors for companies developing additively manufactured patient-specific implants, including ongoing research and development (R&amp, D) investment, investment in advanced technologies for controlling quality, and fostering a quality improvement organizational culture.

© 2019 IEEE. Recommender systems assist customers to make decisions; however, the modest adoption of digital technology in the water industry means no such system exists for household water users. Such a system for the water industry would suggest to consumers the most effective ways to conserve water based on their historical data from smart water meters. The advantage for water utilities in metropolitan areas is in managing demand, such as low pressure during peak hours or water shortages during drought. For customers, effective recommendations could save them money. This paper presents a novel vision of a recommender system prototype and discusses the benefits both for the consumers and the water utility companies. The success of this type of system would depend on the ability to anticipate the time of the next major water use so as to make useful, timely recommendations. Hence, the prototype is based on a long short-term memory (LSTM) neural network that predicts significant water consumption events (i.e., showers, baths, irrigation, etc.) for 83 households. The preliminary results show that LSTM is a useful method of prediction with an average root mean square error (RMSE) of 0.403. The analysis also provides indications of the scope of further research required for developing a commercially successful recommender system.

The advent of advanced metering technologies combined with machine learning creates an opportunity to form digital multi-utility service providers. These service providers can concurrently collect a customers' water, electricity and gas demand data and provide phone and web applications to disseminate this information back to utility professionals and customers. Service companies that can provide inexpensive integrated systems will derive benefits such as operational synergies and access to mass markets not bounded by historical city, state or country limits. This article provides a vision of an integrated multi-utility service provider covering the system architecture, barriers and strategies, and opportunities and benefits. The focus of paper is to demonstrate the data modelling processes and informatics opportunities for contemporaneously collected water, electricity and gas demand data. Moreover, a transformative R&D framework is provided to realize this digital multi-utility future.

This paper systematically reviewed system dynamics applications in water resource management with respect to spatial factors, research aims, modelling sub-systems and model calibration and validation methods. Decision-making context, consideration of climate change, scenarios or management measures were also examined. Results showed that the critical conceptual system dynamics model development phases are often neglected with only 40% of reviewed articles developing causal loop diagrams, and only three studies identifying system archetypes. Most reviewed publications applied scenario-based approaches (82%) to evaluate the effectiveness of management measures, whilst a minority of studies (1.8%) considered water management optimisation. Structure behaviour and behaviour-pattern tests (52%) were mostly applied to evaluate the validity of the model structure and accuracy of the behaviour's pattern, though model validation was not conducted in 12% of the studies. Finally, an integrated system dynamics modelling framework was proposed, exploring opportunities for integration of system dynamics with other modelling tools based on the strengths and limitations of system dynamics modelling; this framework can be used to enhance the simulation and optimisation for future water resource management and planning in the context of climate change and socio-economic development.

Climate change mitigation strategies are multifaceted and require collaboration among a range of stakeholder groups. The objective of this paper was to develop an overarching Renewable Energy and Energy Conservation Area Policy (REECAP) framework. The framework was developed based on a comprehensive literature review, in which seven principles for Renewable Energy and Energy Conservation Policies were identified. The paper also includes a case study to demonstrate an application of the REECAP framework. The novelty of the framework stems from its integration of carbon-energy-cash flows among different decision-making spheres, scales and area specific characteristics. The framework provides a mathematical understanding of how energy strategies can be transformed and optimised in a cost-effective manner by integrating stakeholders under a shared vision.

A comprehensive review was conducted to assess the current state of monitoring approaches for primary faecal indicator bacteria (FIB) E. coli and enterococci. Approaches were identified and examined in relation to their accuracy, ability to provide continuous data and instantaneous detection results, cost, environmental awareness regarding necessary reagent release or other pollution sources, in situ monitoring capability, and portability. Findings showed that several methods are precise and sophisticated but cannot be performed in real-time or remotely. This is mainly due to their laboratory testing requirements, such as lengthy sample preparations, the requirement for expensive reagents, and fluorescent tags. This study determined that portable fluorescence sensing, combined with advanced modelling methods to compensate readings for environmental interferences and false positives, can lay the foundations for a hybrid FIB sensing approach, allowing remote field deployment of a fleet of networked FIB sensors that can collect high-frequency data in near real-time. Such sensors will support proactive responses to sudden harmful faecal contamination events. A method is proposed to enable the development of the visioned FIB monitoring tool.

Cyanobacteria harmful blooms (CyanoHABs) in lakes and reservoirs represent a major risk for water authorities globally due to their toxicity and economic impacts. Anticipating bloom occurrence and understanding the main drivers of CyanoHABs are needed to optimize water resources management. An extensive review of the application of CyanoHABs forecasting and predictive models was performed, and a summary of the current state of knowledge, limitations and research opportunities on this topic is provided through analysis of case studies. Two modelling approaches were used to achieve CyanoHABs anticipation; process-based (PB) and data-driven (DD) models. The objective of the model was a determining factor for the choice of modelling approach. PB models were more frequently used to predict future scenarios whereas DD models were employed for short-term forecasts. Each modelling approach presented multiple variations that may be applied for more specific, targeted purposes. Most models reviewed were site-specific. The monitoring methodologies, including data frequency, uncertainty and precision, were identified as a major limitation to improve model performance. A lack of standardization of both model output and performance metrics was observed. CyanoHAB modelling is an interdisciplinary topic and communication between disciplines should be improved to facilitate model comparisons. These shortcomings can hinder the adoption of modelling tools by practitioners. We suggest that water managers should focus on generalising models for lakes with similar characteristics and where possible use high frequency monitoring for model development and validation.

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies., Key Points: We investigate which water use behaviors can be identified from nonintrusive, single‐point, smart meter data We identify and cluster primary water use behaviors of single‐family households from disaggregated end use data We reveal the main water end uses driving different behaviors, usage patterns, and regularity, to support customized demand management

An integrated approach combining Bayesian Network with GIS was developed for making a probabilistic prediction of sea level rise induced coastal erosion and assessing the implications of adaptation measures. The Bayesian Network integrates extensive qualitative and quantitative information into a single probabilistic model while GIS explicitly deals with spatial data for inputting, storing, analysing and mapping. The integration of the Bayesian Network with GIS using a cell-by-cell comparison technique (aka map algebra) provides a new tool to perform the probabilistic spatial analysis. The spatial Bayesian Network was utilised for predicting coastal erosion scenarios at the case study location of Tanna Island, Vanuatu in the South Pacific. Based on the Bayesian Network model, a rate of the island shoreline change was predicted probabilistically for each shoreline segment, which was transferred into GIS for visualisation purposes. The spatial distribution of shoreline change prediction results for various sea level rise scenarios was mapped. The outcomes of this work support risk-based adaptation planning and will be further developed to enable the incorporation of high resolution coastal process models, thereby supporting localised land use planning decisions.

Climate change adaptation planning requires the integration of disciplines, stakeholders, different modelling approaches, treatment options, and scales of analysis. An integrated stepwise planning approach is a critical requirement for effective climate change adaptation in the context of small island developing states and coastal communities. To address this need, this paper reports on a systematic review of 116 research papers from an initial set of around 650 academic peer-reviewed papers. These papers were assessed and categorised based on their planning framework or the approach utilised, measured climate change impacts, employed methods and tools, and recommended adaptation strategies or options. This study identified three important dimensions of a fully integrated climate change adaptation planning process, namely, integration in assessment, integration in modelling, and integration in adaptive responses. Moreover, it resulted in the formulation of a novel multi-layered integrative climate change adaptation planning approach. Adopting this holistic and integrative approach is more likely to yield better climate change adaptation in planning outcomes over the long term.

This paper presents a novel five-stage integrated participatory systems modelling (IPSM) approach that can be used for a range of systems dynamics (SD) applications. The IPSM approach was formulated considering the advantages and disadvantages of existing SD modelling approaches, as well as balancing the competing goals of SD model development efficiency and robustness. A key feature of the IPSM approach is that stakeholders are central to each of the five stages of the modelling process from problem scoping, to scenario analysis and strategy implementation recommendations. Each stage of the IPSM approach was demonstrated through a case study of the innovation diffusion process in the Russian Federation construction industry. This highly complex innovation system could only be sufficiently understood using a SD model that was conceptualised, critiqued, codified, tested and utilised, by the relevant actors within that system (i.e., stakeholders). The IPSM approach facilitated the efficient formulation of the SD model for the case study application. The case study SD model simulation results indicate that sufficient government incentives and the active promotion of strong collaborative linkages between construction companies and universities are two key enablers of innovation development in the Russian Federation construction industry.

A regression model integrating data pre-processing and transformation, input selection techniques and a data-driven statistical model, facilitated accurate 7 day ahead time series forecasting of selected water quality parameters. A core feature of the modelling approach is a novel recursive input–output algorithm. The herein described model development procedure was applied to the case of a 7 day ahead dissolved oxygen (DO) concentration forecast for the upper hypolimnion of Advancetown Lake, Queensland, Australia. The DO was predicted with an R2 > 0.8 and a normalised root mean squared error of 14.9% on a validation data set by using 10 inputs related to water temperature or pH. A key feature of the model is that it can handle nonlinear correlations, which was essential for this environmental forecasting problem. The pre-processing of the data revealed some relevant inputs that had only 6 days' lag, and as a consequence, those predictors were in-turn forecasted 1 day ahead using the same procedure. In this way, the targeted prediction horizon (i.e. 7 days) was preserved. The implemented approach can be applied to a wide range of time-series forecasting problems in the complex hydro-environment research area. The reliable DO forecasting tool can be used by reservoir operators to achieve more proactive and reliable water treatment management.

Smart metering technology enables the capture of high resolution water consumption data.Intelligent algorithms autonomously categorise single and combined water end use events.Hybrid combination of HMM, ANN and DTW for pattern recognition problem.Expert system developed to autonomously disaggregate water use into end use categories. Over half of the world's population will live in urban areas in the next decade, which will impose significant pressure on water security. The advanced management of water resources and their consumption is pivotal to maintaining a sustainable water future. To contribute to this goal, the aim of this study was to develop an autonomous and intelligent system for residential water end-use classification that could interface with customers and water business managers via a user-friendly web-based application. Water flow data collected directly from smart water metres connected to dwellings includes both single (e.g., a shower event occurring alone) and combined (i.e., an event that comprises several overlapping single events) water end use events. The authors recently developed an intelligent application called Autoflow which served as a prototype tool to solve the complex problem of autonomously categorising residential water consumption data into a registry of single and combined events. However, this first prototype application achieved overall recognition accuracy of 85%, which is not sufficient for a commercial application. To improve this accuracy level, a larger dataset consisting of over 82,000 events from over 500 homes in Melbourne and South-east Queensland, Australia, were employed to derive a new single event recognition method employing a hybrid combination of Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and the Dynamic Time Warping (DTW) algorithm. The classified single event registry was then used as the foundations of a sophisticated hybrid ANN-HMM combined event disaggregation module, which was able to strip apart concurrently occurring end use events. The new hybrid model's recognition accuracy ranged from 85.9% to 96.1% for single events and 81.8-91.5% for combined event disaggregation, which was a 4.9% and 8.0% improvement, respectively, when compared to the first prototype model. The developed Autoflow tool has far-reaching implications for enhanced urban water demand planning and management, sustained customer behaviour change through more granular water conservation awareness, and better customer satisfaction with water utility providers.

3D printing is an emergent manufacturing technology recently being applied in the medical field for the development of custom bone prostheses and scaffolds. However, successful industry transformation to this new design and manufacturing approach requires technology integration, concurrent multi-disciplinary collaboration, and a robust quality management framework. This latter change enabler is the focus of this study. While a number of comprehensive quality frameworks have been developed in recent decades to ensure that the manufacturing of medical devices produces reliable products, they are centred on the traditional context of standardised manufacturing techniques. The advent of 3D printing technologies and the prospects for mass customisation provides significant market opportunities, but also presents a serious challenge to regulatory bodies tasked with managing and assuring product quality and safety. Before 3D printing bone prostheses and scaffolds can gain traction, industry stakeholders, such as regulators, clients, medical practitioners, insurers, lawyers, and manufacturers, would all require a high degree of confidence that customised manufacturing can achieve the same quality outcomes as standardised manufacturing. A Quality by Design (QbD) approach to custom 3D printed prostheses can help to ensure that products are designed and manufactured correctly from the beginning without errors. This paper reports on the adaptation of the QbD approach for the development process of 3D printed custom bone prosthesis and scaffolds. This was achieved through the identification of the Critical Quality Attributes of such products, and an extensive review of different design and fabrication methods for 3D printed bone prostheses. Research outcomes include the development of a comprehensive design and fabrication process flow diagram, and categorised risks associated with the design and fabrication processes of such products. An extensive systematic literature review and post-hoc evaluation survey with experts was completed to evaluate the likely effectiveness of the herein suggested QbD framework.

Purpose– The purpose of this paper is to empirically develop a safety performance (SP) framework for evaluating the various leading and lagging indicators of SP in Saudi public schools.Design/methodology/approach– A comprehensive review of the safety and performance evaluation literature enabled the formulation of the constructs, factors and items that underpinned a conceptual SP evaluation framework. The research method involved a conceptual framework review and refinement process with 18 experts, followed by a questionnaire survey of 200 respondents from Saudi public schools. The final form of the SP evaluation framework was determined through a rigorous statistical analysis process, including exploratory factor analysis to uncover the underlying framework architecture from a relatively large set of items, and then the application of the partial least squares method to confirm framework adequacy and to assign loading coefficients.Findings– The developed SP evaluation framework for Saudi schools included five constructs, namely, safety management and leadership, safety learning and training, safety policy, procedures, and processes, workforce safety culture and SP.Practical implications– The developed SP evaluation framework is proposed for implementation by the Saudi Ministry of Education to better monitor and manage school safety in the future.Originality/value– This SP evaluation framework is one of the first to holistically evaluate SP in schools, particularly in the Saudi Arabian context.

Intelligent metering technology combined with advanced numerical techniques enable a paradigm shift in the current level of water consumption information provision that is available to the customer and the water business. The aim of this study was to develop an autonomous and intelligent system for residential water end-use classification that could interface with customers and water business managers via a user-friendly web-based application. Water flow data collected directly from smart water meters includes both single (e.g., a shower event occurring alone) and combined (i.e., an event that comprises several overlapping single events) water end use events. The authors recently developed intelligent algorithms to solve the complex problem of autonomously categorising residential water consumption data into a registry of single and combined events using a hybrid combination of techniques including Hidden Markov Model (HMM), Dynamic Time Warping (DTW) algorithm, time-of-day probability functions, threshold values and various physical features. However, the issue still remained, which is the focus of this current paper, on how to integrate self-learning functionality into the visioned expert system, in order that it can learn from newly collected datasets from different cities, regions and countries, to that collected for the training data. Such versatility and adaptive capacity is essential to make the expert system widely applicable. Through applying alternate forms of HMM and DTW in association with a frequency analysis technique, a suitable self-learning methodology was formulated and tested on three independent households located in Melbourne, Australia with a prediction accuracy of between 80% and 90% for the major end-use categories. The three principle flow data processing modules (i.e., single and combined event recognition and self-learning function) were integrated into a prototype software application for performing autonomous water end-use analysis and its functionality is presented in the latter sections of this paper. The developed expert system has profound implications for government, water businesses and consumers, seeking to better manage precious urban water resources.

The aim of this study was to disaggregate water flow data collected from high resolution smart water meters into different water end use categories. The data was obtained from a sample of 252 residential dwellings located within South East Queensland (SEQ), Australia. An integrated approach was used, combining high resolution water meters, remote data transfer loggers, household water appliance audits and a self-reported household water use diary. Disaggregating water flow traces into a registry of end use events (e.g. shower, clothes washer, etc.) is predominately a complex pattern matching problem, which requires a comparison between presented patterns and those contained with a large registry of categorised end use events. Water flow data collected directly from water meters includes both single (e.g. shower event occurring alone) and combined events (i.e. an event which comprises of several overlapped single events). To identify these former mentioned single events, a hybrid combination of the Hidden Markov Model (HMM) and the Dynamic Time Warping algorithm (DTW) provided the most feasible and accurate approach available. Additional end use event physical context algorithms have been developed to aid accurate end use event categorisation. This paper firstly presents a thorough discussion on the single water end use event analysis process developed and its internal validation with a testing set. This is followed by the application of the developed approach on three independent households to examine its degree of accuracy in disaggregating two weeks of residential flow data into a repository of residential water end use events. Future stages of algorithm development and testing is discussed in the final section.

Opportunities for replicating large data sets that have already been collected by government, and made available to the public to provide the possibility of statistical data analysis, are starting to emerge. This study examines the factor structure of ambidextrous culture for innovation. Survey data was extracted from the State of the Service Employee Census 2014 comprising 3,125 engineering professionals in Commonwealth of Australia departments. Data were analysed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA returned a two-factor structure explaining 61.1% of the variance of the construct. CFA revealed that a two-factor structure was indicated as a validated model (GFI = 0.99, AGFI = 0.98, RMSEA = 0.05, RMR = 0.02, IFI = 0.99, NFI = 0.99, CFI = 0.99, and TLI = 0.98). From the results, the two factors extracted as characterising ambidextrous culture for innovation were innovative culture and performance-oriented culture.

This research integrates systemic and participatory techniques to model the Russian Federation construction innovation system. Understanding this complex construction innovation system, and determining the best levers for enhancing it, requires the dynamic modelling of a number of factors such as flows of resources and activities, policies, uncertainty and time. To build the foundations for such a dynamic model, the employed study method utilised an integrated stakeholder-based participatory approach coupled with structural analysis (MICMAC - Matrice d'Impacts Croisés Multiplication Appliquée à un Classement Cross-Impact Matrix). This method identified the key factors of the Russian Federation construction innovation system, their causal relationship (i.e. influence/dependence map) and ultimately a causal loop diagram. The generated model reveals pathways to improving construction innovation in the Russian Federation, and underpins the future development of an operationalised systems dynamic model.

A graph H is a square root of a graph G if G can be obtained from H by the addition of edges between any two vertices in H that are at distance 2 from each other. The Square Root problem is that of deciding whether a given graph admits a square root. We consider this problem for planar graphs in the context of the “distance from triviality” framework. For an integer k, a planar + k v graph (or k-apex graph) is a graph that can be made planar by the removal of at most k vertices. We prove that a generalization of Square Root , in which some edges are prescribed to be either in or out of any solution, has a kernel of size O ( k ) for planar + k v graphs, when parameterized by k. Our result is based on a new edge reduction rule which, as we shall also show, has a wider applicability for the Square Root problem.

Leading organizations do not stop at the gathering and analysis of performance data; rather, these organizations use performance measurement to stimulate improvement efforts and successfully translate strategy into action. In other words, they use performance measurement for managing their organizations. The most commonly used performance evaluation tool, the balanced scorecard (BSC) has been used extensively in the manufacturing, government, banking, retail, insurance and financial services sectors. This paper looks at potential applications and benefits of using the BSC as a framework to evaluate the performance improvement resulting from IT/IS implementation by a construction organization. The paper firstly seeks to adapt the original BSC concept to construction and then attempts to develop a performance measurement framework in the form of a tiered “Construct IT” BSC. The developed framework allows for the measurement of IT/IS performance at three different decision‐making tiers (i.e., project, business unit and enterprise tiers). At each tier, a group of performance indicators are used to describe key aspects of activities that occur frequently enough to warrant meaningful measurement and comparison. The proposed framework applies the analytic hierarchy process (AHP) and multi‐attribute utility theory (MAUT) to facilitate aggregating the obtained diverse performance measurements, thus giving rise to an overall IT/IS performance improvement measure (score) at each decision‐making tier. To illustrate the application of the proposed framework, a project tier example is provided.

Learning capability (LC) is a special dynamic capability that a firm purposefully builds to develop a cognitive focus, so as to enable the configuration and improvement of other capabilities (both dynamic and operational) to create and respond to market changes. Empirical evidence regarding the essential role of LC in leveraging operational manufacturing capabilities is, however, limited in the literature. This study takes a routine-based approach to understand capability, and focuses on demonstrating leveraging power of LC upon two essential operational capabilities within the manufacturing context, i.e., operational new product development capability (ONPDC), and operational supplier integration capability (OSIC). A mixed-methods research framework was used, which combines sources of evidence derived from a survey study and a multiple case study. This study identified high-level routines of LC that can be designed and controlled by managers and practitioners, to reconfigure underlying routines of ONPDC and OSIC to achieve superior performance in a turbulent environment. Hence, the study advances the notion of knowledge-based dynamic capabilities, such as LC, as routine bundles. It also provides an impetus for managing manufacturing operations from a capability-based perspective in the fast changing knowledge era.

In this paper we present novel techniques, methods and tool flows that enable embedded systems implemented on FPGAs to start-up under tight timing constraints (i.e., hard deadlines). Meeting the application deadline is achieved by exploiting the FPGA programmability in order to implement a two-stage system start-up approach, as well as a suitable memory hierarchy. This reduces the FPGA configuration time as well as the startup time of the embedded software. Thereby the start-up time for timing-critical parts of a design neither dependent on the complexity nor on the start-up time of the complete system. An automotive case study is used to demonstrate the feasibility and quantify the benefits of the proposed approach.

Dynamic and partial reconfiguration of Xilinx FPGAs is a well known technique for runtime adaptive system design. The technique enables to substitute parts of a configuration while other regions stay operative without any disturbance. The advantage is the fact, that the spatial and temporal partitioning can be exploited with the goal to increase the performance and to reduce the power consumption due to the re-use of chip area. However, the feature of dynamic and partial reconfiguration can be further exploited. This novel kind of exploitation is described in this chapter. FPGAs still have to compete with other solutions like e.g., processor based designs if e.g., a restricted power budget is available for a specific application domain. In order to reduce the power consumption to a minimum, many devices use different kinds of power saving modes, called sleep-or hibernating modes. In these modes, the power supply of the device is reduced or even fully down. Taking this idea to the extreme, many devices in systems are only powered at run-time when it is necessary. If not, they are released from their power supply and do not drain current at all. Due to the fact that such a power down mode leads to a complete loss of data in SRAM based FPGAs, special techniques for such designs needs to be developed. The configuration has to be reloaded into the device every time when reattaching the power to the FPGA. This circumstance leads to restrictions for the device deployment in some electronic systems since in many cases the time a device may use to wake up is strictly limited. In several use cases, the configuration time of a SRAM based FPGA exceeds this limitation and forces designers to use processors instead of FPGAs. This chapter describes a way to decrease the configuration time of a design by exploiting the method of dynamic and partial reconfiguration in order to enable the usage of a sleep mode. With the presented method, the configuration time of any Xilinx SRAM based FPGA from the identical series (e.g., Spartan 3) is independent from the size of the used device.

Knowledge has been widely recognised as a determinant of business performance. Business capabilities require an effective share of resource and knowledge. Specifically, knowledge sharing (KS) between different companies and departments can improve manufacturing processes since intangible knowledge plays an enssential role in achieving competitive advantage. This paper presents a mixed method research study into the impact of KS on the effectiveness of new product development (NPD) in achieving desired business performance (BP). Firstly, an empirical study utilising moderated regression analysis was conducted to test whether and to what extent KS has leveraging power on the relationship between NPD and BP constructs and variables. Secondly, this empirically verified hypothesis was validated through explanatory case studies involving two Taiwanese manufacturing companies using a qualitative interaction term pattern matching technique. The study provides evidence that knowledge sharing and management activities are essential for deriving competitive advantage in the manufacturing industry.

This paper introduces the first available tool flow for Dynamic Partial Reconfiguration on the Spartan-6 family. In addition, the paper proposes a new configuration method called Fast Start-up targeting modern FPGA architectures, where the FPGA is configured in two-steps, instead of using a single (monolithic) full device configuration. In this novel approach, only the timing-critical modules are loaded at power-up using the first high-priority bitstream, while the non-timing critical modules are loaded afterwards. This two-step or prioritized FPGA start-up is used in order to meet the extremely tight startup timing specifications found in many modern applications, like PCI-express or automotive applications. Finally, the developed tool flow and methods for Fast Start-up have been used and tested to implement a CAN-based automotive ECU on a Spartan-6 evaluation board (i.e., SP605). By using this novel approach, it was possible to decrease the initial bitstream size and hence, achieve a configuration time speed-up of up to 4.5×, when compared to a standard configuration solution.