77 research outputs found

    Grounds for a Third Place : The Starbucks Experience, Sirens, and Space

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    My goal in this dissertation is to help demystify or “filter” the “Starbucks Experience” for a post-pandemic world, taking stock of how a multi-national company has long outgrown its humble beginnings as a wholesale coffee bean supplier to become a digitally-integrated and hypermodern cafĂ©. I look at the role Starbucks plays within the larger cultural history of the coffee house and also consider how Starbucks has been idyllically described in corporate discourse as a comfortable and discursive “third place” for informal gathering, a term that also prescribes its own radical ethos as a globally recognized customer service platform. Attempting to square Starbucks’ iconography and rhetoric with a new critical methodology, in a series of interdisciplinary case studies, I examine the role Starbucks’ “third place” philosophy plays within larger conversations about urban space and commodity culture, analyze Starbucks advertising, architecture and art, and trace the mythical rise of the Starbucks Siren (and the reiterations and re-imaginings of the Starbucks Siren in art and media). While in corporate rhetoric Starbucks’ “third place” is depicted as an enthralling adventure, full of play, discovery, authenticity, or “romance,” I draw on critical theory to discuss how it operates today as a space of distraction, isolation, and loss

    Machine Learning in Driver Drowsiness Detection: A Focus on HRV, EDA, and Eye Tracking

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    Drowsy driving continues to be a significant cause of road traffic accidents, necessi- tating the development of robust drowsiness detection systems. This research enhances our understanding of driver drowsiness by analyzing physiological indicators – heart rate variability (HRV), the percentage of eyelid closure over the pupil over time (PERCLOS), blink rate, blink percentage, and electrodermal activity (EDA) signals. Data was collected from 40 participants in a controlled scenario, with half of the group driving in a non- monotonous scenario and the other half in a monotonous scenario. Participant fatigue was assessed twice using the Fatigue Assessment Scale (FAS). The research developed three machine learning models: HRV-Based Model, EDA- Based Model, and Eye-Based Model, achieving accuracy rates of 98.28%, 96.32%, and 90% respectively. These models were trained on the aforementioned physiological data, and their effectiveness was evaluated against a range of advanced machine learning models including GRU, Transformers, Mogrifier LSTM, Momentum LSTM, Difference Target Propagation, and Decoupled Neural Interfaces Using Synthetic Gradients. The HRV-Based Model and EDA-Based Model demonstrated robust performance in classifying driver drowsiness. However, the Eye-Based Model had some difficulty accurately identifying instances of drowsiness, likely due to the imbalanced dataset and underrepre- sentation of certain fatigue states. The study duration, which was confined to 45 minutes, could have contributed to this imbalance, suggesting that longer data collection periods might yield more balanced datasets. The average fatigue scores obtained from the FAS before and after the experiment showed a relatively consistent level of reported fatigue among participants, highlighting the potential impact of external factors on fatigue levels. By integrating the outcomes of these individual models, each demonstrating strong performance, this research establishes a comprehensive and robust drowsiness detection system. The HRV-Based Model displayed remarkable accuracy, while the EDA-Based Model and the Eye-Based Model contributed valuable insights despite some limitations. The research highlights the necessity of further optimization, including more balanced data collection and investigation of individual and external factors impacting drowsiness. Despite the challenges, this work significantly contributes to the ongoing efforts to improve road safety by laying the foundation for effective real-time drowsiness detection systems and intervention methods

    New insights on the multidimensionality of fatigue and on its relationship with cognitive impairments in multiple sclerosis

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    Multiple Sclerosis (MS) is an inflammatory disease of the central nervous system (CNS), and it represents the most common cause of irreversible impairment in young adults, affecting about 2.5 million individuals worldwide. In MS, acute attacks of inflammation, leading to demyelination and axonal loss, determine the accumulation of disabilities, varying in number, nature, and severity. Indeed, motor, sensory, cognitive, and behavioral symptoms may manifest at different times during the disease's variable clinical course. Fatigue is a complex and multifaceted phenomenon and one of the most prevalent and disabling symptoms of MS, affecting 75%–90% of patients. Despite its prevalence, MS- related fatigue is still poorly understood. The absence of a well-validated definition and of clear insights into its pathophysiological causes makes fatigue a hybrid symptom, approached within the context of different disciplines, each with their own methods and tools. As a result, the scientific literature abounds with irreconcilable data, leaving fatigue in a dark shadow zone, at the expense of MS patients still lacking adequate therapies and strategies of management. The main topic of this thesis relates to the multidimensional nature of fatigue, to its variability, and its effects on attentional processes, most commonly affected in MS patients. Specifically, studies presented in the current thesis address four research issues: (i) are physical and mental fatigue two distinct constructs? (ii) how do physical and mental fatigue vary within a short (within a day) and long (within a year) period? (iii) how do induced physical and mental fatigue impact the attentional functions of alerting, orienting, and conflict resolution in MS? The main results of the studies are reported: a) A clear distinction between physical and mental fatigue has been psychometrically documented in MS patients. b) MS patients reported experiencing more overall fatigue than Controls. c) A gradual increase in overall fatigue from the morning to the evening was reported by MS participants. d) Across experiments physical fatigue was significantly more pronounced in MS patients as compared to Controls. e) Both MS patients and Controls reported having experienced more overall fatigue in the past (one year ago) than in the present (the last 24 hours). f) MS patients were slower as compared to Controls in performing attentional tasks; however, inconclusive results have emerged regarding the effects of physical and mental fatigue on attentional processes. g) Sleep quality and depression were both associated with fatigue across the experiments. The relationship between self-efficacy, general cognitive functioning, functional deterioration, and physical and mental fatigue is fragmented, thus preventing a clear conclusion

    A History of Psychological Boredom: The Utility of Boredom in the Practice of Psychological Science

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    The 100-year plus history of psychologists attempting to establish boredom as a quantifiable construct provides insight into the problems associated with how psychology adopts its subject matter. By borrowing terms from the public and assuming they represent universal aspects of human nature, the discipline has spurred critical inquiry regarding the practices hidden assumptions and theory. In particular, boredom, with its associations with both existential and trivial concerns, exposes the limitations of the practice of scientific psychology and reflects the disciplines own conflicted identity. In order to facilitate an examination of these theoretical issues, this historical examination focuses on the failed attempts by 1970s personality psychology and 1990s positive psychology to domesticate the concept. With the inclusion of the publics boredom discourse during these decades, the cultural influence on these disciplines theorizing is excavated. These influences complicate attempts by psychologists to practice as a science and provide a reason to take pause amid repeated calls to unify the discipline

    Methods and techniques for analyzing human factors facets on drivers

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    Mención Internacional en el título de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident. Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces. The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed. Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn. From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models. Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y Tecnología Informåtica por la Universidad Carlos III de MadridPresidente: José María Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart

    In Her Words: Women’s Accounts of Managing Drug-related Risk, Pleasure, and Stigma in Sweden

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    When it comes to the field of drug studies, researchers have tended to privilege men’s perspectives and experiences, assuming women to be mostly marginal, as primarily victims and accomplices. Further, when women’s experiences are taken into account, a view of them as only women has tended to be pushed to the forefront. As such, we are sorely lacking research departing from women’s own recollections of their involvement with drugs that also considers how social location from the intersection of multiple categories of being (e.g. gender, class, type of drug involvement, etc.) characterises these experiences.This dissertation contributes to the literature on drugs and drug involvement by drawing on the accounts of a group of twenty-six women who have, at some point in their lives, used, bought, shared, and/or sold drugs in Sweden. The overarching objective has been to understand why participants started, continued, and sometimes stopped being active with drugs and how they managed drug-related risk, pleasure, and stigma in the contexts in which they were located. Participants’ accounts were analysed through a theoretical lens developed from a synthesis of social constructionism, intersectionality, and symbolic interactionism, thus making it possible to see how their experiences were embedded in specific contexts and how respondents described navigating and managing the challenges these posed.It emerged that respondents discussed their involvement with drugs as being considerably pleasurable and meaningful, but also heavily tinged by the risk of violence and stigma experienced in the illicit drugs market and in conventional society. Participants described developing numerous tactics to attempt to counter some of these risks and stigmatisation processes and, consequently, meanings because of and despite the circumstances they faced. Drugs and drug involvement gave respondents an opportunity to feel alternatively (dis)empowered, (in)capable, and (un)worthy of respect. These practices and meanings were necessarily mediated through participants’ social location, but resourcefulness and creativity also played an important role. Ultimately, respondents’ accounts show that they were simply doing what they could to create meaningful lives for themselves with the resources available to them

    Creating Through Mind and Emotions

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    The texts presented in Proportion Harmonies and Identities (PHI) Creating Through Mind and Emotions were compiled to establish a multidisciplinary platform for presenting, interacting, and disseminating research. This platform also aims to foster the awareness and discussion on Creating Through Mind and Emotions, focusing on different visions relevant to Architecture, Arts and Humanities, Design and Social Sciences, and its importance and benefits for the sense of identity, both individual and communal. The idea of Creating Through Mind and Emotions has been a powerful motor for development since the Western Early Modern Age. Its theoretical and practical foundations have become the working tools of scientists, philosophers, and artists, who seek strategies and policies to accelerate the development process in different contexts

    Development of guidelines to reduce road accidents amongst community members in Botswana: a public health issue

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    Bibliography: leaves 124-147BACKGROUND The leading and increasing contributor to the regional and global disease burden that leads to death and disability is represented by road accidents. An enormous toll on individuals together with communities and national economies has been observed because of the occurrence of road accidents. AIM The study developed guidelines to reduce road accidents amongst automobile drivers in Botswana. METHODS Study design The study was conceptualised using Haddon's theory and the mixed-method sequential explanatory design was utilized to conduct the study. Collection of data for this study was done over a period of time in two consecutive phases. v Study setting The study took place in Gaborone, and the study was conducted at Broadhurst Police Headquarters, Directorate of transport station, and the University of Botswana. Data Collection methods: The data for this study were collected through the usage of focus group interviews and document analysis using a checklist. The first phase involved collecting quantitative data through document analysis of 400 police records using a checklist. The second phase took place in Gaborone at the University of Botswana. It involved collecting qualitative data using two focus group interviews with various stakeholders like traffic police, third party claim officers, and emergency nurses/doctors who have been in contact with people involved in road traffic accidents. Study Population: The study population included traffic accident victims' documents at the police headquarters for Gaborone and Francistown, police and traffic officers, lawyers/third party claims officers, and emergency department staff such as nurses and doctors working in Gaborone and Francistown. Data analysis: A checklist was used in transforming observations of found categories into quantitative statistical data. Data generated from the content analysis were transformed into quantitative statistical data using a checklist. Quantitative data were entered and analysed principally using the Statistical Package for Social Sciences (SPSS 27) software to generate graphs and tables. Inconsistencies of the data set was managed by cleaning and editing the data. The data that were missing were not statistically imputed. The relationships of independent variables based on Haddon Matrix-like, drunk driving, unlicensed drivers, over speeding, deaths, and injuries were analysed against the dependent variable of having a road traffic accident using logistic regression. Qualitative vi data from focus group interviews was transcribed verbatim using a transcription protocol. Using transcription protocol ensured that transcription is done consistently and is of the appropriate type for analytic aims. Tesch’s framework for qualitative data analysis was used. UNISA, Botswana Ministry of Health and Wellness, and The Ministry of Defence, Justice, and Security granted the researcher the permission to conduct the study. Results The study found that most accidents are caused by the drivers’ carelessness followed by animals, both domestic and wild. The accidents had an impact on the health of drivers, passengers, and pedestrians. The accidents resulted in fatalities and lower limb fractures, upper limb fractures, and brain injuries. Over the past five years, Gaborone and Serowe recorded the highest cases of road traffic accidents. Most of the accidents occurred where there were no junction. Conclusion It is envisioned that the guidelines informed by research and literature will ensure a decrease in road traffic accidents and consequently fatalities and injuries among Botswana communities.Health StudiesD. Phil. (Public Health

    Stuck and Exploited. Refugees and Asylum Seekers in Italy Between Exclusion, Discrimination and Struggles

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    This volume analyses exclusion processes, segregation dynamics and the forms of discrimination of refugees and asylum seekers in Italy, where the reception system is marked by opaqueness and arbitrariness and is becoming increasingly similar to the model of “camps”. The numerous vibrant contributions present a fully-fledged system of inferiorization, characterised by labour exploitation, housing discomfort, meagre rights and control strategies, exacerbated by the COVID-19 pandemic, which has led to a sharp worsening of the health, work, housing and administrative conditions. A framework that has found opposition in the daily resistance and in the struggles of asylum seekers

    Vision-based Driver State Monitoring Using Deep Learning

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    Road accidents cause thousands of injuries and losses of lives every year, ranking among the top lifetime odds of death causes. More than 90% of the traffic accidents are caused by human errors [1], including sight obstruction, failure to spot danger through inattention, speeding, expectation errors, and other reasons. In recent years, driver monitoring systems (DMS) have been rapidly studied and developed to be used in commercial vehicles to prevent human error-caused car crashes. A DMS is a vehicle safety system that monitors driver’s attention and warns if necessary. Such a system may contain multiple modules that detect the most accident-related human factors, such as drowsiness and distractions. Typical DMS approaches seek driver distraction cues either from vehicle acceleration and steering (vehicle-based approach), driver physiological signals (physiological approach), or driver behaviours (behavioural-based approach). Behavioural-based driver state monitoring has numerous advantages over vehicle-based and physiological-based counterparts, including fast responsiveness and non-intrusiveness. In addition, the recent breakthrough in deep learning enables high-level action and face recognition, expanding driver monitoring coverage and improving model performance. This thesis presents CareDMS, a behavioural approach-based driver monitoring system using deep learning methods. CareDMS consists of driver anomaly detection and classification, gaze estimation, and emotion recognition. Each approach is developed with state-of-the-art deep learning solutions to address the shortcomings of the current DMS functionalities. Combined with a classic drowsiness detection method, CareDMS thoroughly covers three major types of distractions: physical (hands-off-steering wheel), visual (eyes-off-road ahead), and cognitive (minds-off-driving). There are numerous challenges in behavioural-based driver state monitoring. Current driver distraction detection methods either lack detailed distraction classification or unknown driver anomalies generalization. This thesis introduces a novel two-phase proposal and classification network architecture. It can suspect all forms of distracted driving and recognize driver actions simultaneously, which provide downstream DMS important information for warning level customization. Next, gaze estimation for driver monitoring is difficult as drivers tend to have severe head movements while driving. This thesis proposes a video-based neural network that jointly learns head pose and gaze dynamics together. The design significantly reduces per-head-pose gaze estimation performance variance compared to benchmarks. Furthermore, emotional driving such as road rage and sadness could seriously impact driving performance. However, individuals have various emotional expressions, which makes vision-based emotion recognition a challenging task. This work proposes an efficient and versatile multimodal fusion module that effectively fuses facial expression and human voice for emotion recognition. Visible advantages are demonstrated compared to using a single modality. Finally, a driver state monitoring system, CareDMS, is presented to convert the output of each functionality into a specific driver’s status measurement and integrates various measurements into the driver’s level of alertness
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