Mapping customer needs to engineering characteristics: an aerospace perspective for conceptual design

Designing complex engineering systems, such as an aircraft or an aero-engine, is immensely challenging. Formal Systems Engineering (SE) practices are widely used in the aerospace industry throughout the overall design process to minimise the overall design effort, corrective re-work, and ultimately overall development and manufacturing costs. Incorporating the needs and requirements from customers and other stakeholders into the conceptual and early design process is vital for the success and viability of any development programme. This paper presents a formal methodology, the Value-Driven Design (VDD) methodology that has been developed for collaborative and iterative use in the Extended Enterprise (EE) within the aerospace industry, and that has been applied using the Concept Design Analysis (CODA) method to map captured Customer Needs (CNs) into Engineering Characteristics (ECs) and to model an overall ‘design merit’ metric to be used in design assessments, sensitivity analyses, and engineering design optimisation studies. Two different case studies with increasing complexity are presented to elucidate the application areas of the CODA method in the context of the VDD methodology for the EE within the aerospace secto

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—oversampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates

Development of titanium dioxide nanoparticles/nanosolution for photocatalytic activity

Biological and chemical contaminants by man-made activities have been serious global issue. Exposure of these contaminants beyond the limits may result in serious environmental and health problem. Therefore, it is important to develop an effective solution that can be easily utilized by mankind. One of the effective ways to overcome this problem is by using titanium dioxide (TiO2). TiO2 is a well-known photocatalyst that widely used for environmental clean-up due to its ability to decompose organic pollutant and kill bacteria. Although it is proven TiO2 has an advantage to solve this concern, its usefulness unfortunately is limited only under UV light irradiation. Therefore, the aim of this work was to investigate the potential of TiO2 that can be activated under visible light by the incorporation of metal ions (Fe, Ag, Zr and Ag-Zr). In this study, sol-gel method was employed for the synthesis of metal ions incorporated TiO2. XRD analysis revealed that all samples content biphasic anatase-brookite TiO2 of size 3 nm to 5 nm. It was found that the incorporation of these metal ions did not change the morphology of TiO2 but the crystallinity and optical properties were affected. The crystallinity of anatase in the biphasic TiO2 was found to be decreased and favored brookite formation. PL analysis showed metal ions incorporation suppressed the recombination of electron-hole pairs while the band gap energy of TiO2 (3.2 eV) was decreased by the incorporation of Fe (2.46 eV) and Ag (2.86 eV). Among this incorporation, Ag-Zr incorporated TiO2 showed highest performance for methyl orange degradation (93%) under fluorescent xxv light irradiation for 10 h. This follows by Zr-TiO2 (82%), Fe-TiO2 (75%) and Ag�TiO2 (43%). Meanwhile, the highest antibacterial performance was exhibited by Ag�TiO2. TEM images showed that E.coli bacterium was killed within 12 h after treated with Ag-TiO2. The results obtained from the fieldwork study established that Ag-Zr incorporation have excellent performances for VOC removal and antibacterial test. The VOC content after treated with Ag-Zr-TiO2 fulfilled the Industry Code of Practice on Indoor Air Quality 2010 which is lower than 3 ppm. In addition, the percentage of microbes also found to be decrease around 45 % within 5 days of monitoring

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology