3830 research outputs found
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Contact Tracing using Graph Algorithms and exploring graph analytics in a Graph Database (Neo4j)
No full textThe SARS-COVID-19 epidemic is still spreading rapidly globally. Contact Tracing has played an important role during this emergency in identifying the people most likely to be high spreaders of the virus. Graph Data Science has recently come to the forefront in analysing connected data on a knowledge graph. It included various types of algorithms supporting graph analytics and machine learning workflows. Using GDS we can execute these algorithms we find the optimized result in Neo4j platform
This research explores the use of graph data science in the context of contact tracing. Common real-world scenarios are explored using Neo4j, the leading graph database management system. Graph Data Science algorithms including PageRank, betweenness centrality, Louvain community detection, label propagation, shortest path algorithms are explored, and results visualised. Results prove to be insightful
Key Factors and Barriers Affecting the Adoption of Cloud Computing in the Irish Government
No full textIn 2019 the Irish Government issued a cloud computing advice note informing all government
agencies to adopt a cloud-first strategy. Research has highlighted many issues and challenges
in adopting a cloud-first strategy, particularly for government agencies. However, there is an
enormous desire to adopt cloud computing if specific barriers are removed. This study aimed
to identify the key factors and barriers that affect cloud computing adoption in the Irish
Government. This study employs a quantitative approach using an online questionnaire
survey collecting data from fifty-three senior ICT staff working across various government
sectors. Descriptive statistics were used to answer the primary aims, and the central tendency
was measured using median and mode. The study identified data protection, procurement,
vendor and government support as barriers and key factors. Additionally cost, compatibility,
technology readiness, and cybersecurity were identified as key factors. This research can
support the Irish Government in expanding its cloud-first approach, establishing strategies
and policies to support future cloud adoption
Dating applications: attachment, motivations, and relational and health outcomes in a sample of early adults
No full textThis study aimed to test whether attachment patterns and dating app motivations predicted relational and health outcomes in a sample of early adults. 302 respondents completed an online self-report questionnaire. The cross-sectional and quantitative data was analysed using correlational tests. Although several hypotheses were only partially supported, there were numerous significant findings. As expected, secure attachment predicted relationship status. Love was a stronger dating app motivator than casual sex. The number of one-night-stands was not correlated with attachment patterns or health. These findings suggest dating apps facilitate positive interpersonal interactions for those with relational goals. However, attachment-anxiety and self-worth validation motivation predicted poorer health in current users. Given how relationships influence individuals’ mental well-being, understanding these risk factors of dating app use is critical. In conclusion, attachment styles and dating apps can impact health levels, although the small effects found highlight a need to incorporate additional variables in future studies
A mixed methods study of a Local Authority
No full textPublic Service Motivation (PSM) refers to the orientation of those who deliver public services
to people with the purpose of serving the public good. The study looks at how this might have
been affected by the Covid-19. Following a mixed methods approach, employees of a Local
Authority were surveyed to assess current PSM levels and whether this differed to pre-
pandemic levels. The majority of staff did not experience change but for those who did, it was
predominantly increased rather than decreased PSM. The combination of factors which
accounted for the resilience of PSM could not be isolated however. Analysis of what aspects
of the work is most valued by staff revealed three themes: Meaningful Work, Quality of Life
and Workplace Factors. The predominance of the Meaningful Work theme aligns with the
concept of PSM and it is suggested that it be explored for possible incorporation into HR
strategies
Investigating the role organizational culture plays in the relationship between work-related curiosity and workforce agility: antecedent or mediator?
No full textGlobalization, increased political uncertainty and unforeseen
economic instability, have forced corporations to reinvent themselves.
They need to develop an agile workforce to handle and adapt to
unforeseen changes, and organizational culture is an important
element for the success of a company. Yet, organizational culture has
not been fully explored with work-related curiosity and workforce
agility. The aim is to determine whether organizational culture
promotes work-related curiosity and, consequently, workforce agility.
This study found that there is no direct relationship between
organizational culture and workforce agility, and that organization
culture does not act as a mediator in the relationship between work-
related curiosity and workforce agility. It was also found that
organization culture does not have a straight relationship with work-
related curiosity, but it is a strong predictor of it. Using mixed methods
design combining a correlational and a between groups designs, with
data collected from 62 participants based in Brazil and Ireland, all the
hypotheses were tested. The findings add value to the existing
literature on the three constructs, helping companies find a way to
improve the performance of the workforce
Criminally insane; exploring society’s attitude and understanding of the insanity defense
No full textThe overall aim of the study is to explore society's understanding of why the insanity defense is necessary in a criminal court setting. This will be achieved by thematically analysing responses to an open-ended question based on society's
understanding of why mentally ill defendants receive more lenient sentencing once convicted. Furthermore, the study will quantitatively identify any population trends in the sample population pertaining to gender, or a participant's generation. This will be achieved by conducting a variety of statistical analysing; including Chi Square, Simple Linear Regression and Independent T-Test. Four themes emerged in the thematic analysis; Diminished responsibility, Medical Intervention, Generational Gap in Language and Responsibility and Lack of Conscious Intent suggesting society has an empathetic and understanding attitude towards the defense. The statistical test supports this assumption and additionally suggests that neither gender or generation influence a participant's attitude toward the defense
Facilitating recovery on a special rehabilitation unit during the Covid-19 pandemic
No full textThe impact of Covid-19 introduced many challenges for mental health services around the world. Recovery as a concept has gained traction internationally since the 1980s inspiring the development of Ireland first specialised rehabilitation unit (SRU) in Ireland. Despite resources being curtailed recovery continued to be facilitated by SRU staff. To get an in-depth understanding of the effects Covid-19 had on facilitating recovery on the SRU during the pandemic this study uses a semi-structured interview to conduct qualitative research exploring the experiences of 6 members of a multidisciplinary team (MDT) and through thematic analysis 6 high-quality themes were generated. The findings reveal a tri-structural narrative illustrating how the participants collectively overcame the obstacles they were confronted with through collaboration, adaptation, and innovative technology. Investigated are factors maximising the potential for successful recovery orientated practices to be facilitated, barriers that can be minimised and several implications based off the current research findings
Guilt, prosocial behaviour, and compliance among students who received the Pandemic Unemployment Payment
No full textA large volume of research suggests links between guilt, prosocial behaviour and compliance. To test these theories in the context of negative media coverage and financial gain from the PUP, a representative sample (n = 80) of fulltime students was surveyed. A quantitative, correlational, cross-sectional study was employed using psychological scales to collect data. The results indicate that negative media coverage had no significant impact on levels of self-reported guilt. There was a strong positive significant relationship between perceived significant financial gain from the PUP and levels of self-reported guilt. Results indicate a strong positive significant relationship between levels of guilt and prosocial behaviour, and between levels of guilt and compliance scores. Gender did not impact on self-reported guilt levels. The results from this study will add to existing research on the effects of guilt on prosocial behaviour and compliance. Implications for practice and recommendations for future research are discussed
UAE Bank's high-interest business loans: SME perspective
No full textGlobally small and medium enterprises make a big impact on the national economic growth of the country as well as the employment it creates. Similarly, the developing economy of the United Arab Emirates is largely dependent on SMEs for growth in countries’ GDP. Many factors like financial crisis, funding availability, and trust are creating a downturn for SMEs to obtain competitive interest rates on bank loans to grow their business. The aims to find if the null hypothesis – “High-interest rates provided by banks to SME's are leading to a loss in business opportunities” is valid or not. The research was conducted to gain
insight on government initiatives, bank support, and SME business impacts through a quantitative study which resulted in proving the null hypothesis are true, where SMEs in the United Arab Emirates are facing challenges with high-interest rates for business loans from banks as well as other financial institutions. The secondary aim of the study was to also gain insight into how SME professionals think about reaching out to digital platforms in order to acquire a business loan, where facilities such as crowdfunding could be available to raise capital. The results provided a unique understanding of SMEs’ strong interest in digital crowdfunding platforms to fulfill their lending needs
Comparative study of traditional and deep learning algorithms on social media using sentiment analysis
No full textIn recent years, there has been a significant increase in social media content. This study focuses on sentiment analysis of Twitter data. This research has been done to improve the accuracy of the sentiment analysis varying from various machine learning models to deep neural network models. Deep learning has recently demonstrated enormous success in the field of sentiment classification. The traditional models implemented include Logistic Regression, Naïve Bayes, Gradient Boost, Support vector machine and Random Forest and the deep learning models include CNN and RNN LSTM. A general-purpose deep learning ANN outperforms the traditional algorithms. The highest performance is achieved with the specialised LSTM deep learning classifier. The primary goal of this research is to highlight the capabilities of state-of-the-art deep learning architectures in building sentiment classifiers for Twitter data