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“Artistic practice as Embodied Learning: Reconnecting Pedagogy, Improvisation, and Composition,” Rethinking the Teaching of Music Performance in Higher Level Institutions
Get PDFIntegrated Data-Driven Approach for Early Pollution Detection and Management in the Thames River Ecosystem
Get PDFThe increasing pollution levels in rivers have become a serious concern worldwide due to their
detrimental impact on ecosystems and human health. Recently, there has been a growing recognition of the need for early warning systems (EWS) to monitor and manage water quality in
river ecosystems [1]. EWS is a method that is used to detect and predict potential risks or hazards before they occur. It helps alert individuals, organisations, or communities and provides them with timely information to take necessary precautions and actions to minimise the impact of the
anticipated event [2]. EWS for water quality management also can be efficient when real-time data (both water quality and quantity) can be combined with real-time flood forecasting [3].
This study presents a new method based on data-driven models for early warning pollution detection in the Thames River. The proposed method collects and analyses various types of data, including weather data and water quality parameters obtained from water samples and sensing systems. These inputs are integrated into a robust computational framework to forecast and identify potential pollution incidents in the Thames River system. The data-driven model incorporates real-time weather data to encompass the dynamic nature of pollution levels. The model can identify high-risk situations and issue timely warnings to prevent further pollution by analysing historical weather patterns and their correlation with pollution incidents. The system's
computational framework utilises a deep neural network to analyse and interpret the collected data. The model is fine-tuned and calibrated using historic data, allowing it to effectively recognise and predict pollution events in real-time for every flood event through combined sewer overflow
structures. By integrating historical and real-time data, the model can enhance predictive capabilities of pollution spread in the river system and hence prepare the relevant bodies to take appropriate actions in time.
The proposed method holds great promise in mitigating the adverse impacts of pollution on the river's ecosystem and the surrounding communities. By integrating diverse data sources, including in-situ measurements, sensing systems, and weather information, the model provides a holistic understanding of pollution dynamics and enables proactive pollution control measures.
Implementing this model can contribute significantly to preserving the health and ecological integrity of the Thames River, serving as a blueprint for other river systems facing similar pollution challenges worldwide
Enhancing flood risk mitigation by advanced data-driven approach
Get PDFFlood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the
complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges
by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50–70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP
significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management
Dressing for Disorder: Examining the Effect of Enclothed Cognition and Uniform Type on Police Officers’ Self-Perception
Get PDFPublic order policing in the United Kingdom is a specialist response to events of large
crowds, public safety concerns or the risk of disorder. The police officers assigned to
public order policing will often be wearing specialist protective uniform, which is likely
to be different to that worn in their ordinary, daily role. A range of research has been
conducted to examine how the public perceive police officers in different types of
uniform, however very little exists to consider how these officers perceive themselves
when wearing specialist, or ordinary uniform.
This study uniquely used the theoretical framework of enclothed cognition to examine
the way officers perceive themselves whilst wearing their ordinary, and alternatively
their public order uniform, and to compare this self-perception across the ordinary and
public order roles. Thematic analysis is used to analyse qualitative data from 20 semi
structured interviews with police officers in their ordinary, or public order roles. This
research fills a clear gap in the existing body of research by utilising authentic police
officer participants, on duty, and dressed in the uniform appropriate to their role.
This research makes two contributions. Firstly, it provides academic evidence in the
enclothed cognition field examining police officers’ self-perception with participants
who are authentic, operational police officers. This research found that enclothed
cognition does influence officers’ self-perception, but significantly, this is a result of the
meaning and experience associated with previously wearing it, and not the physical
presence of the uniform itself. Secondly this research contributes to professional
practice with the development of evidence-based policing and informing decision
making regarding public order dress codes. This research concludes by making
recommendations relevant to developing police public order training and commander
briefing tools
Access to personalised dementia care planning in primary care: a mixed methods evaluation of the PriDem intervention
Get PDFObjectives: Provision of personalised care planning is a national priority for people with dementia. Research suggests a lack of quality and consistency of care plans and reviews. The PriDem model of care was developed to deliver feasible and acceptable primary care-based postdiagnostic dementia care. We aimed to increase the adoption of personalised care planning for people with dementia, exploring implementation facilitators and barriers.
Mixed-method feasibility and implementation study.
Setting: Seven general practices from four primary care networks (PCNs) in the Northeast and Southeast of England.
Participants: A medical records audit collected data on 179 community-dwelling people with dementia preintervention, and 215 during the intervention year. The qualitative study recruited 26 health and social care professionals, 14 people with dementia and 16 carers linked to participating practices.
Intervention: Clinical dementia leads (CDL) delivered a 12-month, systems-level intervention in participating PCNs, to develop care systems, build staff capacity and capability, and deliver tailored care and support to people with dementia and their carers.
Primary and secondary outcome measures: Adoption of personalised care planning was assessed through a preintervention and postintervention audit of medical records. Implementation barriers and facilitators were explored through semistructured qualitative interviews and non-participant observation, analysed using codebook thematic analysis informed by Normalisation Process Theory.
Results: The proportion of personalised care plans increased from 37.4% (95% CI 30.3% to 44.5%) preintervention to 64.7% (95% CI 58.3% to 71.0%) in the intervention year. Qualitative findings suggest that the flexible nature of the PriDem intervention enabled staff to overcome contextual barriers through harnessing the skills of the wider multidisciplinary team, delivering increasingly holistic care to patients.
Conclusions: Meaningful personalised care planning can be achieved through a team-based approach. Although improved guidelines for care planning are required, commissioners should consider the benefits of a CDL-led approach
Yoga is a way of life” exploring experiences of yoga as a treatment for substance use: An interpretative phenomenological analysis.
Get PDFIntroduction: Yoga is a form of complementary medicine for substance use disorder (SUD). Randomized controlled trials involving yoga for the treatment of SUD found that yoga practice reduces the risk of relapse, improves mood and wellbeing for people undergoing treatment for SUD; however, the lived experience of yoga practice involving the benefits on reducing SUD is unknown. The aim of the present study was to examine the in-depth experience of yoga to inform the treatment of SUD.
Methods: Five semi structured interviews exploring experiences of yoga among people with a prior history of substance use. Four out of the five participants reported prior use of alcohol, and one reported the use of ‘GBL’ and methamphetamine. Data were analysed using Interpretative Phenomenological Analysis.
Results: Analysis resulted in three final Superordinate themes 1) Growing awareness of the body, mind, and emotions 2) Yoga opens a positive way of life and 3) Blending the worlds of yoga and 12 step recovery. Yoga was reported to enhance awareness of muscle tension, reduce physical stress, increase positive emotions, and build tolerance to negative emotions. The integration of the eight-limb philosophy of yoga, notably withdrawing of the senses, helped combat internal cues and triggers (negative thoughts and emotions) for relapse. Yoga was reported to be compatible with an abstinence-based lifestyle found in 12-step mutual aid programs and helped extend social networks to support long term abstinence.
Conclusions: The experience of integrating the eight-limb philosophy to support abstinence and the asana practice helped participants to reduce cue reactivity. Yoga appeared to enhance interoceptive awareness which is useful for reducing physical stress related to triggers for relapse, making yogic practice a valuable tool to integrate within mainstream group and individual relapse prevention programs. Therefore, programs and health policymakers may want to consider treatments that integrate yogic practices to enhance and support long term abstinence for SUD
Emotion-Aware Online Learning: A Hybrid Sentiment Analysis-based Model to Augment Online Learning Pedagogies using Artificial Intelligence Algorithm
No full textThe rapid growth and expansion of online learning, especially during the Coronavirus Disease
2019 (COVID-19) pandemic, have highlighted its significance across various sectors, including traditional education, corporate training, and workshops. However, challenges such as
student engagement and satisfaction during learning sessions continue to affect its effectiveness. Students’ engagement and emotions play an essential role in learning, directly impacting their satisfaction with the content, the platform, and the instructor’s teaching methods.
Effective student engagement can be achieved through continuous prompting and gamification, providing timely feedback during learning sessions, and creating balanced groups of
students in collaborative learning settings. Emotional recognition can be achieved using feature extraction from images and videos, sentiment analysis of students’ textual utterances,
physiological sensors, and speech or voice recognition.
This thesis addresses two major challenges in online learning: achieving balanced heterogeneous groups in collaborative learning to improve student engagement and accurately
predicting students’ emotions in real-time, as it requires accurate recognition and classification of multiple emotions in real-time from different modalities.
This thesis introduces a novel activity-based technique for Dynamic Group Formation (DGF)
to address the group formation challenge. This technique automatically swaps students into
different groups based on their learning styles and knowledge levels to ensure balanced heterogeneous groups. These balanced groups are then used in the Intelligent Tutor-Supported
Collaborative Learning System (ITSCL), an online platform designed for collaborative learning, to improve the learning process and increase educational gains. Additionally, the proposed technique is validated through user experience experiments to evaluate its practical
application in real-world scenarios
Another significant contribution of this thesis is the development of a meta-emotional model.
This model utilises data from Student Utterances (SUs) and facial gestures to improve student satisfaction and engagement collected during 10 online learning sessions. The proposed
meta-emotional model employs a transfer learning-based Bi-directional Long Short-Term
Memory (Bi-LSTM) deep learning model to classify SUs into emotional categories: engaged,
bored, confused, frustrated, and neutral. Furthermore, a Convolutional Neural Network
(CNN)-based modified MobileNet model is used to classify facial gestures into these emotional categories.
An intelligent online learning platform (Intelli-Student) has been developed to analyse the
effectiveness of the meta-emotional model. This platform integrates meta-emotional models (SUs and facial gestures) with an Intelligent Tutoring System (ITS). ITS main purpose
is to intervene during student inactivity and prompting in the online learning session. The
Intelli-Student platform recognises students’ emotions from SUs and facial gestures and classifies them into academic emotion classes. The system’s emotion detection modules provide
real-time feedback to the instructor about students’ understanding and engagement during
learning sessions. Moreover, the system provides overall class-level and individual student
engagement levels to the instructor during and at the end of each session. This information
offers insights into student learning experiences and satisfaction during learning sessions,
which helps to improve online pedagogies, learning content, and student engagement. By
providing timely and precise feedback, the system enhances the adaptability and responsiveness of online learning environments, ensuring a more personalised and effective educational experience for each student
Towards a Socially Conscious Start-up Brand: EDI in Entrepreneurial Branding
Get PDFSuccessful start-ups are those capable of evoking customer loyalty. To achieve this, a well-crafted brand with a clear brand message that resonates with the target audience is needed. The pandemic, and its aftermath, has led to heightened scrutiny of brands by audiences, with consumers spending more time and money online. Purchase decisions and brand loyalty are increasingly influenced by what brands stand for, with a preference for those that believably prioritise people over profits. Nearly two-thirds of UK consumers expect brands to address societal issues. This trend is particularly pronounced among Generation Z, the fastest-growing demographic in the UK and other advanced economies in terms of spending power. This age cohort differs in several respects from preceding generations, showing a distinct hunger for social causes relating to equity, diversity and inclusion (EDI).
This paper presents findings from an investigation into Generation Z's perception of EDI as part of entrepreneurial market communications. Our research aims to explore the target audience's awareness and attitude toward the inclusion of EDI elements in start-up brands and to ideate tangible recommendations for entrepreneurs in terms of embracing EDI as part of their brand’s activities.
Guided by Design Thinking, a total of 35 semi-structured interviews were conducted with young adults in Greater London, UK. Through inductive Thematic Analysis, four key themes emerged: (i) There is currently limited visibility and awareness of start-ups with EDI branding; (ii) there is a desire for authenticity alongside distrust of brands delivering on this; (iii) there is an expectation of intersectional diversity; and (iv) there is a limited risk of 'cancel culture' for start-ups. Based on these insights, four tangible recommendations were formulated for crafting socially conscious start-up brands: (i) embrace intersectional diversity internally and externally; (ii) cultivate a community to co-create EDI initiatives; (iii) utilising/collaborate with existing EDI associations; and (iv) prioritise EDI efforts in branded communications
The Role of Previous Experience in the Analysis of the Psychological Contract and its Outcomes During the Socialization Process: A Signalling Theory Perspective
Get PDFPsychological contract theory has largely neglected the role of previous experience. In this study, we examine how previous work experience influences outcomes of communication with organizational insiders during organizational socialization among healthcare staff. We develop a model based on signalling theory, within which information acquisition during socialization is associated with psychological contract fulfilment, which is in turn is related to better health, happiness, and social relationships. Moderated mediation analysis based on data collected at entry and three months later confirms indirect effects between three types of information acquisition and three employee outcomes via the mediating role of psychological contract fulfilment. Importantly, these indirect effects are present only for inexperienced newcomers. Our findings build on signalling theory and add to knowledge about how the psychological contract forms during early socialization. They also suggest that organizations should pay particular attention to inducting inexperienced newcomers
Optogenetic Multiphysical Fields Coupling Model for Implantable Neuroprosthetic Probes.
Get PDFOptogenetic-based neuroprosthetic therapies are increasingly being considered for human trials. However, the optoelectronic design of clinical-grade optogenetic-based neuroprosthetic probes still requires some thought. Design constraints include light penetration into the brain, stimulation efficacy, and probe/tissue heating. Optimisation can be achieved through experimental iteration. However, this is costly, time-consuming and ethically problematic. Hence it is highly desirable to have an alternative to excessive animal trials. Thus, a simulation tool for optimising probe design can be an important benefit for the community. The challenge is to understand the interplay between the optical, neural and thermal aspects in the interaction of probe and living neural tissue. In this work, we propose a model which combines these aspects to allow clinically orientated neuroprosthetic teams to design neuroprosthetic probes for optogenetic therapies. Our model provides analyses for optical, thermal and optogenetic electrophysiological processes based on the energy equivalence and exchange among different physical fields. To validate and calibrate the model, optogenetic implantable neuroprosthetic arrayed probes based on miniature LEDs were developed. Then, optical, thermal measurement and neural photocurrent recording experiments were implemented on the probes. We can then provide analysis on exemplar arrayed neural probes