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Investigating the Relationship between Climate Change and Crop Production in Europe
No full textThis report investigates the relationship between climate change and wheat production in Europe between 1961 and 2023, with the goal of building predictive models for use in agricultural planning and risk assessment. Historical climate and yield data was cleaned, prepared and analysed. Various Machine learning algorithms were tested including, Linear regression, Tree based methods, Support Vector Machine and Gradient Boosted Regression. GBR outperformed all other models giving the highest accuracy (R2=0.93). The results highlight the potential of ensemble methods for yield production. Future work will include looking into non-climatic variables to get a wider more well-rounded outlook
A literature review of the lived experiences of Chaplains in the Irish Prison Service
No full textThis qualitative research project explores the lived experiences of chaplains in the Irish Prison Service. It is a literature review of prison chaplaincy’s annual report using thematic analysis. The chosen reports provided a broad overview of the Irish prison service. They highlight the multifaceted and unique role that chaplains fulfil in the prison system as they advocate for prisoners, prison staff and families. The extensive support they offer includes rehabilitation, emotional, educational, spiritual and counselling. Their reports provide expansive insight into structural inadequacies within the prison system that is being consistently neglected. Key themes identified included engagement with prison staff, family engagement, bereavement, challenges of leaving prison and finally, social justice and endemic discrimination. The reports reflect how societal inequality is contributing to vulnerable demographics ending up in the prison system with limited alternative pathway and inadequate support systems when they leave
Evaluating the impact of ESG on firms' performance in financial sector : insights, challenges and path forward
No full textThis dissertation examines how adoption of Environmental, Social and Governance (ESG) can influence the financial and operational performance of small and medium-sized enterprises (SMEs) in the financial sectors of India and Ireland. Applying a pragmatic ideology and an explanatory sequential mixed-design method, the research used both quantitative analysis (as per SPSS) and qualitative analysis (as per NVIVO), in terms of thematic analysis. The results indicated that ESG adoption has a significant positive influence on brand reputation and operations effectiveness, which proved to be powerful determinants of financial success. However, the demand of the investors did not exhibit a statistically significant effect, indicating the difficulty that SMEs find in aligning to the demands of investors as a resource-constrained situation. The quantitative findings were supported by qualitative themes that confirmed the stakeholder power, difficulties in reporting and cultural obstacles and outlined the methods to be used to improve the situation. The research makes a theoretical contribution by generalizing Stakeholder Theory, RBV and Legitimacy Theory to the setting of SMEs and provides practical, policy and theoretical recommendations that can be used to drive healthy adoption of sustainable finance
What's the story with ADHD?: Exploring sociocultural conceptualisations and the treatment of ADHD
No full textThis study explores the sociocultural conceptualisations and treatment of ADHD, challenging its biomedical construction as a chronic neurodevelopmental disorder. Drawing on theoretical insights from Michel Foucault and Iain McGilchrist, it examines how psychiatric discourse has permeated social institutions. Section 1 critiques the authority of the DSM and its role in pathologising disruptive behaviour, positioning ADHD as an objectifying mechanism of social control. Section 2 examines the dominance of behavioural therapies, linking their rise to left-brain tendencies for control and standardisation, while exploring how right-brain-oriented approaches remain marginalised. Section 3 advocates for psychoanalytic and humanistic therapies that explore the meaning of one’s symptoms in relationship to their environment. This study advocates for a shift towards a more humanistic approach that moves beyond symptom-management. â€
Gen Zs and the Future of Digital Banking: Behaviours, Preferences, and Marketing Implications for Digital Banking in Canada.
No full textIn the aftermath of the Covid-19 epidemic, like most businesses, banking moved towards cashless payments to reduce physical interactions. While this presented a challenge to older customers, Generation Z have been brought up in a digital-first environment. This research examines the unique digital banking behaviours and preferences of Generation Z in Canada, focusing on individuals born between 1997 and 2001. This research assesses how well digital banking services align with Generation Z's expectations and outlines critical areas for improvement. Recommendations emphasize enhancing mobile platforms, maintaining security trust, offering culturally responsive features, and leveraging personal recommendations. The research underscores the need for financial institutions to refine their strategies continuously to meet the evolving demands of this digitally adept generation, which is at the forefront of transforming digital finance
From Rhythm to Recognition: “Leveraging Deep Learning and Machine Learning Models for Music Genre Classification”
No full textMusic powerfully reflects and shapes human culture, yet the task of classification of music into distinct genres is inherently challenging given the existence of stylistic overlaps, cultural specificity, and the subjectivity of listener interpretation. This research explores the evolving landscape of music genre classification through application of machine learning and deep learning techniques, with a specific focus on the implementation and performance evaluation of Convolutional Neural Networks (CNN), Support Vector Machine (SVM) and Random Forest (RF) algorithms. An extensive evaluation of 30 CNN architectures were carried out, along with 5 SVM and 5 RF model configurations using large datasets like FMA-large and MuMu datasets. Feature extraction techniques – particularly Mel-frequency Cepstral Coefficients (MFCCs) and Mel-spectrogram were determined to be highly significant in distinguishing the acoustic feature specific to each genre. The research addresses persisting challenges to genre classification, such as class imbalance issues, the emergence of hybrid genres, and questions of cultural representation. Through empirical testing and architectural improvements, the research determines strong model configurations that outperforms conventional approaches on genre recall and macro-average F1 score metrics. This research contributes to the development of genre classification systems that are not only technologically adept but also contextually through, offering important implications for applications in music streaming, recommendation systems, and global music archiving
Inflation prediction: An hybrid time series approach
No full textInflation forecasting is critical for effective economic planning and policy formulation. However, predicting inflation is challenging due to multiple external factors such as housing market trends and immigration. This study explores a hybrid modelling approach to enhance inflation prediction by combining the strengths of traditional statistical methods and advanced predictive techniques. Using time series data for Ireland and the United Kingdom, this research integrates Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) with other models, including Random Forest, Support Vector Regression, and Long-Short-Term Memory. Four hybrid models SARI-SVR, SARI-RF, RF-SVR and SARI-LSTM were developed and evaluated based on their ability to capture linear patterns and complex nonlinear relationships in the data. The findings demonstrate that the SARI-LSTM model consistently outperformed the others, achieving the lowest Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) for Ireland and the UK. This model’s ability to combine SARIMAX’s seasonal trend analysis with LSTM’s strength in handling sequential dependencies makes it particularly effective for inflation forecasting. By leveraging hybrid modelling, this study provides a comprehensive framework for addressing the complexities of inflation prediction. The results highlight the potential for improved forecasting accuracy, offering valuable insights for policymakers and economists
Examine the impact of organizational structural dimensions on employee turnover in finance sector
No full textEmployee turnover is a key concern in finance sector and organizational structure plays a vital role in employee retention decisions. This quantitative study examines how organizational structural dimensions particularly centralization, hierarchy levels, formalization, span of control and matrix structure impact on employee turnover intentions in finance sector. The theoretical framework covers from contingency theory and social exchange theory. The study reveals that contemporary finance organizations must prioritize on relationship quality dimensions of span of control, matrix structure and hierarchy levels over traditional mechanisms of centralization and formalization. The study provides policy makers with an evidence-based framework for structural risk assessment and targeted interventions to reduce voluntary turnover. The study has provided key recommendations including optimize spans through enhanced trainings and digital support, implementation of clear governance framework for matrix structures, manage the hierarchical levels in strategic manner while maintaining compliance and transforming formalization from restrictive to supportive mechanisms
Loan Approval Prediction Using Machine Learning: A Data-driven Approach to Binary Classification
No full textThis research analyzes the usage of machine learning techniques for predicting loan approval accuracy and fairness. The research employs data from real world credit risk datasets and SMOTENC to tackle the class imbalance problem in building a robust binary classification model. Such methods implemented include Logistic Regression and Random Forest algorithms, and evaluated through the metrics accuracy, F1 score, and AUC ROC. The data privacy and bias mitigation are prioritized. The findings offer a replicable framework for efficient and inclusive evaluation of credit risk, and therefore contribute to innovative finance decision making
Beyond the Diagnosis: Reframing Adult ADHD in a Psychotherapeutic and Sociocultural Context
No full textThis thesis interrogates how adult Attention-Deficit/Hyperactivity Disorder (ADHD) is defined, experienced, and treated within contemporary culture, with the aim of developing a relational, neurodiversity-informed, and neurodivergent-affirming psychotherapeutic framework. It explores how dominant narratives continue to pathologise attentional difference while obscuring the roles of stigma, shame, and emotional dysregulation. Drawing on academic literature, diagnostic manuals, memoirs, and podcasts, the study critiques deficit-based models and centres lived experience. The methodology integrates Foucauldian discourse analysis, Lacanian psychoanalysis, and perspectives from the neurodiversity paradigm to examine discursive power, affect, and identity. Findings highlight how ADHD is shaped by sociocultural forces, intersectional exclusions, and internalised norms around productivity and self-control. The study concludes by proposing inclusive clinical adaptations grounded in co-regulation, pacing, and mentalization, repositioning therapy away from behavioural correction and towards attuned, identity-affirming engagement and ethical responsiveness