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The Complexities of Acculturation and Discrimination of Immigrants in Ireland
No full textIntroduction: The increasing number of multi-cultured immigrants reaching the Republic of Ireland, suggests notable psychological adaptations for the Irish-arrived immigrants, in relation to their ability to acculturate and assimilate in Irish society. This research study explores the relationship between perceived discriminatory experiences and the processes of assimilation and acculturation among Irish immigrants, considering demographic variables (age, gender, ethnicity).
Method: This study employs a quantitative, cross-sectional, within-subjects design. A number of 82 participants, have been administered three measured scales, 1) the Acculturation Attitude Scale (AAS), (2) Vancouver Index of Acculturation (VIA), and lastly, (3) Day-to-Day and Major Events Discrimination Scale (EDS) & (MEDS); alongside recording the demographical factors of each individual (age, gender, and ethnicity).
Results: The results administered non-significant results regarding all hypotheses identified; two correlational analyses, simple linear regression analyses for each variable, and lastly, two multiple regression analyses.
Conclusion: The findings of this study determined non-significant relationship between perceived discrimination and assimilation/acculturation in Irish immigrants. Therefore, suggesting that other factors beyond discrimination influence assimilative and acculturative attitudes among Irish immigrants. Further research is recommended to explore these potential influencing factors in greater depth
Securing IoT System Using ML Models
No full textThe security mechanisms for Internet of Things systems often rely on traditional rule-based detection systems alone to detect sophisticated threats like distributed denial of service, which sometimes fall inadequate in terms of adaptability required in modern-day detection. This research tries to bridge the gap between the academic researches and practical applications by contributing a scalable and robust detection framework in modern cloud infrastructure. The research aims to design and implement a real-time hybrid detection mechanism on a cloud platform—Amazon Web Services that would integrate the security information and event management, intrusion detection system (IDS), and machine learning models to detect and classify the cyber threats efficiently. Attack simulations were conducted to generate real-time logs which were monitored through the Suricata IDS, followed by the log processing in ELK (Elasticsearch, Logstash, and Kibana) stack. Ensemble learning models like Random Forest and XGBoost were deployed to complement the rule-based detections and all this was presented in visual forms in real time without significant delays. This was proven by an average detection time of 0.514 milliseconds, demonstrating the system’s suitability for real-world conditions. The framework tends to bridge the gap between conceptual and practical deployments with the implementation of real-time hybrid detection system in a cloud environment
Federated Learning and Edge Computing for Latency Reduction in Smart Farming IoT Sensor Data Filtering
No full textThe integration of Federated Learning and Edge Computing has emerged as a promising approach to handle latency, security, and bandwidth limitations in IoT sensor networks for smart farming. Traditional cloud-based models are usually afflicted with high communication costs and privacy concerns, hence proven to be inefficient for real-time agricultural applications. FL enables decentralized machine learning, thus allowing models to be trained right on IoT devices without requiring any raw data centralization, hence preserving data privacy and reducing transmission overhead. On the other hand, Edge Computing enhances local processing of data for reduced latency, reduced dependency on cloud infrastructure for quicker decision-making. However, despite advantages in both, certain key challenges like heterogeneous data processing, communication overhead, resource constraints, and security vulnerabilities remain concerning. This paper reviews the related literature with regard to the existing FL, Edge Computing, and IoT data filtering techniques. It outlines the critical research gap on the scalability in large-scale farms, energy-efficient learning models, and secure FL. The study explores enhancing the privacy, efficiency, and real-world deployment issues of FL techniques within an agricultural IoT system. This research hence tries to bridge the gaps with respect to the optimization of FL in smart farming for reducing latency by filtering data in real time for decision-making with increased security concerning IoT-driven agricultural systems
he relationship between physiological factors and lifestyle choices and their impact on social anxiety
No full textAims: The current study aimed to observe the relationship between physiological factors and lifestyle choices and their impact on social anxiety (SA). The study then aimed to observe this relationship when controlling for gender posing as another research question.
Methods: a survey was given to the participants (N=90) as the link was provided through social media platforms such as Instagram and Facebook. The survey consisted of 5 scales measuring SA (The Liebowitz Social Anxiety Scale), sleep (The Sleep Quality Scale), alcohol (Scale of the Measurements of Attitudes Towards Alcohol), caffeine (Motives for Caffeine Consumption Questionnaire), and physical Activity (European Health Information Survey-Physical Activity Questionnaire).
Results: results showed that poor sleep quality was associated with higher rates of SA in men and women. Additionally, stronger alcohol relationships along with high SA levels were reported for men, but not women. Caffeine motives and physical activity levels were found to hold an insignificant relationship with SA.
Conclusion: these findings add to the literature by using a multivariate approach and provide an argument for poor sleep quality and high alcohol relationship correlating with high SA and high caffeine motives and physical activity sharing no relationship. Future findings should incorporate a longitudinal study design using staged scenarios to give a more extensive evaluation
Big 5 Personalities Impact on Clusters of Symptoms of Depression
No full textThe present study aims to examine the association between specific big 5 personality traits and specific symptoms of depression. Previous research has shown to be inconclusive and only focuses on the broad label of depression, not the symptoms of it. A questionnaire was completed by participants (n=112), which questioned gender, age, personality scores on BFI-20 and depression symptoms in clusters (Affective, Somatic, Internalizing and Sensorimotor) on the PHQ-9. Results were not statistically significant for Affective, but Conscientiousness negatively predicted Somatic, Internalizing and Sensorimotor, Neuroticism positively predicted Somatic and Internalizing, and Open Mindedness positively predicted Internalizing, with Extraversion and Agreeableness not having an effect on any of the clusters. These findings help support certain preexisting findings, namely on Conscientiousness’s effect on depression, while being critical of other, namely the lack of effect by Extraversion. This may provide a better understanding of the causes of specific symptoms of depression as well as a new avenue of research for treatment via therapeutic personality change
Artificial Intelligence Literacy and Attitudes: Relations to Age, Gender and Education
No full textBackground: Artificial intelligence (AI) literacy, AI attitudes and the interaction(s) between the two are not properly understood. With widespread AI use, this presents a number of potential problems for researchers and users of AI.
Aims: This study aimed to explore the interaction of age, gender, years of education and AI attitudes with AI literacy.
Methodology: A cross-sectional observational design was used. AI literacy was measured with the 32 item Artificial Intelligence Literacy Questionnaire (AILQ) and AI attitudes were measured with the 20 item General Attitudes towards Artificial Intelligence Scale (GAAIS) which consisted of 2 subscales. The sample consisted of 88 participants.
Statistical analysis: Statistical analysis was performed using IBM SPSS version 29.0.
Results: Only AI attitudes were significantly correlated with AI literacy, AI literacy scores were higher among males than females and lastly the only significant predictors of AI literacy was AI attitudes.
Conclusion: Positive AI attitudes are among the factors that can contribute towards a greater ability to use and evaluate AI which are vital skills in current times, so should be promoted conditionally
The Impact Of Transformational Leadership On Employee Job Satisfaction In Ireland’s IT Sector
No full textThis study seeks to examine the impact of transformational leadership on employee job satisfaction within the Irish IT sector, with a specific focus on how employees perceive the core dimensions of transformational leadership. As organisations continue to navigate rapid technological change and workforce expectations, effective leadership becomes increasingly critical in shaping employee outcomes. A quantitative research approach was employed, using a structured questionnaire distributed to 50 employees across five IT organisations in Ireland. The survey measured employees’ perceptions of transformational leadership behaviours and their levels of job satisfaction. The study adopted a positivist research philosophy and utilised descriptive and inferential statistical techniques to interpret the data. The findings indicate that employees generally held moderately positive views of transformational leadership behaviours of their leaders. Transformational leadership was found to positively impact key aspects of job satisfaction, though no significant effect was observed on collaboration and teamwork, and work-life balance and wellbeing. All core dimensions of transformational leadership were recognised by the respondents to be present in their organisations although with some variations amongst them. These findings contribute to the growing body of literature on leadership effectiveness in dynamic and innovation-driven sectors like the IT sector. It also highlights the need for IT organisations to strengthen transformational leadership practices particularly in areas that enhance employee engagement and satisfaction, by investing in leadership development programmes. Future research should consider exploring mediating variables such as organisational culture, team dynamics, employee demographics or generational differences to gain deeper insight into the leadership-job satisfaction relationship
Artificial Intelligence In Human Resource Management: Applications, Benefits And Ethical Implications
No full textThe development of Artificial Intelligence (AI) is rapidly changing the face of Human Resource Management (HRM), which is how organizations can acquire, manage, and keep employees. This research proposal proposes the integration of AI technologies in HRM to examine its applications, benefits, and challenges to identify research gaps for future development. These technologies include machine learning algorithms, natural language processing, and predictive analytics employed in the recruitment process, talent management, employee engagement, and workforce planning. These technologies improve efficiency by performing tasks like sorting through resumes and managing payroll, and help in enhancing decision-making through real-time data analysis.
This study looks at how AI-based tools help in enhancing the employee experience, in improving the process of performance appraisal, and in supporting data-driven decisions on workforce management. Additionally, it will attempt to establish how AI can reduce bias in hiring and improve diversity in the workplace. This study looks closely at the ethical issues and practical challenges that come with using AI in HRM. It highlights the need for fair algorithms, responsible handling of employee data, strong AI security measures, and strategies to manage the risk of job displacement. The goal is to make sure AI is used in a way that is transparent and accountable. The research follows a mixed-methods approach, combining case studies, surveys, and interviews with experts to explore how AI is being applied in HRM across different industries.
The insights aim to give clear and useful guidance to HR professionals, policymakers, and organisations on how to benefit from AI while staying true to strong ethical standards. By examining AI’s role in HRM, this study seeks to help build organisations that are agile, inclusive, and innovative—while also keeping people at the heart of their operations to boost both performance and employee satisfaction
Workplace Well-being: Investigating workplace health and well-being initiatives and their impact on the retention and engagement of graduate employees in Ireland
No full textThis dissertation will investigate employee health and well-being initiatives and their impact on graduates’ retention and engagement levels within the workplace. It examines and analyses how these initiatives influence graduate employees in an Irish context. By synthesising research and gathering primary data surrounding this topic, through the use of semi-structured interviews, the researcher aimed to answer the research question effectively.
The findings emerged that it is the health and well-being initiatives that focus on increasing social well-being and workplace connections that has the most influence on employee engagement levels among the graduate employees. Additionally, the research found that while formal well-being initiatives are valued, the two factors that have the most significant impact on graduate employees’ retention is flexibility and company culture
The study's significance lies in its ability to inform and influence employers, universities, graduate employees, and graduate programmes, ultimately improving the organisations and employee’s performance and engagement. Additionally, the findings will contribute valuable insights to the field of human resource management, guiding future research and practical applications aimed at supporting employees during the early stages of their careers
Exploring Global Confectionery Marketing STRATEGIES - especially Packaging and Labelling - and Applying those Insights to strengthen Indian Local Confectionary Brands
No full textIntroduction: Research has been conducted on marketing strategies of global confectionary brands so that valuable insights on packaging and labelling aspects can be obtained. Four research objectives have been identified. Based on research objectives, a literature review has been conducted to consult scholarly insights on marketing strategies in the food industry.
Methodology: A primary qualitative method has been followed in this research. 10 professionals from managerial or proprietorship levels have been selection for interview. 16 context-based questions have been asked to collect responses for data analysis. Selected participants have adequate work experience in small, medium and large-sized enterprises in the Indian local confectionery industry. Based on their responses, open code and axial code analysis have been conducted for analysis.
Findings: Interpretation of primary findings has been based on responses of interview participants. Tree diagrams have been formed for open codes and axial codes. It has been found that emotional storytelling through packaging helped to improve packaging ROI. Moreover, technology adoption such as inclusion of QR codes to provide product information has been followed. Indian brands are recommended not to blindly follow global packaging strategies and ensure adaptive selection for maximum outcomes in the Indian local confectionery industry