5,732 research outputs found

    The Non-Standardization of Attention Deficit Hyperactive Disorder: A Call to Action

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    Attention Deficit Hyperactive Disorder (ADHD) is one of the most diagnosed disorders in adults and children, yet there is no standardized method to assess for ADHD. The similarity of symptoms shared across other disorders (comorbidity) makes the assessment of ADHD a very delicate process. This is not aided by the fact that the assessment of ADHD is not standardized. This allows individuals able to assess for ADHD to give a test or a combination of tests that they find fitting. This in turn brings into question the quality of testing and disagreement in diagnosing across fields. Lastly, ADHD-focused measures typically fail to address the overlap in symptoms with other disorders, which can help assist clinicians with differential diagnoses. The question then becomes, how does one attempt to standardize ADHD testing while providing testing that shows adequate clinical validity in both the diagnosis of ADHD and differential diagnosing? This paper aims to produce insight into the complications of ADHD diagnosis and suggest a solution to current testing, in the form of an assessment battery

    Mobile heritage practices. Implications for scholarly research, user experience design, and evaluation methods using mobile apps.

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    Mobile heritage apps have become one of the most popular means for audience engagement and curation of museum collections and heritage contexts. This raises practical and ethical questions for both researchers and practitioners, such as: what kind of audience engagement can be built using mobile apps? what are the current approaches? how can audience engagement with these experience be evaluated? how can those experiences be made more resilient, and in turn sustainable? In this thesis I explore experience design scholarships together with personal professional insights to analyse digital heritage practices with a view to accelerating thinking about and critique of mobile apps in particular. As a result, the chapters that follow here look at the evolution of digital heritage practices, examining the cultural, societal, and technological contexts in which mobile heritage apps are developed by the creative media industry, the academic institutions, and how these forces are shaping the user experience design methods. Drawing from studies in digital (critical) heritage, Human-Computer Interaction (HCI), and design thinking, this thesis provides a critical analysis of the development and use of mobile practices for the heritage. Furthermore, through an empirical and embedded approach to research, the thesis also presents auto-ethnographic case studies in order to show evidence that mobile experiences conceptualised by more organic design approaches, can result in more resilient and sustainable heritage practices. By doing so, this thesis encourages a renewed understanding of the pivotal role of these practices in the broader sociocultural, political and environmental changes.AHRC REAC

    Graduate Catalog of Studies, 2023-2024

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    Digital technologies for behavioral change in sustainability domains: a systematic mapping review

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    Sustainability research has emerged as an interdisciplinary area of knowledge about how to achieve sustainable development, while political actions toward the goal are still in their infancy. A sustainable world is mirrored by a healthy environment in which humans can live without jeopardizing the survival of future generations. The main aim of this contribution was to carry out a systematic mapping (SM) of the applications of digital technologies in promoting environmental sustainability. From a rigorous search of different databases, a set of more than 1000 studies was initially retrieved and then, following screening criteria based on the ROSES (RepOrting standards for Systematic Evidence Syntheses) procedure, a total of N = 37 studies that met the eligibility criteria were selected. The studies were coded according to different descriptive variables, such as digital technology used for the intervention, type of sustainable behavior promoted, research design, and population for whom the intervention was applied. Results showed the emergence of three main clusters of Digital Technologies (i.e., virtual/immersive/augmented reality, gamification, and power-metering systems) and two main Sustainable Behaviors (SBs) (i.e., energy and water-saving, and pollution reduction). The need for a clearer knowledge of which digital interventions work and the reasons why they work (or do not work) does not emerge from the outcomes of this set of studies. Future studies on digital interventions should better detail intervention design characteristics, alongside the reasons underlying design choices, both behaviourally and technologically. This should increase the likelihood of the successful adoption of digital interventions promoting behavioral changes in a more sustainable direction

    THE IMPACT OF HUMAN-CENTRIC LIGHTING PARAMETERS ON OLDER ADULT’S PERCEPTION, AND COGNITIVE PERFORMANCE

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    Population aging is a prominent demographic challenge. Older adults face increased risks of sleep dysfunctions, depression, and cognitive impairments due to physical, biological, and psychological factors associated with aging. These behavioral issues elevate safety risks at home, which necessitates the transition to assisted living facilities. Extensive research highlights the influence of healthcare environmental design, particularly related to architectural lighting impacts on residents' well-being and quality of life. To optimize older adults' health and well-being, it is essential to consider both the visual and non-visual effects of architectural lighting. Visual impacts include parameters related to task performance and visual acuity, while non-visual impacts may include outcomes such as circadian rhythm regulation, sleep quality, mood enhancement, and cognitive performance, thereby emphasizing the importance of implementing a holistic conceptual approach to human-centric lighting in indoor environments.While existing gerontology studies have primarily focused on light-level attributes, such as radiant flux, illuminance, and equivalent melanopic lux, there has been limited exploration of spectral and spatial pattern parameters in indoor lighting. The primary objective of this research is to investigate the impact of both quantitative and qualitative aspects of lighting design, including spatial layout characteristics such as uniformity, direction, centrality, and spectral attributes like correlated color temperature (CCT), on the visual perception, preference, mood, cognitive performance, and overall well-being of older adults in assisted living facilities. The study employed a multi-method approach across three main research phases. In phase I, a Q-sort survey involving 60 participants assessed the impact of diverse spatial light patterns on visual perception and preference. In phase II, a within-subject design evaluated the cognitive performance of 32 older adults in similar lighting scenarios within real and virtual environments. Lastly, in phase III, the study examined the relationship between spatial and spectral light patterns and cognitive performance through virtual reality testing with 32 participants. Results revealed significant effects of different spatial light patterns on older adults' environmental impressions, including visual preference, stress levels, and cognitive performance. Uniform and indirect lighting were preferred, with no substantial differences between peripheral and central spatial arrangements of light layers. Non-uniform lighting induced a relaxed impression, while uniform lighting heightened perceived stress. Furthermore, the study demonstrated the suitability of virtual reality environments (VR) for assessing cognitive performance and subjective perception. The findings underscore the substantial influence of spatial and spectral light patterns on the cognitive performance of older adults in assisted living facilities. This research contributes to the understanding of the visual and non-visual effects of human-centric lighting on the well-being of older adults. By considering spatial and spectral light attributes, designers can enhance cognitive function, reduce impairments, and cultivate healthier and more efficient living environments

    Virtual reality body swapping to improve self-assessment in job interview training

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    Swapping visual perspective in Virtual Reality pro vides a unique means for embodying different virtual bodies and for self-distancing. Moreover, this technology is a powerful tool for experiential learning and for simulating realistic scenarios, with broad potential in the training of soft skills. However, there is scarce knowledge on how perspective swapping in Virtual Reality might benefit the training of soft skills such as those required in a job interview. The present study investigates the impact of virtual body swapping on the self-assessment of verbal and non- verbal communication skills, emotional states, and embodiment in a simulated job interview context. Three main conditions were compared: a baseline condition in which the participants practiced a job interview from the first-person perspective of a virtual interviewee (No Swap condition); an external point of view condition where, first, the participants answered questions from the interviewee perspective, but then swap visual perspective to re-experience their responses from a non-embodied point of view (Out of Body condition); a condition in which, after answering questions from the interviewee perspective, the participants re-experienced their responses from the embodied perspective of the virtual recruiter (Recruiter condition). The experimental results indicated that the effectiveness of the Out of Body and Recruiter Conditions was superior to the No Swap Condition to self-assess the communication styles used during a job interview. Moreover, all the conditions led to a high level of embodiment towards the interviewee avatar when seen from the first-person perspective; in the case of the Recruiter Condition, the participants also felt embodied in the recruiter avatar. No differences in emotional states were found among conditions, with all sharing a positive valence

    How social media brand community development impacts consumer engagement and value formation; perspectives from the cosmetics industry

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    Social media and social media brand communities (SMBCs) are powerful tools for long-term consumer-brand relationship building. As a result, SMBCs are becoming significant marketing channels. Despite the wide use and adoption of SMBCs, further research is called for, as both practitioners and academics lack an understanding of the processes taking place within SMBCs. This study aims to contribute to knowledge of: (1) consumer engagement, (2) value formation in SMBCs, and (3) establishing the relationship between consumer engagement and value formation within the SMBC environment. This thesis adopts netnography, a method commonly employed to explore online communities in the social media environment. Three cosmetics brands were selected for this study. The selection was driven by geographical location, posting frequency and user activity. Data were retrospectively collected from Facebook SMBCs between 1st December 2019 and 31st January 2020. The data analysis employed thematic analysis techniques and was further guided by netnographic procedural steps, encompassing 25 distinct data operations. In total, 87 conversation threads were examined, which included 6,401 consumer comments. The findings present a typology of brand posts consisting of five overarching themes: presentation of offerings, belongingness building, engagement building, value-led, and educational. The research also identified a consumer comment typology consisting of four overarching themes brand-centred communication, cognitive-centred communication, conversation-centred communication, and personal experience-centred communication. Additionally, the thesis explores value formation processes within SMBCs, and the value types formed through consumer-to-consumer value formation interaction, brand-to-consumer value formation interaction, consumer-to-brand value formation interaction, as well as individual value formation processes, i.e., customer independent value formation and brand independent value facilitation. Through the findings, thesis broadens knowledge of the implication of SMBC development on consumer engagement. Additionally, this study extends the scope of value formation beyond service marketing, providing valuable insights into how value is created and perceived in the context of SMBCs. This research is also of significance for practice as it offers guidance and insight into how different brand posts can facilitate SMBC development, and, in turn, consumer engagement and value formation. The research provides a link between SMBC development and consumer engagement, highlighting the importance of SMBCs in the successful facilitation of consumer engagement. In particular, it provides evidence that the development of an SMBC has a significant impact on consumer engagement. The typology of brand posts that this study generates highlights the link between the types of posts published by the brand and SMBC development. In addition, the typology of consumer posts also suggests that there is a link between the types of comments published by consumers and the degree of SMBC development. As a result, the findings indicate significant growth in the variety of topics discussed within more developed SMBCs alongside a shift within the topics discussed. The study also investigates value formation within SMBCs, thereby enhancing the understanding of how SMBCs can facilitate value formation. By doing so, this research successfully extends the value formation lens predominantly applied in service marketing. In particular, the findings highlight the role of different actors in enabling the formation of different value types. Furthermore, the research emphasises the value of SMBCs as knowledge repositories as important virtual spaces for both brands and consumers. The findings facilitate understanding of the importance of SMBCs in value formation processes, contributing to advancing knowledge of the role of SMBCs in the development of consumer engagement and value formation. The thesis presents a contextualised conceptual framework of value formation within SMBCs, that captures different interactions taking place in the SMBC environment but also draws attention to the different value types generated through interaction between different actors. Finally, the thesis offers a conceptual framework of SMBCs, consumer engagement and value formation, which captures the correlation between the three researched concepts

    Graduate Catalog of Studies, 2023-2024

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    Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

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    The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

    Deep Learning Techniques for Electroencephalography Analysis

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    In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective
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