2821 research outputs found
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Long-term mental health impacts of the Covid-19 pandemic on university students in the UK : a longitudinal analysis over 12 months
Full text linkUniversity students in the UK have encountered many challenges as a result of the COVID-19 pandemic. This research aimed to track the mental well-being of a large sample of British university students (n = 554) over a one-year period of the COVID-19 pandemic, capturing data at four time points between May 2020 and May 2021. Overall retention after 12 months was 34.73%. Findings showed the COVID-19 pandemic has caused a significant, negative impact on the well-being of British university students. Students are suffering from prolonged and high levels of psychological distress and anxiety. Levels of flourishing in students are still very low. The different phases of the pandemic appear to have played an influential role in student mental health. The practical implications for higher education and recommendations for future research are discussed
A case study using virtual reality to prime knowledge for procedural medical training
Full text linkProcedural training within medical education relies heavily on skill practice. This training requires developing a cognitive understanding of a procedure to prime learners before motor skill trials. With the high demand and costs of specialist equipment, virtual reality (VR) is poised to provide accessible content to develop cognitive understanding, and bridge the gap between knowledge and practice outside of dedicated training centres. Previous work in this field has focused on knowledge transfer, which is important yet insufficient to understand the interplay of instruction, usability, presence, and experience. All of which could impact learning outcomes and frequency of use. To have a more nuanced view of VR medical training beyond its knowledge transfer capability, we integrate HCI & games perspectives into our evaluation approach appraising the VR Bronchoscope Assembly (VR-Bronch) training
Is supported living a pathway to recovery? A preliminary investigation of a new model.
No full textEvidence suggests supported living can improve functioning and reduce need. However, its lack of a clear definition has presented significant challenges to establishing a definitive evaluation of its efficacy. The present study evaluated the efficacy of a defined model of supported living using in terms of reductions made to aspects of clinical and social recovery.
A naturalistic, non-controlled assessment was conducted using the Camberwell Assessment of Need Clinical Scale (CAN-C) with a sample of adults with severe and enduring mental illness residing with a UK-based mental health company at one of twelve UK locations.
Analysis regarding preliminary outcomes relating to health and social need is presented with comparison between admission and 6-months post-admission (N=90). Additional analysis relating to outcomes at twelve-months is also provided (N=39). Significant outcomes are noted at both timepoints in terms of reducing unmet need and levels of formal and informal help given/required during tenancy.
Our findings support that, even in the absence of clinical recovery, opportunities exist to make meaningful and valuable improvements to unmet need and functional independence, with implications for clinical practice in the context of supported living.
The findings provide encouraging early indications of the benefits of the model in making meaningful reductions to functional and psychological needs in individuals with severe and enduring mental illness
Ultra-low losses SiC based shunt active power filter for harmonics mitigation and harmonics power recovery in industrial power systems
Full text linkThe classical method to suppress resonances in power systems is by installing passive
dampers in parallel to the loads. However, observations indicate significant power losses
due to harmonic currents flowing over passive dampers. Certainly, passive dampers
absorb harmonic active power and dissipate this power as heat on their resistive elements
leading to energy waste. On the other hand, the passive damper counterpart is the
active damper. The latter is also known in technical literature as Voltage driven shunt
active power filter (VSAPF). The active damper is a power-electronics-based system that
emulates a virtual resistance at harmonic frequencies. Truly, very little was known about
the harmonic power absorption on active dampers. Therefore, this dissertation delves
into a profound analysis of the capability of an ultra-low losses active damper based
on SiC semiconductor technology to process the harmonic power intake and perform
harmonic power recovery. Harmonic power recovery in this context is understood as the
process of transforming the harmonic active power absorbed into fundamental power
that is injected back into the power system. The next topic that is addressed is the
reduction in the fundamental power demanded by an industrial facility due to the
recovery of harmonic active power. To this end, this dissertation analyzes the power
balance flow of a distribution power system (e.g., industrial grid) that includes an ultra-low losses active damper. Arising out of the power balance flow analysis, it was found
that the active damper with harmonic recovery function achieves a 1.4% reduction on the
fundamental power demanded compared to a passive damper. Naturally, the lower the
active damper´s power losses, the higher will be the amount of harmonic active power
that can be recovered from the power system. Therefore, during this research work,
various power electronics converters topologies are analysed to find the best possible
design for the active damper with harmonic power recovery functionality. Arising out
of this investigation, it was found that the conventional three-level neutral point piloted
converter (3L-TNPC) and the asymmetrical three-level converter (3L-ASYM) are the most
suitable power circuit topologies for the ultra-low losses active damper. The former
topology, the 3L-TNPC, exhibits the lowest power losses for switching frequencies up
to 60 kHz. And then, the 3L-ASYM topology presents the lowest losses among all
the studied power circuits for switching frequencies beyond 70 kHz. Furthermore, as
an active damper forms a closed loop between harmonic voltages and compensating
currents, its stability must be ensured. Thus, a careful design of the VSAPF control
system and its inner current controllers is essential. On account of this, this dissertation
proposes using the Ragazzini method to design the VSAPF’s inner current controllers.
Furthermore, the direct design of the inner current controllers on the discrete domain
using the Ragazzini method increases the current controllers’ bandwidth by a factor of
three compared to the controllers’ design with conventional methods. Consequently,
the increased current controller’s bandwidth achieved through the Ragazzini method
pushes the stability limit of the active damper forward compared with traditional current
controller designs
Developing emotional intelligence using simulation with pre-registration nursing students: a mixed methods enquiry
Full text linkBackground: Nursing students need to develop Emotional Intelligence (EI) skills to deliver effective care and navigate the challenges of their profession. However, the practical application of teaching EI in pre-registration nursing education and the development of key components such as self-awareness, social skills, and decision-making abilities have been neglected in existing literature. This oversight hampers students' ability to effectively apply EI in their future practice. Moreover, traditional learning methods are predominantly used, with minimal integration of simulation-based learning (SBL) techniques.
Purpose: The study examines the effectiveness of teaching EI using simulation with pre-registration nursing students in an English Higher Educational Institute (HEI).
Methods: A mixed methods sequential explanatory approach was selected to address the research question. An SBL intervention was developed and delivered for nursing students in the university campus. A quantitative pre-test/post-test intervention design was employed to assess the students’ EI scores. EI score. A total of 116 pre-registration nursing students from three academic year groups completed the Trait Emotional Intelligence Short Form Questionnaire (TEIQue-SF). Subsequently, post-intervention focus group interviews were conducted with both students and nursing tutors.
Results: The findings from both quantitative and qualitative data demonstrated a significant effect of SBL on nursing students' post intervention EI scores. The students in this study scored a higher level of overall EI after the implementation of the simulation intervention and reported four main themes: SBL intervention enhanced awareness of their own and others’ emotions, learning to control their own emotions as well as feeling more empowered to transition to engage in clinical practice. The combined findings from both the quantitative and qualitative studies generated insights of the phenomenon to develop EI among nursing students.
Conclusion and Recommendation: To produce emotionally intelligent nurses capable of managing negative emotions, reducing stress and burnout, and providing better patient care, innovative and structured development of EI in pre-registration nursing education is necessary. The use of SBL in pre-registration nursing programs promotes optimal interaction, team-based learning, and decision-making while learners practice EI skills in realistic scenarios. Integrating EI competencies throughout all academic years, with a focus on the initial phase of nursing student recruitment, is imperative. It is recommended that higher education communities mandate EI training in the nursing curriculum across all academic years and conduct further research on the relationship between simulation and EI to embed EI training effectively
Carbonic acid gas emission rating by vehicles using datascience techniques
No full textOne factor contributing to the warming of the upper orbit is the rollout of man-made pollutants into the eco system (biogas, Dioxide, laughing gas, and so on). Approximately 14% of total worldwide carbon dioxide emissions are attributed to the road transport. Wheels dust are dangerous to us and contain global warm gases that leads to changes in climate. Products of gas and diesel fuels that include NO2, CO, CH, C6H6, CH2O. Wheels also emit CO2, common human-caused global warm gas. It has been set emission targets to dramatically reduce highway's contribution to Dioxide. These are inferred from the global weather conference's goal of keeping the peak warming of the planet to a maximum of 2 degrees Celsius until 2100. In order to accomplish, in this study, a machine learning hybrid algorithm was developed in the combination of many classifications’ algorithm to find the vehicle CO2 emission with high accuracy rate. The results show that hybrid models can produce more accuracy with a lower error rate when developing an application for emission rating. Accurate carbon emission prediction models can aid in the development of emission-reduction policies
Academic performance prediction using machine learning algorithms
Full text linkThe objective of the study is to use a method to predict student
performance during the semesters and to compare accuracy perceptron for a
dataset of student performance. In this regard, Machine Learning techniques
were applied to the student performance dataset provided by the Kaggle.com
website. Multilayer Perceptron, Random Forest, SVM, Naïve Bayes, Decision
tree and K-NN algorithms were used to predict the Grade result of students as a
factor of performance. The Student Performance dataset is used to forecast how
well students will perform in their tests. As a result, with 94.9% accuracy, the
results were predicted
Comparative analysis on the use of teleconsultation using support vector regression and decision tree regression to predict patient satisfaction
No full textTeleconsultation is the use of electronic information and communication technology
to assist and provide medical care to patients who are unable to go to a healthcare facility for
treatment. Globally, teleconsultation is used to provide medical care in a variety of
specialisations, for different ailments, and in a variety of ways. Over the past years, significant
advancement in technology has improved the accessibility and standard of care that is received
by patients through teleconsultation. Over time, researchers have examined the benefits and
drawbacks of teleconsultation in comparison to conventional patients visit but still note that the
benefits of teleconsultation outweigh its drawbacks as patients can easily have access to quality
medical care attention remotely, easily, and timely. Recently, researchers have documented the
use of machine-learning techniques to predict diseases and patient experiences like satisfaction,
however, there are few papers on the prediction of experiences compared to the prediction of
diseases. Therefore, this paper adopts the use of Supervised Machine Learning techniques in
training and testing patients’ dataset and specifically used regression to predict patient
satisfaction. Comparing the results gotten for Support Vector Regression (SVR) Model (using
radial basis function kernel) with Decision Tree Regression, it is evident that SVR Model is the
best-fit model for the dataset because it has a lower mean squared error of 1.061899561612261
for test data compared to the mean squared error value of DTR Model which is
2.6202531645569622. Hence, this paper concludes that SVR Model is the best-fit model to be
used with teleconsultation in predicting patient satisfaction. However, the paper recommends that
more machine learning algorithms should be explored and implemented with teleconsultation in
treating patients and improving other healthcare services