3,686 research outputs found
Eating Behavior In-The-Wild and Its Relationship to Mental Well-Being
The motivation for eating is beyond survival. Eating serves as means for socializing, exploring cultures, etc. Computing researchers have developed various eating detection technologies that can leverage passive sensors available on smart devices to automatically infer when and, to some extent, what an individual is eating. However, despite their significance in eating literature, crucial contextual information such as meal company, type of food, location of meals, the motivation of eating episodes, the timing of meals, etc., are difficult to detect through passive means. More importantly, the applications of currently developed automated eating detection systems are limited.
My dissertation addresses several of these challenges by combining the strengths of passive sensing technologies and EMAs (Ecological Momentary Assessment). EMAs are a widely adopted tool used across a variety of disciplines that can gather in-situ information about individual experiences. In my dissertation, I demonstrate the relationship between various eating contexts and the mental well-being of college students and information workers through naturalistic studies.
The contributions of my dissertation are four-fold. First, I develop a real-time meal detection system that can detect meal-level episodes and trigger EMAs to gather contextual data about oneās eating episode. Second, I deploy this system in a college student population to understand their eating behavior during day-to-day life and investigate the relationship of these eating behaviors with various mental well-being outcomes. Third, based on the limitations of passive sensing systems to detect short and sporadic chewing episodes present in snacking, I develop a snacking detection system and operationalize the definition of snacking in this thesis. Finally, I investigate the causal relationship between stress levels experienced by remote information workers during their workdays and its effect on lunchtime. This dissertation situates the findings in an interdisciplinary context, including ubiquitous computing, psychology, and nutrition.Ph.D
Innovation in Energy Security and Long-Term Energy Efficiency ā ”
The sustainable development of our planet depends on the use of energy. The increasing world population inevitably causes an increase in the demand for energy, which, on the one hand, threatens us with the potential to encounter a shortage of energy supply, and, on the other hand, causes the deterioration of the environment. Therefore, our task is to reduce this demand through different innovative solutions (i.e., both technological and social). Social marketing and economic policies can also play their role by affecting the behavior of households and companies and by causing behavioral change oriented to energy stewardship, with an overall switch to renewable energy resources. This reprint provides a platform for the exchange of a wide range of ideas, which, ultimately, would facilitate driving societies toward long-term energy efficiency
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
Reshaping Higher Education for a Post-COVID-19 World: Lessons Learned and Moving Forward
No abstract available
Sensing Collectives: Aesthetic and Political Practices Intertwined
Are aesthetics and politics really two different things? The book takes a new look at how they intertwine, by turning from theory to practice. Case studies trace how sensory experiences are created and how collective interests are shaped. They investigate how aesthetics and politics are entangled, both in building and disrupting collective orders, in governance and innovation. This ranges from populist rallies and artistic activism over alternative lifestyles and consumer culture to corporate PR and governmental policies. Authors are academics and artists. The result is a new mapping of the intermingling and co-constitution of aesthetics and politics in engagements with collective orders
āNot the story you want, Iām sureā: Mental health recovery and the narratives of people from marginalised communities
Background: The dominant narrative in mental health policy and practice has shifted in the 21st century from one of chronic ill health or incurability to an orientation towards recovery. A recovery-based approach is now the most frequently used in services in the Global North, and its relevance has also been explored in Global South settings. Despite the ubiquity of the recovery approach, people experiencing poverty, homelessness, intersecting oppressions (based for example on race, ethnicity, gender, sexuality or ability), and other forms of social marginalisation remain under-represented within recovery-oriented research. More inclusive research has been called for to ensure that knowledge of recovery processes is not based solely on the experiences of the relatively well-resourced.
Personal narratives of recovery from mental distress have played a central role in the establishment of the recovery approach within mental health policy and practice. Originating in survivor/service-user movements, the use of ārecovery narrativesā has now become widespread for diverse purposes, including staff training to improve service delivery and increase empathy, public health campaigns to challenge stigma, online interventions to increase access to self-care resources, and as a distinctive feature of peer support. Research suggests that recovery-focused narratives can have benefits and also risks for narrators and recipients. At the same time, the elicitation of such narratives by healthcare researchers, educators and practitioners has been problematised by survivor-researchers and other critical theorists, as a co-option of lived experience for neoliberal purposes.
Following a systematic review of empirical research studies undertaken on characteristics of recovery narratives (presented in Chapter 4), a need for empirical research on the narratives of people from socially marginalised groups was identified. What kinds of stories might we/they be telling, and what are their experiences of telling their stories? What do their experiences tell us about the use of stories within a recovery approach?
Aim: Drawing on a body of critical scholarship, my aim is to conduct an empirical inquiry into (i) characteristics of recovery stories told by people from socially marginalised groups, and (ii) their experiences of telling their stories in formal and everyday settings.
Method: I undertook a critical narrative inquiry based on the stories of 77 people from marginalised groups, collected in the context of a wider study. This comprised narratives from people with lived experience of mental distress who additionally met one or more of the following criteria: (i) had experiences of psychosis; (ii) were from Black, Asian and other minoritised ethnic communities; (iii) are under-served by services (operationalised as lesbian, gay, bi, trans, queer + communities (LGBTQ+) or people identified as having multiple and complex needs); or (iv) had peer support roles. Two-part interviews were conducted (18 conducted by me). Part A consisted of an open-ended question designed to elicit a narrative, and part B was a semi-structured interview inviting participants to reflect on their experiences of telling their recovery stories in different contexts. Following Riessmanās analytical approach, I undertook three forms of analysis: a structural narrative analysis of Part A across the dataset (informed by a preliminary conceptual framework developed in Chapter 4); a thematic analysis of Part B where participants additionally reflected on telling their stories; and an in-depth performative narrative analysis of two accounts (parts A and B) from people with multiple and complex needs.
Findings: In a structural analysis of Part A, the recovery narratives told by people from marginalised groups were found to be diverse and multidimensional. Most (97%) could be characterised by the nine dimensions described in the preliminary conceptual framework (Genre; Positioning; Emotional Tone; Relationship with Recovery; Trajectory; Turning Points; Narrative Sequence; Protagonists; and Use of Metaphors). Each dimension of the framework contained a number of different types. These were expanded as a result of the structural analysis to contain more types: for example, a ācyclicalā type of trajectory was added), and a more comprehensive typology of recovery narratives was produced. Two narratives were found to be āoutliersā, in that their structure, form and content could not adequately be described by the majority of existing dimensions and types. These served as exemplars of the frameworkās limitations.
In a thematic analysis of Part B, my overarching finding was that power differentials between narrators and recipients could be seen as the key factor affecting participantsā experiences of telling their recovery stories in formal and everyday settings. Four themes describing the possibilities and problems raised by telling their stories were identified: (i) āChallenging the status quoā; (ii) āRisky consequencesā; (iii) āProducing acceptable storiesā and (iv) āUntellable storiesā.
In a performative analysis of two narratives of people with multiple and complex needs (Parts A and B), I found two contrasting ways of responding to the invitation to tell a recovery story: a ānarrative of personal lackā and a ānarrative of resistanceā. I demonstrate how the genre of ārecovery narrativeā, with its focus on transformation at the level of personal identity, may function to occlude social and structural causes of distress, and reinforce ideas of personal responsibility for ongoing distress in the face of unchanging living conditions.
Conclusion: The recovery narratives of people from socially marginalised groups are diverse and multidimensional. Told in some contexts, they may hold power to challenge the status quo. However, telling stories of lived experience and recovery is risky, and there may be pressure on narrators to produce āacceptableā stories, or to omit or de-emphasise experiences which challenge dominant cultural narratives. A recovery-based approach to the use of lived experience narratives in research and practice may be contributing towards an over-emphasis on individualist approaches to the reduction of distress. This over-emphasis can be seen to reflect what has been identified as a global trend towards the āinstrumentalā use of personal narratives for utilitarian purposes based on market values. Attention to power differentials and structural as well as agentic factors is vital to ensure that the use of narratives in research and practice does not contribute towards a decontextualised, reductionist form of recovery which pays insufficient attention to the economic, institutional and political injustices that people experiencing mental distress may systematically endure. A sensitive and socially just use of lived experience narratives will remain alert to a variety of power dimensions present within the contexts in which they are shared and hear
Faculty of Mathematics and Science 1st Graduate Research Day Conference, 2022
FMS Graduate Research Day (FMS GRaD) is an academic conference open to all FMS students with a mandate to celebrate and communicate Brock University research and teaching. The FMS GRaD 2022 conference was hosted by the Deanās office of the Faculty of Mathematics and Science and Graduate Mathematics and Science Society at Brock University. With 57 presenters and over 300 attendees this first FMS GRaD held on September 16th 2022 strengthened the STEM research community and highlight the research and profile of FMS graduate student research programs
2023-2024 academic bulletin & course catalog
University of South Carolina Aiken publishes a catalog with information about the university, student life, undergraduate and graduate academic programs, and faculty and staff listings
Short-term forecast techniques for energy management systems in microgrid applications
A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Sustainable Energy Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn the 2015 Paris Agreement, 195 countries adopted a global climate agreement to limit the
global average temperature rise to less than 2Ā°C. Achieving the set targets involves increasing
energy efficiency and embracing cleaner energy solutions. Although advances in computing
and Internet of Things (IoT) technologies have been made, there is limited scientific research
work in this arena that tackles the challenges of implementing low-cost IoT-based Energy
Management System (EMS) with energy forecast and user engagement for adoption by a
layman both in off-grid or microgrid tied to a weak grid.
This study proposes an EMS approach for short-term forecast and monitoring for hybrid
microgrids in emerging countries. This is done by addressing typical submodules of EMS
namely: load forecast, blackout forecast, and energy monitoring module. A short-term load
forecast model framework consisting of a hybrid feature selection and prediction model was
developed. Prediction error performance evaluation of the developed model was done by
varying input predictors and using the principal subset features to perform supervised training
of 20 different conventional prediction models and their hybrid variants. The proposed
principal k-features subset union approach registered low error performance values than
standard feature selection methods when it was used with the ālinear Support Vector Machine
(SVM)ā prediction model for load forecast. The hybrid regression model formed from a fusion
of the best 2 models (ālinearSVMā and ācubicSVMā) showed improved prediction performance
than the individual regression models with a reduction in Mean Absolute Error (MAE) by
5.4%.
In the case of the EMS blackout prediction aspect, a hybrid Adaptive Similar Day (ASD) and
Random Forest (RF) model for short-term power outage prediction was proposed that predicted
accurately almost half of the blackouts (49.16%), thereby performing slightly better than the
stand-alone RF (32.23%), and ASD (46.57%) models. Additionally, a low-cost EMS smart
meter was developed to realize the implemented energy forecast and offer user engagement
through monitoring and control of the microgrid towards the goal of increasing energy
efficiency
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