8333 research outputs found
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Machine Learning-Aided Supply Chain Analysis of Waste Management Systems: System Optimization for Sustainable Production
No full textElectronic-waste (e-waste) management is a key challenge in engineering smart cities due to its rapid accumulation, complex composition, sparse data availability, and significant environmental and economic impacts. This study employs a bespoke machine learning infrastructure on an Indian e-waste supply chain network (SCN) focusing on the three pillars of sustainability—environmental, economic, and social. The economic resilience of the SCN is investigated against external perturbations, like market fluctuations or policy changes, by analyzing six stochastically perturbed modules, generated from the optimal point of the original dataset using Monte Carlo Simulation (MCS). In the process, MCS is demonstrated as a powerful technique to deal with sparse statistics in SCN modeling. The perturbed model is then analyzed to uncover “hidden” non-linear relationships between key variables and their sensitivity in dictating economic arbitrage. Two complementary ensemble-based approaches have been used—Feedforward Neural Network (FNN) model and Random Forest (RF) model. While FNN excels in regressing the model performance against the industry-specified target, RF is better in dealing with feature engineering and dimensional reduction, thus identifying the most influential variables. Our results demonstrate that the FNN model is a superior predictor of arbitrage conditions compared to the RF model. The tangible deliverable is a data-driven toolkit for smart engineering solutions to ensure sustainable e-waste management
Investigating the Experiences of Burnout among Healthcare Professionals
No full textBurnout is a major health challenge among healthcare professionals in Ireland. Given its prevalence, this qualitative study investigated the experiences of burnout among healthcare professionals in Ireland, focusing on healthcare assistants based on the gap in previous literature. Utilizing a qualitative methodology, six healthcare assistants were interviewed through semi-structured sessions, allowing for in-depth examination of their experiences. Thematic analysis identified four primary themes: emotional exhaustion, workplace environment, external pressures, and work-life balance. Key findings indicated that prolonged work durations, insufficient rest, and workplace toxicity significantly contributed to burnout, while self-care practices and professional support systems were vital for resilience. Drawing on the study findings, recommendations were made for healthcare organizations to implement policies that foster a supportive work environment and enhance staff well-being within the healthcare sector
Detecting and Mitigating AWS-Specific Code Smells in Ansible Infrastructure as Code
No full textThis research examines the vital problem of code smells in Ansible infrastructure-as-code (IaC) scripts specifically for AWS deployment scenarios. The research uses previously studied assessment methods in IaC quality assessment to create a new three-tiered framework for code smell detection and mitigation that integrates static analysis, program dependence graph (PDG) analysis and deep learning (DL) approaches. The method required developing multiple functional Ansible playbooks targeting AWS infrastructure deployment tasks followed by code smell pattern identification and the establishment of an advanced detection solution. The PDG analysis enables the framework to detect variable dependencies, and code smells and utilizes Multi-layer Perceptron (MLP) neural networks to recognize contextual code smells. The framework categorizes code smells into six primary types (UR1, UR2, UO1, UO2, HP1, HP2) plus security vulnerabilities, all of which affect AWS infrastructure code quality and security. Experimental evaluation included testing the framework using open-source Ansible repositories and creating test playbooks focused on AWS deployment with different roles. The unique patterns of smells found in AWS-specific infrastructure code extend previous findings and hard-coded credentials and improper error handling appear most frequently. The combined PDG-MLP detection produced higher accuracy at spotting code smells compared to traditional static analysis. The research presents both theoretical AWS code smell knowledge about Ansible infrastructure along with practical tools for practitioners including a detection optimizer and solution database
Predictors of Quality of Life among Single Mothers and Single Fathers
No full textObjectives: A growing number of studies suggest that loneliness and self-stigma are important predictors of well-being and quality of life (QOL) among single parents. However, much of this research has focused primarily on single mothers and less is understood about how these factors impact single fathers. The overall aim of this research is to examine if loneliness and self-stigma predict QOL among single parents and identify if there are gender differences.
Method: A quantitative approach using an anonymous online questionnaire through Microsoft forms recruited and analysed 93 single parents (70 women and 23 men) examining the loneliness, self-stigma and QOL.
Results: As feelings of loneliness increase, QOL decreases. Furthermore, gender differences are evident with single parent men reporting lower loneliness and self-stigma scores when compared to single mothers (p<.001). Additionally, the QOL levels among single fathers were significantly higher than single mothers indicating that single mothers had higher feelings of loneliness and self-stigma resulting in lower QOL levels.
Conclusion: The present study supports previous findings and extends these to include gender differences in loneliness, self-stigma, and QOL. Future research should examine these variables in larger, population representative samples, and include qualitative research to further examine individual beliefs and experiences surrounding single parenthood
The Impact of Family Dynamics on Emotional Resilience and Self-Esteem in Adults
No full textThe current study examined the impact of family dynamics, specifically family functioning and family structure, on emotional resilience and self-esteem in adults. While previous research has established the influence of family dynamics on psychological well-being, much of it has focused on children, often overlooking the long-term effects on adults. The present study aimed to address this gap in literature by focusing on adults, with participants recruited through snowball sampling and convenience sampling (N = 141). Findings from Spearman’s rho correlation coefficient revealed that positive family functioning is associated with greater self-esteem and emotional resilience. Findings from Kruskal Wallis test revealed kinship family structure reported the highest levels of self-esteem and emotional resilience. Findings provide a greater understanding of the complexity of family dynamics, emphasising the importance of a cohesive family environment in promoting emotional resilience and self-esteem across the lifespan. Implications of these findings suggest mental health professionals develop programmes focusing on improving family function as well as the promotion of parental education
The Impact of Alcohol Use on Anxiety, Quality of Life, and Perceptions of Others’ Well-Being
No full textAims: To examine the impact of alcohol use on anxiety, quality of life (QoL), and perceptions of others’ well-being.
Methods: A questionnaire was given to participants (n=101) through social media which consisted of questions from the Alcohol Use Disorders Identification Test (AUDIT), Generalised Anxiety Disorder Scale (GAD-7), an adapted version of the Quality of Life Scale (WHOQOL-BREF), and a custom-developed Perceived Alcohol Use of Others’ Scale (PAUO).
Results: Higher levels of alcohol consumption is associated with higher problematic alcohol use (AUDIT scores). Higher levels of alcohol use is also associated with lower levels of anxiety. However, no significant relationship was found between alcohol use, quality of life and perceptions of others’ alcohol related wellbeing. There was also a significant relationship found between alcohol use, gender and age; females tend to drink more than males and older individuals consume more alcohol than younger people.
Conclusions: Higher alcohol use is linked to lower anxiety but not to quality of life or perceptions of others' well-being. Gender and age differences highlight areas for further research
Examining The Relationship Between Employment and Stress among Irish and International Third-Level Students
No full textResearch on stress levels in the third-level population has demonstrated inconsistent findings, particularly when comparing between domestic and international students. This study aimed to examine the relationship between employment status (employed vs. unemployed), nationality (Irish vs. international students), and stress levels among third-level students in Ireland. The purpose of this study was to investigate the interaction and main effects of employment and nationality on stress, as well as the relationship between financial stress and perceived stress. A cross-sectional, between-groups design was employed in the present study. A total of 110 participants were recruited through social media and completed an online questionnaire. The survey included demographic questions, a single-item self-developed 10-point Likert scale measuring financial stress, and the Perceived Stress Scale (PSS). Contrary to expectations, results showed no significant interaction or main effects of employment status and nationality on stress levels. However, a significant moderate positive correlation was found between financial stress and perceived stress (rs(108) = .30, p <.001). Future research should repeat this study with a larger sample size to confirm and validate these findings. The result of the current study contributes to a growing body of research that has the potential to inform future policies
The Impact of Psychological Capital (PsyCap) on Job Performance in Hospitality Workers in Dublin, Ireland
No full textThe primary aim of this dissertation is to explore the role of Psychological Capital (PsyCap), comprising hope, self-efficacy, resilience, and optimism, in influencing job performance within Dublin’s hospitality sector. Given the demanding and often unpredictable nature of the industry, particularly in the wake of the COVID 19 pandemic, this research focuses on how positive psychological traits support hospitality employees in navigating everyday workplace challenges. To examine this, a qualitative research design was adopted using semi structured interviews with staff working in a range of roles across hotels, restaurants, and other hospitality settings in Dublin.
The findings reveal that PsyCap plays a significant role in enhancing performance by helping employees recover from setbacks, maintain belief in their own capabilities, and remain motivated under pressure. Among the four PsyCap components, resilience and self-efficacy were especially prominent, enabling individuals to manage stress, adapt to changing demands, and sustain their performance. In addition, hope and optimism were consistently linked to higher motivation, future focused thinking, and a more positive outlook on work, even in high pressure situations.
Overall, this study demonstrates the practical value of PsyCap in the hospitality industry, not only as a personal psychological asset for employees but also as a strategic tool that managers and HR practitioners could develop to strengthen workplace wellbeing and improve overall performance. The insights generated here provide a foundation for further research and suggest that investing in PsyCap could be a meaningful step towards addressing long standing human resource challenges in hospitality settings
Effective Recruitment and Retention Strategies for High-Performing Talent
No full textEmployee turnover is a significant challenge for organisations worldwide, with high costs in recruitment, training, and lost productivity. In Ireland, small and medium-sized enterprises (SMEs) face particular difficulties retaining high-performing employees due to limited resources and the absence of tailored retention strategies. This study examines the main factors contributing to turnover among high performers in Irish SMEs and explores best practices for recruitment and retention.
A mixed-methods approach was used, combining a review of existing literature with primary data collection. The research investigates the link between employee engagement and turnover, the specific expectations of high performers, and the constraints SMEs face in meeting these needs.
Findings highlight that high performers value career development, continuous learning, recognition, and flexibility. SMEs that implement personalised retention initiatives—such as mentorship programmes, performance-based rewards, and flexible working arrangements—can improve engagement and reduce turnover. The study offers practical recommendations to help Irish SMEs attract and retain top talent, strengthening both performance and long-term competitiveness
Investigation on how does the integration of AI in IT recruitment impact the efficiency and decision-making processes of recruiters in the IT industry
No full textThe dissertation explores how AI-powered tools are changing efficiency, quality of decision-making, and professional practice in the area of IT recruitment. Using twelve semi-structured interviews covering corporate, agency, and operational functions, the research uses reflexive thematic analysis to uncover repeated themes taking into account the positionality of the researcher. The structure includes strong ethical and governance safeguards, including GDPR compliant consent, anonymity measures, and auditability provisions.
The findings identify six interrelated themes: clear improvements in efficiency; a paradigm of human-artificial intelligence collaboration; technical and ethical limitations persisting; the evolution of recruiter role and skills needed; tensions around candidate experience and fairness; and the need for rigorous governance. Participants reported screen and source time reductions of 40% to 75% and noted issues of mis-ranking, lack of explainability, and maternity break or non-linear career path bias, highlighting the need for continued monitoring and human oversight.
Suggestions include accredited AI literacy pathways, bias audits in contracts, clear policies on "AI use," layered candidate transparency, outcome-balanced scorecards, and steering committees. The study enhances augmentation and sociotechnical theories and provides direction for leaders and regulators. The study's limitations encompass its sector-specific focus and qualitative scope; subsequent mixed-methods research should evaluate causality and transferability across various contexts