Methodological diversity and theoretical integration: Research in design fixation as an example of fixation in research design?

ABSTRACT: Over the last three decades, design fixation research has emerged as a distinct and productive area of design creativity research, and of design research more generally. In studying an applied aspect of human behaviour, fixation research is unusually restricted in its methodological choices, primarily adopting experimental and statistical approaches. The few relevant qualitative studies remain isolated from the core experimental literature. This limited diversity and limited integration restricts our ability to interpret the relevance of prior work, plan new studies and impact practice. By viewing research as a creative design activity, I argue that the fixation research community is well placed to study and change its own behaviour, and also to inform the behaviour of other research disciplines.This work was partly supported by the UK’s Engineering and Physical Sciences Research Council (EP/K008196/1)

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Data-Driven Research On Engineering Design Thinking And Behaviors In Computer-Aided Systems Design: Analysis, Modeling, And Prediction

Research on design thinking and design decision-making is vital for discovering and utilizing beneficial design patterns, strategies, and heuristics of human designers in solving engineering design problems. It is also essential for the development of new algorithms embedded with human intelligence and can facilitate human-computer interactions. However, modeling design thinking is challenging because it takes place in the designer’s mind, which is intricate, implicit, and tacit. For an in-depth understanding of design thinking, fine-grained design behavioral data are important because they are the critical link in studying the relationship between design thinking, design decisions, design actions, and design performance. Therefore, the research in my dissertation aims to develop a new research platform and new research approaches to enable fine-grained data-driven methodology that helps foundation ally understand the designers’ thinking and decision-making strategies in engineering design. To achieve this goal, my research has focused on modeling, analysis, and prediction of design thinking and designers’ sequential decision-making behaviors. In the modeling work, different design behaviors, including design action preferences, one step sequential decision behavior, contextual behavior, long short-term memory behavior, and reflective thinking behavior, are characterized and computationally modeled using statis tical and machine learning techniques. For example, to model designers’ sequential decision making, a novel approach is developed by integrating the Function-Behavior-Structure (FBS) design process model into deep learning methods, e.g., the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. In the work on analysis, this dissertation focuses primarily on different clustering analysis techniques. Based on the behaviors modeled, designers showing similar behavioral patterns can be clustered, from which the common design patterns can be identified. Another analysis performed in this dissertation is on the comparative study of different sequential learning techniques, e.g., deep learning models versus Markov chain models, in modeling sequential decision-making behaviors of human designers. This study compares the prediction accuracy of different models and helps us obtain a better understanding of the performance of deep-learning models in modeling sequential design decisions. Finally, in the work related to prediction, this dissertation aims to predict sequential design decisions and actions. We first test the model that integrates the FBS model with various deep-learning models for the prediction and evaluate the performance of the model. Then, to improve the accuracy of the prediction, we develop two approaches that directly and indirectly combine designer-related attributes (static data) and designers’ action sequences (dynamic data) within the deep learning-based framework. The results show that with ap propriate configurations, the deep-learning model with both static data and dynamic data outperforms the models that only rely on the design action sequence. Finally, I developed an artificial design agent using reinforcement learning with a data-driven reward mechanism based on the Markov chain model to mimic human design behavior. The model also helps validate the hypothesis that the design knowledge learned by the agent from one design problem is transferable to new design problems. To support fine-grained design behavioral data collection and validate the proposed approaches, we develop a computer-aided design (CAD)-based research platform in the application context of renewable engineering systems design. Data are collected through three design case studies, i.e., a solarized home design problem, a solarized parking lot design problem, and a design challenge on solarizing the University of Arkansas (UARK) campus. The contribution of this dissertation can be summarized in the following aspects. First, a novel research platform is developed that can collect fine-grained design behavior data in support of design thinking research. Second, new research approaches are developed to characterize design behaviors from multiple dimensions in a latent space of design thinking. We refer to such a latent representation of design thinking as design embedding. Furthermore, using deep learning techniques, several different predictive models are developed that can successfully predict human sequential design decisions with prediction accuracy higher than traditional sequential learning models. Third, by analyzing designers’ one-step sequential design behaviors, common and beneficial design patterns are identified. These patterns are found to exist in many high-performing designers in the three respective design problems studied. Fourth, new knowledge has been obtained on the ability of deep learning-based models versus traditional sequential learning models to predict sequential design decisions of human designers. Finally, a novel research approach is developed that helps test the hypothesis of transferability of design knowledge. In general, this dissertation creates a new avenue for investigating designers’ thinking and decision-making behaviors in systems design context based on the data collected from a CAD environment and tested the capability of various deep-learning algorithms in predicting human sequential design decisions

Neurological and Mental Disorders

Mental disorders can result from disruption of neuronal circuitry, damage to the neuronal and non-neuronal cells, altered circuitry in the different regions of the brain and any changes in the permeability of the blood brain barrier. Early identification of these impairments through investigative means could help to improve the outcome for many brain and behaviour disease states.The chapters in this book describe how these abnormalities can lead to neurological and mental diseases such as ADHD (Attention Deficit Hyperactivity Disorder), anxiety disorders, Alzheimer’s disease and personality and eating disorders. Psycho-social traumas, especially during childhood, increase the incidence of amnesia and transient global amnesia, leading to the temporary inability to create new memories.Early detection of these disorders could benefit many complex diseases such as schizophrenia and depression

Empirical investigation on the barriers of adoption of cryptocurrency-based transaction from an Islamic perspective

Purpose: This paper presents a user study of “perception of the cryptocurrency-based transaction from the Islamic views”. Bitcoin is considered the special type of cryptocurrency used in this study. Users view bitcoin is as an app that provides a personal currency in a digital wallet through which transactions can occur in order to either send, receive, buy, or sell the currency (bitcoins). Information System is an enabler of this mode of transaction, unfortunately, some users raised concern about the nature of transactions with Bitcoin. Specifically, some argued that Bitcoin can be easily used for illegal purposes and that the global public already uses Bitcoin mostly for illegal and Shari’ah non-compliant purposes under Islamic views. Design/methodology/approach: This study adopted “Technological Acceptance Model” and utilized quantitative research methodology, in order to formulate and test some hypotheses that will lead to an establishment of a model. A sample of 306 participants was used in the study. Findings: The result of the hypothesis testing indicate that “Behavioral Intention to Use Cryptocurrency from the Islamic perspective” is influenced directly by Shari’ah Compliance, Perceived Ease of Use, Emotionality, Perceived Usefulness, and Financial Concern. As evident from the analysis, Emotionality is influenced directly by Financial concern and Shari’ah Compliance. Whereas, Behavioral Intention is influenced indirectly by Financial Concerns. Research limitations/implications: The sample is general and does not specify a specific group of study. Practical implications: This study has contributed to understanding the Islamic issues behind the implementation of Cryptocurrency Originality/value: The study formulates and tests a theory for cryptocurrency-based transaction from an Islamic view

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The conception of New Venture Ideas by novice entrepreneurs: A question of nature or nurture?

This research aims to further understanding around the cognitive mechanisms lying behind the generation of entrepreneurial New Venture Ideas (NVIs). It assesses the extent to which this competency is innate or one which is capable of being proactively developed. This has particular salience in the context of novice entrepreneurs, a group lacking the knowledge corridors and cognitive frameworks of their serial or portfolio counterparts. Innovative in nature, NVIs represent the first candidate concepts for new means-end relationships. Existing as cognitive products at the very start of the entrepreneurial journey, significant academic attention has focused on the cognitive micro-foundations that influence their conception. Nonetheless, notable gaps in this body of work remain, not least in how different cognitive antecedents impact upon NVI quality. This thesis looks at these issues through three independent but inter-related studies. The first undertakes a systematic literature review of the existing empirical research to elucidate the extent, and associated transmission methods, through which entrepreneurship education and training (EET) supports opportunity identification. The second takes a quantitative approach to observe how an individual’s innate cognitive capabilities, notably those aspects of intelligence related to executive functioning, explain significant inter-person performance differences when it comes to entrepreneurial ideation. The third adopts an experimental methodology, to assess the extent to which the use of cognitive heuristics, in this case analogical reasoning, impacts on performance outcomes in the conception of NVIs, and the extent to which it can be supported. Collectively this study finds that EET interventions, innate cognitive capabilities, and cognitive heuristics all contribute to NVI quality. It highlights the potency of nurturing interventions but simultaneously illustrates their limitations. With different cognitive antecedents shown to exude varying degrees of malleability, this research has relevance to both the structure, and expectations, of EET programmes dedicated to the ‘fuzzy front’ end of entrepreneurship