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The aviation industry is one of the sectors that has been heavily impacted by the pandemic. While the major body of literature has focused on passenger experience and behaviour, this study focuses on airport employees instead—their experiences, perceptions, and preferences following the emergence of COVID-19. More than 1000 participants from 4 major airports—Amsterdam Airport Schiphol, Singapore Changi Airport, Taipei Taoyuan Airport, and Zurich Airport—representing over 10 different occupations, have provided a variety of sentiments about the airport as an employment ecosystem in the wake of COVID-19. Quantitatively and qualitatively surveying four different airports enabled a cross-border analysis of the results to identify interesting geographic contrasts, as well as global themes, among the responses. Regional differences regarding, the feeling of preparedness, confidence in measures, and optimism are presented. A significant difference in confidence in non-pharmaceutical measures between employees from Asian and European airports is shown. Wants and needs such as better physical/IT workplace infrastructure and more flexibility regarding job scope and hours are pointed out. The results of this research provide insights for future airport employee experience research by outlining areas to study in greater detail. Furthermore, practical implications for airport stakeholders and companies arising from the challenges experienced by the workforce are laid out to provide guidance to prepare for similar circumstances in the future and navigate the aftermath of and recovery from the pandemic.

Inspired by recent advancements in the field of computer vision, specifically models for generating higher-resolution images from low-resolution images, we investigate the utility of a deep convolutional autoencoder for downscaling and bias correcting climate projections for South East Asia (SEA). Downscaled projections of 2 m surface temperature are generated, using autoencoders trained with data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and data from the fifth generation ECMWF atmospheric reanalysis (ERA5) project. Using CMIP5 projections as an input, three sets of downscaled data are generated using three methods of autoencoder training, which allow us to determine how autoencoder downscaling and bias correction modify temperature values. Where possible, the downscaled outputs are compared against the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment–Southeast Asia (SEACLID/CORDEX–SEA) project and outputs from available CMIP6 experiments, to evaluate performance. The autoencoders are found to excel at the rapid generation of highly spatially-resolved climate projections for surface temperature. Realistic spatial features due to coastal and topographic variation are generated by the autoencoder, which are not present in the CMIP5 projections. Additionally, the autoencoders are capable of generating forecast data with regional temperature profiles exceeding that of those appearing in the training set (out-of-sample extrapolation). Seasonal temperature cycles are retained after downscaling throughout the region, despite the absence of temporal information provided to the model. However, autoencoders trained to carry out bias correction display a tendency to smooth daily average temperatures and reduce daily highs and lows beyond that which can be expected to be realistic. Without bias correction, downscaled outputs have a reduced improvement in spatial resolution but the daily temperature profiles of the CMIP5 input forecasts are maintained. Autoencoders rely on the presence of structural features in the datasets to carry out downscaling, and so performance over the oceans is reduced as strong temperature gradients are absent. For this reason, ocean warming is not well represented, an artefact which is not immediately clear in the downscaled outputs. This study demonstrates the importance of rigorous analysis of 'black-box' methods, which can generate non-obvious artefacts that could potentially create misleading results. Despite these limitations, Autoencoders are clearly capable of generating much needed high-resolution climate projections, and strategies to improve upon shortcomings are numerous and well established.

Productivity studies in the AEC industry has gained significant attention in the past decade, however the impact from actual industry application has not kept up. This could be attributed to the focus on construction productivity instead of engineering productivity. This paper presents a systematic literature review on engineering design productivity in AEC industry focusing on design research method applications.

The recognition of the value of design has resulted in an increased number of programs and courses that include design and evaluate design competencies. However, there is no common reference system to (1) identify and assess the design competency of learners and the level of design competency aimed for by a course or curriculum; (2) universally recognize design competencies and competency levels.Our research goal is to identify and define distinct levels of design competency and develop a framework to help instructors, design learners, institutes as well as employers assess and/or recognize competency. This paper introduces our DesCA (Design Competency Assessment) framework and places it in the context of other frameworks. We describe how DesCA helps: (1) identify and assess design competencies associated with different design activities planned for a course or curriculum; (2) formulate learning outcomes and select appropriate competency levels, methods and tools; (3) plan and develop the design content of courses and curricula; (4) ensure curricular consistency across courses.The vision is to make DesCA a digital platform that can serve as an international standard for design teaching, learning and curriculum development.

Prosthetic socket coupling with the residual limb should be comfortable without causing skin breakdown or excessive pressure. However, users report socket discomfort, and there is a scarcity of objective measurements available to assess this feeling of discomfort. Quantifying the specific pressure may enable clinicians and users to determine and improve comfort levels objectively.(1) To evaluate if a peak pressure reduction assists in resolving wounds, redness, and pain inside prosthetic sockets in people with transtibial amputation. (2) To determine if peak pressures measured inside the prosthetic socket due to external forces could be used to quantify the level of improvement in socket discomfort.In this cohort study, we used a pressure sensor to quantify and facilitate adjustments to the prosthetic socket, correlating this information to the user's socket comfort.Outpatient clinic in a tertiary hospital in Singapore.People (N = 16) with unilateral transtibial lower limb amputation using a prosthesis.NA.Peak pressure and socket comfort score (SCS).The peak pressure value showed a statistically significant reduction across all participants following adjustments at a 50% delta change in pressure (p = .001). This was achieved with a mean number of 2.6 ± 1.4 adjustments per participant. Following the adjustments, the paired t-test results showed a mean increase between the first SCS and final SCS was 2.6 (p = .001).The wound, redness, and pain resolved in 15 of 16 participants regardless of diabetic status following socket adjustments. Although the peak pressures values did not correlate to the SCS score, the reduction in peak pressure saw significant improvement to the SCS. The use of a portable sensor is a fast and efficient means to quantify adjustments inside the prosthetic socket and could potentially be considered as part of future care delivery.

This study aims to understand the main issues in design education and discuss solutions. We conducted two workshops on the state of implementation of design in the Singaporean education system at the Design Education Summit, a conference for educators. We found that the main issues were the inclusion of design education within curriculum and change of stakeholder mindsets. Several solutions were discussed, such as ways to introduce design as part of the organization culture. We summarize results into action items and recommendations.

The COVID-19 pandemic has caused an unprecedented crisis for the air transportation industry, affecting millions of aviation users and stakeholders. As the aviation sector has faced disease outbreaks and extreme events before-albeit not at the same scale-and will, in all likelihood, face them again, we provide an assessment in this study that a) gives an overview of the effects of the pandemic, b) categorizes the response mechanisms that were observed, and c) proposes a framework for a coordinated global response to future disease outbreaks. We highlight that of the many lessons, recommendations, and suggestions that emerged during previous outbreaks, few were introduced effectively into civil aviation practices and operations. Based on multiple data sources for passengers, cargo, and flight schedules, we assess the impact of COVID-19 on the global aviation industry and compare the data of some prominent airports to highlight the need for a coordinated response to effectively deal with future disruptions. As global aviation navigates its ongoing recovery, we discuss different responses during the pandemic including guidelines issued by bodies such as the International Civil Aviation Organization (ICAO), operational decisions such as closing terminals, increased cleaning frequencies, and mask mandates etc. We emphasize the need for resilience to accommodate disease outbreaks in future planning, design, and preparedness strategies for airports and airlines. We further argue that the existing civil aviation system needs a coordinated global response mechanism to combat future outbreaks and propose a framework with a threat response matrix to keep aviation safe and operational during future pandemics and mitigate socioeconomic fallout.

Classification of crisis events, such as natural disasters, terrorist attacks and pandemics, is a crucial task to create early signals and inform relevant parties for spontaneous actions to reduce overall damage. Despite crisis such as natural disasters can be predicted by professional institutions, certain events are first signaled by civilians, such as the recent COVID-19 pandemics. Social media platforms such as Twitter often exposes firsthand signals on such crises through high volume information exchange over half a billion tweets posted daily. Prior works proposed various crisis embeddings and classification using conventional Machine Learning and Neural Network models. However, none of the works perform crisis embedding and classification using state of the art attention-based deep neural networks models, such as Transformers and document-level contextual embeddings. This work proposes CrisisBERT, an end-to-end transformer-based model for two crisis classification tasks, namely crisis detection and crisis recognition, which shows promising results across accuracy and f1 scores. The proposed model also demonstrates superior robustness over benchmark, as it shows marginal performance compromise while extending from 6 to 36 events with only 51.4% additional data points. We also proposed Crisis2Vec, an attention-based, document-level contextual embedding architecture for crisis embedding, which achieve better performance than conventional crisis embedding methods such as Word2Vec and GloVe. To the best of our knowledge, our works are first to propose using transformer-based crisis classification and document-level contextual crisis embedding in the literature.

There is limited research on design guidelines for multilingual older adults, especially those with dementia, and from Southeast Asian backgrounds. In this paper, we investigate usability issues and user experience of a touchscreen-based dual-language cognitive intervention tool with cognitively healthy older adults and older adults in early stages of dementia from multilingual backgrounds. We present the iterative design and development process of our dual-language intervention tool, including usability and user experience testing with multilingual older adults. Our observations of usability issues and user experience during testing of the intervention tool with older adults primarily pertain to (a) language, (b) motivation, and (c) user interaction. From the observed needs, we derived preliminary design guidelines for touchscreen-based user interfaces. Our observations and preliminary design guidelines for touchscreen-based user interfaces are significant due to their focus on the needs of multilingual users older adults from Southeast Asian backgrounds.

As design and design thinking become increasingly important competencies for a modern workforce, the burden of assessing these fuzzy skills creates a scalability bottleneck. Toward addressing this need, this paper presents an exploratory study into a scalable computational approach for design thinking assessment. In this study, student responses to a variety of contextualized design questions – gathered both before and after participation in a design thinking training course – are analyzed. Specifically, a variety of text features are engineered, tested, and interpreted within a design thinking framework in order to identify specific markers of design thinking skill acquisition. Key findings of this work include identification of text features that may enable scalable measurement of (1) user-centric language and (2) design thinking concept acquisition. These results contribute toward the creation of computational tools to ease the burden of providing feedback about design thinking skills to a wide audience.

There has been a great deal of research interest in applying industrial symbiosis in the food manufacturing industry as part of the industry-wide shift towards sustainable manufacturing. However, an evaluation of its participation rates first requires an in-depth understanding of the factors that influence the manufacturers’ waste management behaviours and decision-making process. This paper attempts to propose a comprehensive waste management behavioural framework for the food manufacturing industry by building on the works of previous waste management behavioural studies, most notably Young et al.’s work on employee pro-environmental behaviour. The proposed framework was validated through an exploratory survey conducted on a representative group of waste managers from Singapore’s food manufacturing industry. Using exploratory factor analysis, we identified Intention, Perceived Moral Obligation, Company Structure, and Situational Barriers to be the most significant factors influencing the waste manager’s waste management decisions. Based on these results, several policy recommendations were proposed which focus on the education of industrial symbiosis to the leaders of the organisation. The findings from this paper will provide valuable insights into the manufacturer’s waste management decision-making process, as well as a more human-centric framework for the conduct of future industrial symbiosis studies in the food manufacturing industry.

Design thinking has been gaining importance in training and education worldwide, but mostly in the form of short courses and executive education initiatives. Although there is enormous value in short courses and executive education, they often lack the depth required to effectively practice the tools and methods learned and thus to realize design as a strategic investment for both companies and countries. The particular focus of this paper is Singapore. At the Singapore University of Technology and Design, a new Master of Engineering (MEng) program has been set up to address this perceived gap in education. The MEng program in Innovation by Design (MIbD) is a research-based program that takes design thinking and design innovation to the level of other post-graduate programs in other areas worldwide. The organization allows practitioners to participate part-time. Three terms into the program, the balance is extremely positive. The program has been very well received in several presentations to companies. It is expected that these students will either start their own business or easily find jobs in a context that is craving for people with this formal education: a broad view of design and the ability to implement it.

We propose a large, scalable engineering knowledge graph, comprising sets of real-world engineering “facts” as < entity, relationship, entity > triples that are found in the patent database. We apply a set of rules based on the syntactic and lexical properties of claims in a patent document to extract facts. We aggregate these facts within each patent document and integrate the aggregated sets of facts across the patent database to obtain an engineering knowledge graph. Such a knowledge graph is expected to support inference, reasoning, and recalling in various engineering tasks. The knowledge graph has a greater size and coverage in comparison with the previously used knowledge graphs and semantic networks in the engineering literature.

Singapore University of Technology and Design (SUTD) in Singapore was set up in January 2010 to tackle global challenges of the 21st century and beyond. To truly address human-centric issues and problems, a design-focused curriculum using an outside-in approach was adopted in formulating its undergraduate degree programmes in engineering and architecture. This has led to the formation of degree programmes focusing on products, services, systems and built environment which the world needs. To break down the silo mentality and promote multidisciplinary education, SUTD adopts a very forward-looking approach in its academic structure in which majors are offered by four multidisciplinary pillars, supported by two clusters. The absence of conventional colleges, faculties, departments and even divisions encourages seamless and boundaryless collaboration in education and research across all pillars and clusters. With a strong focus on Big-Design (i.e., Big-D) throughout and across courses, semesters and domain areas, undergraduate students are well equipped with skills and attitudes through 20 to 35 team-based Big-D projects during their undergraduate education to provide learning experiences beyond book knowledge, preparing them well with various soft skills such as creativity, critical thinking, teamwork and others before embarking on their career in industry. Coupled with small group-cum-active or peer-to-peer learning in a dedicated cohort classroom environment, the SUTD undergraduate programme further encourages students by having them collaborate with each other across pillars to foster teamwork and a collaborative spirit in formulating and sharing solutions beyond the team. Besides being recognized by the "Global State-of-the-Art in Engineering Education" report commissioned by the MIT School of Engineering [1] as the top of the emerging leader in engineering education recently, the response from industry for the first 5 batches of undergraduates has been very positive.

BACKGROUND The prosthetic and orthotic industry typically provides an artisan “hands-on” approach to the assessment and fitting of orthopedic devices. Despite growing interest in digital technology for prosthetic and orthotic service provision, little is known of the quantum of use and the extent to which the current pandemic has accelerated the adoption. OBJECTIVE This study’s aim is to assess the use of digital technology in prosthetics and orthotics, and whether its use can help overcome challenges posed by the current COVID-19 pandemic. METHODS A web-based survey of working prosthetists, orthotists, and lower limb patients was conducted between June and July 2020 and divided into three sections: lower limb amputees, prosthetist and orthotist (P&O) currently using digital technologies in their practice, and P&O not using any digital technology. Input was sought from industry and academia experts for the development of the survey. Descriptive analyses were performed for both qualitative (open-ended questions) and quantitative data. RESULTS In total, 113 individuals responded to the web-based survey. There were 83 surveys included in the analysis (patients: n=13, 15%; prosthetists and orthotists: n=70, 85%). There were 30 surveys excluded because less than 10% of the questions were answered. Out of 70 P&Os, 31 (44%) used digital technologies. Three dimensional scanning and digital imaging were the leading technologies being used (27/31, 88%), primarily for footwear (18/31, 58%), ankle-foot orthoses, and transtibial and transfemoral sockets (14/31, 45%). Digital technology enables safer care during COVID-19 with 24 out of 31 (77%) respondents stating it improves patient outcomes. Singapore was significantly less certain that the industry's future is digital (P=.04). The use of virtual care was reported by the P&O to be beneficial for consultations, education, patient monitoring, or triaging purposes. However, the technology could not overcome inherent barriers such as the lack of details normally obtained during a physical assessment. CONCLUSIONS Digital technology is transforming health care. The current pandemic highlights its usefulness in providing safer care, but digital technology must be implemented thoughtfully and designed to address issues that are barriers to current adoption. Technology advancements using virtual platforms, digitalization methods, and improved connectivity will continue to change the future of health care delivery. The prosthetic and orthotic industry should keep an open mind and move toward creating the required infrastructure to support this digital transformation, even if the world returns to pre–COVID-19 days.

Conventional forms of design assessment are time consuming for instructors. Crowdsourced assessment of students’ design concepts raises the need of efficient rubrics that facilitate novices to score similar to experts, in reduced time. We investigate rubrics in the context of conceptual design problems, that comprise open-ended questions, requiring students to express their design concepts and supporting rationale using text and sketches. We conducted exploratory post-hoc analysis on assessment data collected by instructors of a Design program at a Singaporean secondary school. Our results suggest that integrated rubrics — that consider both text and sketch component together — are better suited for the assessment of conceptual design problems, than task-specific rubrics, that consider textual and sketch components separately. Evidence from both novice assessors as well as experts suggests that the articulation of design rationale using text is crucial for the assessment of conceptual design problems as it provides assessors input into why design decisions were taken, thus aiding in the evaluation process. Our insights are relevant for developing frameworks that employ crowdsourcing for the assessment of conceptual design problems.

Since the start of COVID-19, there has been several relevant corpora from various sources that were released to support research in this area. While these corpora are valuable in supporting analysis for this specific pandemic, researchers will benefit from additional benchmark corpora that contain other epidemics for better generalizability and to facilitate cross-epidemic pattern recognition and trend analysis tasks. During our research, we discover little disease related corpora in the literature that are sizable and rich enough to support such cross-epidemic analysis tasks. To address this issue, we present EPIC30M, a large-scale epidemic corpus that contains more than 30 million micro-blog posts, i.e., tweets crawled from Twitter, from year 2006 to 2020. EPIC30M contains a subset of 26.2 million tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 4.7 million tweets of six global epidemic outbreaks, including the 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola. Furthermore, we explore and discuss the properties of this corpus with statistics of key terms and hashtags and trends analysis for each subset. Finally, we discuss the potential value and impact that EPIC30M could generate through a discussion of multiple use cases of cross-epidemic research topics that attract growing interest in recent years. These use cases span multiple research areas, such as epidemiological modeling, pattern recognition, natural language understanding and economical modeling. The corpus is publicly available at https://www.github.com/junhua/epic.

Traditionally, design opportunities and directions are conceived based on expertise, intuition, or time-consuming user studies and marketing research at the fuzzy front end of the design process. Herein, we propose the use of the total technology space map (TSM) as a visual ideation aid for rapidly conceiving high-level design opportunities. The map is comprised of various technology domains positioned according to knowledge proximity, which is measured based on a large quantity of patent data. It provides a systematic picture of the total technology space to enable stimulated ideation beyond the designer's knowledge. Designers can browse the map and navigate various technologies to conceive new design opportunities that relate different technologies across the space. We demonstrate the process of using TSM as a rapid ideation aid and then analyze its applications in two experiments to show its effectiveness and limitations. Furthermore, we have developed a cloud-based system for computer-aided ideation, that is, InnoGPS, to integrate interactive map browsing for conceiving high-level design opportunities with domain-specific patent retrieval for stimulating concrete technical concepts, and to potentially embed machine-learning and artificial intelligence in the map-aided ideation process.

While public awareness of climate change has grown over the years, many people still have misconceptions regarding effective individual environmental action. In this paper, we present a serious game called PEAR, developed using elements of geolocation and augmented reality (AR), aimed at increasing players’ awareness of climate change issues and propensity for effective sustainable behaviours. We conducted a study with participants who played the game, gauging their knowledge of and attitudes towards climate change issues before and after playing the game. Our results show that the game significantly improved participants’ knowledge on sustainability and climate-change-related issues, and that it also significantly improved their attitudes towards these topics, thus proving that serious games have the potential to impart knowledge and promote sustainable behaviours. Additionally, our results address the lack of empirical studies on the knowledge base of serious sustainability games by introducing methods of quantitatively analysing the effects of serious sustainability games while additionally providing more knowledge about the effectiveness of the specific design elements of our game.

Function modelling is proposed in the literature from different disciplines, in interdisciplinary approaches, and used in practice with the intention of facilitating system conceptualisation. However, function models across disciplines are largely diverse addressing different function modelling perspectives and using different structures and forms for representing the contained information. This hampers the exchange of information between the models and poses particular challenges to joint modelling and shared comprehension between designers from different disciplines. This article proposes an integrated function modelling framework, which specifically aims at relating between the different function modelling perspectives prominently addressed in different disciplines. It uses interlinked matrices based on the concept of DSM and MDM in order to facilitate cross-disciplinary modelling and analysis of the functionality of a system. The article further presents the application of the framework based on a product example. Finally, an empirical study in industry is presented. Therein, feedback on the potential of the proposed framework to support interdisciplinary design practice as well as on areas of further improvement has been obtained from participants working in industry.

Design fixation is a phenomenon with important significance to many fields of design due to the potential negative impacts it may have in design outcomes, especially during the ideation stage of the design process. The present study aims to provide a framework for understanding, or at least probing, design fixation by presenting a review of existing defixation approaches, as well as metrics that have been employed to understand and account for design fixation. This study also describes the results of two design by analogy (DbA) methods, WordTree and SCAMPER, to overcome design fixation in an experiment that involved 97 knowledge-domain experts. The study outcomes are at least twofold: a common framework of metrics and approaches to overcome design fixation in a wide range of design problems and nonintuitive results for DbA approaches in design fixation and other related creativity metrics. The application of WordTree and SCAMPER shows that both methods yield increased novelty compared to a control, where the SCAMPER results are significantly higher than WordTree. It is also found that WordTree mitigates design fixation whereas SCAMPER appears to be ineffective for this purpose but effective to generate an increased quantity of novel ideas. These results demonstrate that both DbA methods provide de-fixation capabilities and enhance designers’ creativity during idea generation.

Given the unprecedented challenges imposed on the aviation industry by the COVID-19 pandemic, this paper proposes a new perspective on airport user experience as a field of study to unlock its potential as a basis for strategic roadmapping. Through an integrative literature review, this study points out a dominant focus, in practice and research, on customer experience and service quality, as opposed to user experience, to help airports gain a competitive edge in an increasingly commoditized industry. The review highlights several issues with this understanding of experience, as users other than passengers, such as employees, working for the airport and its myriad stakeholders, as well as visitors, are largely omitted from study. Given the complexity of the system, operationally, passengers are generally reduced to smooth flows of a passive mass, which this study argues is both a missed opportunity and a vulnerability exacerbated by the COVID-19 pandemic. Major events apart from COVID-19 are used to show the negative effects this simplification of user experience has had. Based on solutions and models proposed in previous studies, a conceptual model has been developed to illustrate the postulated potential of a deeper and more holistic study of airport user experience to make airport systems generally more agile, flexible and future-proof. As such, the paper advocates to utilize the user experience as a basis for strategic planning to equip airports with the know-how to manage not just daily operations more effectively but also the aftermath of and recovery from major events like the COVID-19 pandemic. Moreover, with the user experience at the center of the strategic roadmap, airports can plan ahead to mitigate the impact of future scenarios. The importance of future research and the use of existing research are discussed., Highlights • Airport user experience has been proposed as a key strategic planning tool for post-COVID Aviation. • Volatility, Uncertainty, Complexity, Ambiguity environments require systemic focus. • Study highlights ‘passenger experience’ dominated the discourse in the past. • A conceptual model has been developed to present the potential of the Airport User Experience. • Flexible and resilient systems planning must focus on airport as the 'experience providers’ for all users.

Plug loads account for up to one-third of the overall energy use in commercial buildings. There is thus a growing research interest in utilising load monitoring systems to track plug load usage by installing smart plugs to capture high-resolution consumption data. The availability of such data has also enabled the development of automatic plug load identification models that enhance the capabilities of existing load monitoring systems. Through our literature review, we highlighted several limitations that impede real-world implementation, such as the limited number of publicly available datasets for commercial buildings, models trained on data with high sampling frequencies while using an extended time window, and data leakage issues during model training. In this study, we proposed a near-real-time plug load identification approach that uses low-frequency power data (1/60 Hz) to identify plug loads in office spaces. The dataset used in this study is processed by first identifying the active periods of the plug loads before applying a novel dynamic time window strategy during feature extraction. These extracted features are subsequently passed through several classification algorithms and evaluated using different accuracy metrics. The proposed approach is also assessed through multiple experiments, including (1) identifying the best online and offline models, (2) comparing between different time window strategies, and (3) evaluating model performances under different sampling frequencies. As a result, the best online model achieved accuracies up to 93% using the Bagging algorithm with a minimum dynamic time window of 5 minutes. Finally, we highlighted two application areas of automatic plug load identification in energy dashboards and personalised control systems as part of future works.

Building occupants are often assumed to follow deterministic schedules in building performance simulation programs. Therefore, to accurately capture the dynamic nature of the occupants' movement patterns, researchers have proposed various indoor localisation technologies to infer occupancy information with varying degrees of accuracy and resolution. Among these technologies, the Bluetooth Low Energy (BLE) technology emerged as a popular alternative due to its availability in smartphone devices, as well as its low cost and power demand. In this study, we proposed a scalable and less intrusive occupancy detection method that leverages existing BLE technologies found in smartphone devices to perform zone-level occupancy localisation, without the need for a mobile application. The proposed method uses a network of BLE beacons for data collection before passing the pre-processed data into a machine learning model to infer the occupants' zone-level location. A supervised ensemble model and a semi-supervised clustering model were proposed and evaluated to identify the best performing model. The feasibility of the proposed method is demonstrated during a five-week case study involving two office spaces in an academic building in Singapore. While the supervised ensemble model produced the best performance in terms of accuracy and macro-average f1-score, the semi-supervised model was able to produce a reasonable performance while using a fraction of the training data (

In this study, we proposed a non-intrusive occupancy monitoring approach which leverages on existing BLE technologies found in smartphone devices to track the occupants’ movement patterns using BLE beacons. Unlike existing methods, the proposed approach does not require the installation of a mobile application and only requires the occupants to provide the MAC address of their Bluetooth-enabled smartphone devices. The feasibility of the proposed approach was demonstrated by conducting a two-week data collection effort in a university office environment where the occupancy patterns identified are used to develop various occupancy profiles for different types of occupants.

Identifying relevant stimuli that help generate solutions of desired novelty and quality is challenging in analogical design. To quell this challenge, the multifaceted effects of using stimuli which are located at various analogical distances to the design problem on the novelty and quality of concepts generated using the stimuli are studied in this research. Data from a design project involving 105 student designers, individually generating 226 concepts of spherical rolling robots, are collected. From these data, 138 concepts generated with patents as stimuli and the patents used are analyzed. Analogical distance of a patent is measured in terms of knowledge similarity between technology classes constituting the patent and design problem domain of spherical rolling robots. The key observations are (a) technology classes in closer than farther distances from the design problem are used more frequently to generate concepts, (b) as analogical distance increases the novelty of concepts increases, and (c) as analogical distance decreases the quality of concepts increases.

Gesture- and speech-based 3D modeling offers designers a powerful and intuitive way to create 3D Computer Aided Design (CAD) models. Instead of arbitrary gestures and speech commands defined by researchers, which may not be intuitive for users, such a natural user interface should be based on gesture and command set elicited from the users. We describe our ongoing research on a speech-and-gesture-based CAD modeling interface, GesCAD, implemented by combining Microsoft Kinect and Rhino, a leading CAD software. GesCAD is based on gestures and speech commands elicited from a specially designed user experiment. We conducted a preliminary user study with 6 participants to evaluate the user experience of our prototype, such as ease of use, physical comfort and satisfaction with the models created. Results show that participants found the overall experience of using GesCAD fun and the speech and gesture commands easy to remember.

Design is a ubiquitous human activity. Design is valued by individuals, teams, organizations, and cultures. There are patterns and recurrent phenomena across the diverse set of approaches to design and also variances. Designers can benefit from leveraging conceptual tools like process models, methods, and design principles to amplify design phenomena. There are many variant process models, methods, and principles for design. Likewise, usage of these conceptual tools differentiates in industrial contexts. We present an integrated process model, with exemplar methods and design principles that is synthesized from a review of several case studies in client based industrial design projects for product, service, and system development, professional education courses, and literature review. Concepts from several branches of design practice: (1) design thinking, (2) business design, (3) systems engineering, and (4) design engineering are integrated. A design process model, method set, and set of abstracted design principles are porposed.

Past work has demonstrated that simulating an extraordinary user scenario could have an impact on increasing designer empathy and creativity. It is likely, however, that an individual’s background could make a difference on how such simulated scenarios influence designers and how well the design method works. In this paper, we study the impact of demography and personal connection of the participants to any given simulated scenario versus their response to different empathic simulation workshops. With a variety of design tools and techniques available, understanding such influencing factors could help designers decide on the appropriate design tool for effective ideation. In this study, we investigated the effect of 81 (49 female and 32 male) users from three different workshops that simulated three different extraordinary user scenarios. Results of the study show that personal connection to a population being simulated significantly affects the impact a simulated scenario has on evoking creativity and empathy. And, for the given set of participants, gender did not show significant impact on the participants’ response.

Analogy-making has been deemed one of the core cognitive mechanisms which play a role in human creative thinking activities such as design and art. Designers can make use of analogies in various stages of design including ideation, planning and evaluation. However, human analogy-making is limited by experience and reliance of human memory on superficial attributes rather than relational or causal structure during analogy retrieval. In this regard, different design-by-analogy tools have been developed to assist designers in analogical reasoning. Analogical reasoning tools can be viewed as either based on hand-coded structured knowledge or natural-language-based design-by-analogy tools. The former are naturally limited in extent and scope to that which was hand coded [1]. Alternatively, natural language analogical reasoning can leverage the abundantly available textual resources. Current text-based analogy research for design have relied on analogies between individual word meanings. This leaves open consideration of the relational structure of the language where the relational similarity of texts can indicate a significant analogy. In this article, we develop four computational models of analogy that capture relational structure of the text. This includes spatial representation of semantics, multi-level deep neural reasoning, graph matching based model and transformation-based model. The models are then combined together into an ensemble model to achieve acceptable level of analogical accuracy for the end-user. The underlying design-related knowledge upon which analogies were drawn includes engineering ontologies, function hierarchy and raw patent texts. Instantiating this analogical reasoning model in design concept analogy retrieval system, we show this approach can help retrieve meaningful analogies from the World Intellectual Property Organization (WIPO) patent repository. We demonstrate this for a particular design problem.

This paper presents a stage based framework for analyzing transdisciplinary design processes in engineering product design and manufacturing. The framework provides a stage-wise, product lifecycle centric frame of reference for comparing design processes in industries from different industrial sectors involving multi-disciplinary stakeholders. The framework is based on extensive literature analysis in the domain of design theory and methodology, as well as from models in product life cycle management. The paper also reports insights on application of the framework for design processes analysis of 23 industries based on the mapping of their individual design processes to the developed framework.

There has been a widely documented phenomenon identifying an energy performance gap between the building’s design and operational phases. This result has been attributed to the stochastic behaviours exhibited by the occupants, who are assumed to follow deterministic and routine schedules. With the recent advancements in smart monitoring technologies, the increasing affordability of wireless sensors has allowed researchers to collect detailed information on the occupants’ dynamic behaviours. However, past applications of such technologies have been highly intrusive and limit the validity of the data collected due to the Hawthorne effect. Therefore, this paper proposes a non-intrusive data collection methodology using a comprehensive range of wireless smart meters, Bluetooth beacons, and questionnaires to capture the occupants’ movement and appliance interaction patterns. The feasibility of the approach is demonstrated during a two-week data collection effort in a university office. By combining the occupants’ presence with appliance energy consumption data, the authors were able to identify the occupants’ appliance interaction patterns. An extension of this work includes the use of the data collected to identify different occupancy and appliance interaction profiles, which contributes to the development of an appliance interaction model that addresses the energy performance gap caused by occupants’ appliance interaction.

A great number of complex electronic devices are now part of our everyday lives. While many of us learn to handle these products by trial and error; others, especially older users with little experience in using electronic devices, need support. In order to allow the user maximum flexibility in terms of learning time and location, a training programme is presented which is implemented as part of the software embedded in the product itself. Particular focus is placed on the effect of adaptive training on learning. In this study, the training versions differed in their ability to adjust their complexity to the user's experience adaptive user interface complexity and their capability to support the learner by prompting them during the learning process adaptive training advice. The results show that the adjustment of complexity had a positive effect on users’ experience: elderly users who trained with an adaptive interface were more successful in learning to use a mobile phone. Adaptive training advice, however, was found to have no significant effects on learners’ success and reduced their self-efficacy. This work offers guidelines on how to design integrated training applications for electronic devices that successfully help elderly users with little prior experience.

Authors across disciplines propose functional modeling as part of systematic design approaches, in order to support and guide designers during conceptual design. The presented research aims at contributing to a better understanding of the diverse functional modeling approaches proposed across disciplines. The article presents a literature review of 41 modeling approaches from a variety of disciplines. The analysis focuses on what is addressed by functional modeling at which point in the proposed conceptual design process (i.e., in which sequence). The gained insights lead to the identification of specific needs and opportunities, which could support the development of an integrated functional modeling approach. The findings suggest that there is no such shared sequence for functional modeling across disciplines. However, a shared functional modeling perspective has been identified across all reviewed disciplines, which could serve as a common basis for the development of an integrated functional modeling approach.

This paper is integrated in our research project on fostering sustainable user behaviour by designing meaning. It explores different types of meaning in products and services, and their creation. For that, the results of a literature study are integrated into an initial taxonomy of meaning and an initial framework for creating meaning. From the taxonomy we derive four roles that products and services can play in meaning making. Additionally we discuss how five possible interventions in users meaning making for behavioural change can be pursued by design on the basis of the framework. We illustrate the role of designers in creating meaning with examples, touch on its potential for behavioural change of users and outline further research needs.

The paper presents an evaluation study for a DSM-based function modeling framework with practitioners in industry. Questionnaires and open discussion are used in a workshop format to obtain feedback on the framework’s usefulness and practical applicability. It allows flexibly adaptive modeling multiple, inter-related views onto the functionality of (interdisciplinary) systems. The findings suggests a large potential for the framework to support cross-disciplinary design and for using the inherent matrices to do function analysis. The paper highlights the views of the practitioners on the strengths and potentials for further improvement.

There is still much to learn about the mechanisms and interactions by which psychological and cognitive factors influence creative performance in design, and more explicitly in design’s ideation stage. The present study aims to explore such influences for Design by Analogy (DbA) ideation methods. 69 participants from 52 companies in Mexico and Singapore executed two ideation sessions (phase I and phase II) to generate solution ideas for a service design problem (same design problem was used for both phases). Between the two phases participants were assigned and trained with one of three selected ideation conditions: Control, WordTree and SCAMPER. Finally, they were asked to self-evaluate 11 factors that may influence their creative performance. The explored factors comprised individual, performance self-perception and contextual factors. Individual factors are considered with respect to the ideation activity and the design problem. In the case of the ideation activity evaluated factors are: entertainment (boring-fun), motivation, inspiration and easiness (difficult-easy). For the design problem, interest, easiness and inspiration are considered. Performance self-perception factors are: commitment (minimal effort-hard work), concentration (unfocused-focused) and level of success (unsuccessful-successful). Lastly, the Contextual factor considered is the self-perceived relation of time with respect to ideas. Two main analyses are presented and discussed in this article: the effect of learning and applying DbA ideation methods on participants’ factor self-evaluation, and; the relationship between the 11 self-evaluated factors and creative performance using the metrics of Fluency, Novelty and Fixation.Copyright © 2015 by ASME

This chapter summarizes the results of a discussion between participants from academia and industry about their experiences in transferring design research into practice. The results are divided into guidelines for successful transfer and measures for supporting on-going interation between academia and practice.

Although the development of methods and tools was for many decades the main focus of design research, transfer of research results to practice has been fragmented and limited and, hence, had a low impact. Various studies into the problems involved in transfer have been undertaken the uptake of the recommended improvements has been limited. One of the reasons, in our opinion, is the lack of a coherent, and agreed upon set of heuristics. This is where we intend to contribute. In this chapter we focus on the transfer of design research results into practice as experienced by those who have been involved in their development. Our aim is to propose a preliminary set of best practice heuristics for researchers to enhance the chances of successful transfer of research results into practice as a starting point for discussion and further research.