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Challenges, ethnic networks, and the positive impact it has on the community
No full textIn the current era, globalization and migration have changed the economic and cultural landscapes of countries around the world, giving rise to new forms of immigrant entrepreneurship. The city of Dublin, Ireland, has experienced a significant increase in the number of immigrants, including the Mexican community. Despite the growing trend toward Mexican gastronomy and business, there is limited academic theory on this economic and social phenomenon. This research explores the phenomenon of Mexican migrant entrepreneurship in the restaurant and food business sector, analyzing their motivations, challenges, support networks, and community impact.
Based on qualitative and quantitative methods, this research used interviews and questionnaires with Mexican entrepreneurs in Dublin. The study is framed within the existing literature on immigrant entrepreneurship (Light & Dana, 2013; Portes & Manning, 2006; Zhou, 2004) and other authors, also incorporating critical perspectives that challenge traditional theories of ethnic entrepreneurship (Cooney & Godwin, 2021).
The findings reveal that Mexican entrepreneurial motivations go beyond economic necessity. Cultural identity, nostalgia for Mexican food, and an entrepreneurial spirit were the main motivators. Ethnic networks and transnational ties played a limited role in supporting these businesses. It was also confirmed that Mexican businesses have a positive impact on the local community through job creation, the use of Irish suppliers, cultural diversity, and social cohesion by promoting Mexican traditions. This research contributes to the field of entrepreneurship by offering a deeper understanding of Mexican businesses in Dublin and highlighting the need to expand current theoretical frameworks to include factors such as emotional, cultural, and identity-based motivations. It also offers practical recommendations for the benefit of entrepreneurs
An Integrated Hedonic Pricing and Predictive Modelling Approach: Comparing ML and DL for Dublin’s Rental Market
No full textDublin’s persistent housing crisis has been a critical issue for a long time. While prior research has primarily focused on the property sales market, the rental market also requires attention, especially as rocketing house prices have forced many residents to rely on rented accommodation. This study bridges this gap by proposing a model integrating hedonic regression with predictive modelling to analyse rental prices in Dublin. A key challenge was overcome by conducting web scraping to construct a detailed dataset encompassing core features summarised from the literature review. The dataset was further enhanced through spatial analysis by integrating proximity measures to key external amenities. The findings in this study revealed a significant rental burden issue within Dublin’s housing market, with the Rent-To-Income Ratio (RTR) across all household types exceeding the affordability threshold of 30%. Key features that influence rental prices were identified, including property types, building energy ratings (BER), number of bedrooms and bathrooms, and accessibility to neighbourhood and location facilities and amenities. Among Random Forest, SVR, XGBoost, LightGBM, GBR, and RNNs models, LightGBM was found to achieve the optimised predictive accuracy, with an R2 of 0.79, MSE of 106189.96, MAE of 234.97, RMSE of 325.87, %RMSE of 13.32%, and %MAE of 34.61%
Serverless AI: Leveraging Cloud Functions For GPU-Optimized Machine Learning Deployment and Comparative Analysis with Traditional Methods
No full textThe fusion of serverless computing and GPU-accelerated machine learning (ML) marks a new approach in AI deployment at the cloud level. Proposed strategies incorporating virtual machines or containers seem to struggle with scaling, cost efficiency, management overhead, and infrastructure. This work analyzes the hybrid serverless architecture based on AWS Lambda and API Gateway for managing inference workloads using GPU-backed EC2 instances. The focus of the research is on two machine learning models - cyberbullying detection model with ML algorithms and an object detection model - YOLOv8, monitoring latency, resource consumption, processing time, and cost. The findings illustrate that lightweight models perform consistently under serverless configurations, but heavy workload models take advantage of dynamic offload to GPU-backed EC2 instances. In addition to facing latency spikes and inconsistent resource utilization, delay-sensitive computing GPU models exhibit tremendous resource needs. Nevertheless, the hybrid model achieves some balance of diminishing returns with performance and cost. The study illustrates the capability of such serverless architectures to configure with lower responsiveness for modern AI workloads, increasing the potential to enable these approaches with suitable requirements
The social must be stabilised: How are the social needs of young people with social work involvement characterized in their mental health case notes?
No full textIn Donzelot's landmark The Policing of Families, he traced the rise of the “social” sector in the 18th century, where institutions like social work, education, and healthcare regulated families, shaping norms of deviance to justify intervention. Social scientists continue to debate the impact of post-2008 austerity measures on the relationship between the social sector and family life in contemporary society. This study aims to contribute to these discussions through a critical discourse analysis of how the social needs of 70 young people with social work involvement have been characterised in their Child and Adolescent Mental Health Service case notes. This analysis was co-produced alongside three experts-by-experience with lived experience of both mental health and social care. Results of this analysis indicate that the social needs of our sample were a) rejected from mental health services for being too social, too chaotic and lacking a stable base; b) accepted but secondary to psychological concerns c) outsourced to other services or to families or young people themselves. Where young people's social needs were sufficiently high risk in the community they were d) contained in mental health facilities or under deprivation of liberty orders by social services. We contend that in the contemporary context, rather than the social comprising an ever-expanding entity designed to govern the conduct of family life, we identified ways in which the social sector was also governing through neglect and containment. This analysis offers important insights into inequalities faced by young people with social care involvement who seek mental health support
SecureMedZK - A Blockchain-based Approach with Zero-Knowledge Rollups for Secure EHR
No full textThis paper aims to develop SecureMedZK, a blockchain-based framework designed to secure Electronic Health Records (EHRs) using quantum-resistant encryption, privacy-preserving techniques, and HIPAA-compliant data management. The study integrates NTRU lattice-based cryptography, Zero-Knowledge Rollups, and Practical Byzantine Fault Tolerance (PBFT) to address scalability, security, and consensus issues. Performance evaluations were conducted through simulated environments, comparing SecureMedZK with traditional EHR systems on metrics such as scalability, latency, and security. The findings show that SecureMedZK significantly improves scalability by reducing on-chain transaction loads with Zero-Knowledge Rollups, resulting in a 30% improvement in transaction speed. PBFT ensures minimal delays even with increasing transaction volumes, while NTRU lattice-based cryptography offers quantum-resistant encryption, safeguarding EHRs against quantum threats. The system meets HIPAA standards with encrypted data storage, robust access controls, and immutable audit trails, proving its ability to provide secure and compliant healthcare data management. SecureMedZK has the potential for expanding secure healthcare data management, including genomic and imaging data, with future improvements in cryptographic techniques and consensus mechanisms
Study protocol for an observational research study with an embedded N-of-1 design: Increasing the availability of goal-oriented cognitive rehabilitation for people living with dementia in Ireland
No full textBackground: Goal-Oriented Cognitive Rehabilitation (GREAT-CR) is an evidence-based early intervention for people with mild-moderate dementia that improves goal attainment and functional outcomes. In Ireland, this intervention is not widely available, likely due to resource shortages within memory services. This project aims to pilot and evaluate a new service delivery model for GREAT-CR, and to assess its impact on goal attainment, cognition, and quality of life for people living with dementia.
Methods: This pilot study implements a new service-delivery model where trainee psychologists will offer GREAT-CR within Memory Clinics in Ireland. Sub-study 1 will evaluate the impact of GREAT-CR in this context and sub-study 2 will evaluate the implementation model. Sub-study 1 will employ a quantitative pre-post design with an embedded randomized N-of-1 experimental design. Outcomes will include goal attainment, quality of life, and cognitive function. A subset of three participants will be randomly selected for an N-of-1, single-case experimental design evaluation. The N-of-1 design will include within-case and start-point randomization. Sub-study 2 will include a mixed-methods approach to evaluate the feasibility, acceptability, appropriateness, and potential sustainability of the implementation model at organizational, practitioner, and service-user levels. We will conduct semi-structured interviews with key stakeholders and analyze the data using framework analysis. Quantitative implementation outcomes will be assessed against predefined targets to support qualitative findings.
Discussion: The results will inform guidelines for wider service delivery. This project aims to ensure immediate and long-term impacts for participants with early-stage dementia and their carers by increasing opportunities to access evidence-based psychosocial interventions; for trainee psychologists by creating novel placement opportunities; and for service providers by disseminating GREAT-CR implementation recommendations. By addressing current staffing and resource limitations, this model has the potential to enhance the availability of GREAT-CR for dementia patients in Ireland
Constructing a Mothers’ Culture: Affective Bargains in Branding Discourses
No full textThe fantasy of traditional motherhood, according to which women find fulfilment through family care, still dominates market texts. However, there is increasing acknowledgement of motherhood’s difficulties, often in the very same texts. This tension is not immediately understandable; neither are its political and economic implications. We propose an explanation by deploying a Berlantian framework to analyse the new media presence of five brands targeting UK mothers. We identify a mothers’ culture: a market where motherhood’s difficulties are organised incessantly. We argue that mothers’ culture simultaneously validates traditional motherhood’s power to achieve the good life and provides therapeutic explanations and tactics to retain optimism when its lived experiences become too far removed from the fantasy of that good life. Combined, these mechanisms absorb the difficulties of traditional motherhood in a way that protects its promises of fulfilment. This prevents a questioning of the system of norms that effectively blocks this fulfilment
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