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Impact of Grief on Immigrants in Ireland
No full textBackground: Grief is universal to all humans, yet its coping style and expression vary across societies and cultures. Immigrants have unique grieving issues due to displacement, cultural differences, and limited social support. Migration and bereavement converge to result in complex emotional, social, and psychological issues that must be studied in depth.
Aims and objectives: The principal objective of this research is to establish the impact of grief on immigrants in Ireland. It aims to examine personal grief experiences among immigrants and analyze the social, emotional, and psychological impact of grief.
Methodology: The study uses semi-structured interviews, anchored on a qualitative technique that allows for an in-depth knowledge of participants' experiences and perspectives.
Results and Conclusions: The study's results shed light on how difficult it was for the immigrants to deal with their losses, as they were far from home and could not fully rely on video calls. The findings from this research conclude that the immigrants were dealing with the losses of close loved ones, and it was heartbreaking that they could not pay their last respects to them. The inability to travel back home to be with their loved ones sparked a lot of guilt.
Recommendations: Policymakers can step in to establish local immigrant support groups. These may include peer networking and social network support groups where grieving immigrants can seek support when struggling with grief. Future research should focus on analysing the impact of social networks on immigrant grief experiences
Sleep quality within students: Does sleep quality predict academic performance and mental wellbeing in students?
No full textResearch on sleep has proven that poor sleep quality leads to negative consequences academically, physiologically, and psychologically. Drawing from previous research surrounding sleep quality, academic performance and mental well-being this study aimed to expand on previous literature by exploring underrepresented demographic factors. The hypothesis presented that poor sleep quality would lead to poor academic performance and mental distress while attributing to demographic factors. Participants were recruited through social media using a snowball sampling technique (N=112) and completed an anonymous online survey containing demographic questions, The Pittsburgh Sleep Quality Index Scale (PSQI), Academic Performance Questionnaire (APQ), and the Depression, Anxiety, Stress Scale (DASS21). Results showed that age did not affect sleep quality, women reported poorer sleep quality and increased mental distress compared than men, however, these findings do not support the hypothesis that these results affect academic performance amongst female participants. And that Irish participants report poorer academic performance than non-Irish participants, despite their being no relationship between Irish participants and poor mental well-being compared to non-Irish participants. The results of the study suggest that a controlled sample may be a more effective approach when understanding the effects of demographic factors on sleep quality
The 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
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 Role of Social Support in Mental Health Outcomes for Black Adults in Ireland
No full textThis study investigates the connection between Black adults' mental health outcomes, specifically, stress, anxiety, and depression and their perceptions of social support in Ireland. 89 participants in a cross-sectional, quantitative survey design filled out standardised questionnaires measuring psychological distress, family functioning, socioeconomic status (SES), perceived social support, and attitudes towards getting psychological help. Higher perceived social support was substantially linked to lower levels of anxiety and depression, according to hierarchical regressions. None of the three suggested moderators SES, family functioning, or help-seeking attitudes significantly influenced the association between support and distress, but socioeconomic status and family functioning also independently predicted mental health outcomes. These results imply that socioeconomic factors and perceived social support both have additive effects on mental health, but not an interactive one. The results highlight the importance of addressing both individual and structural contributors to wellbeing in Black communities and highlight the need for culturally relevant mental health interventions within the Irish context
Beyond Star Ratings: Comparison of Sentiment‑Driven Deep Learning Models for Play-Store Game Recommendations
No full textUser-generated reviews on app marketplace like google play store always presents a major challenge for assessing user sentiment and rating prediction. Game app recommendation systems provide substantial advantages to mobile users alongside developers. Mobile games form a substantial part of app stores yet users struggle to find suitable games because of the numerous available options. An efficient recommendation system solves this challenge through customized game recommendations that boost user engagement and satisfaction. The recommendation system generates longer playtime and increased game downloads that help developers reach their success goals. This study addresses the need for sentiment and rating prediction by evaluating four state‑of‑the‑art, sentiment‐enhanced deep‑learning architectures a GloVe‐embedded LSTM with attention, a DistilBERT‑based Transformer, a hybrid GRU–CNN model, and a DistilGPT2 Transformer on a large corpus of Play Store reviews. We trained and tested each model on 100K reviews using a consistent preprocessing pipeline (tokenization, padding, user–app embeddings) and optimized hyperparameters via randomized search. The hybrid GRU–CNN model delivers the lowest test MSE of 0.039 alongside MAE of 0.124 to outperform all other models including both LSTM‐ and Transformer‐only approaches while GloVe+LSTM+attention reaches the highest Recall@10 level of 0.8440
Advanced Strategies for Enhancing Tesla Stock Price Prediction
No full textIn this paper, a deep learning hybrid model was introduced utilizing both historical market data and sentiment signals collected from social media to predict Tesla stock prices. The architecture involved a combination of Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and Transformer encoders for extracting local, temporal, and global contextual features from the multivariate timeseries data. VADER sentiment analyser was employed to extract sentiment features from Kaggle datasets of Tesla posts from Twitter and Reddit before processing. The sentiment scores were then accumulated on a daily basis, and combined with technical indicators (such as Open, High, Low, Close and Volume) to form 15-day lookback sequences to predict next day stock prices. The models were trained with MSE and scored with MAE. Individual CNN-BiLSTM and Transformer models were developed, producing MSEs of 0.00318 and 0.02180 while MAEs were 0.04498 and 0.12434 respectively. Based on the insights gained from these results, a final hybrid model was formulated and optimized using Bayesian hyperparameter tuning. The combined model achieved the lowest MAE of 0.04486, MSE of 0.00324 and validation loss of 0.00323 outperforming both baseline models. Sentiment data were integrated through an attention mechanism, which was observed to enhance predictive accuracy. SHAP and LIME were used to interpret the model’s predictions, producing explainable, sentiment aware forecasts for data-driven decision-making
Deep learning models for the prediction of earthquake magnitudes
No full textEarthquake prediction is a critical yet complex task due to the stochastic nature of seismic activities and the multitude of factors influencing tectonic movements. This study aims to leverage advanced machine learning and deep learning techniques to predict earthquake magnitudes based on spatial, temporal, and geophysical parameters. Multiple baseline models were implemented and evaluated, including Random Forest Regressor, Feedforward Neural Network, SVR, Extra Trees Regressor, and Gradient Boosting Regressor. Hyperparameter tuning using RandomizedSearchCV was employed. As part of the research, a number of deep learning architectures were studied in detail, including ones with GRU, Conv1D, Bidirectional LSTM, a hybrid Bidirectional GRU + Conv1D layers. Performance was assessed using MAE, MSE, RMSE, and R2 score. Over the course of this document, it will be shown that Gradient Boosting Regressor emerged as the best-performing model with the lowest MAE (0.2817), lowest MSE (0.1514), lowest RMSE (0.3891), and the highest R2 Score (0.1734), demonstrating its robustness and reliability. The work did not find comparable results with any deep learning technique. However, this study underscores the potential of ensemble learning models, particularly Gradient Boosting, instead of much-hyped deep learning techniques
The effect of pornography use and sex education on rape myth acceptance
No full textResearch investigating possible contributors to rape myth acceptance (RMA) has found that pornography use and sex education may have a significant impact on levels of RMA. The current study aimed to expand upon this topic by investigating how the interaction between pornography use and sex education affects RMA. The hypotheses presented were that both pornography use and sex education would have an impact on RMA and that the interaction between pornography use and sex education would impact RMA, all while controlling for demographic variables including gender, age, sexual orientation and ethnicity. Participants were recruited via social media (n=81). Participants completed an online survey including demographic information, the Problematic Pornography Use Scale, sex education type and the Updated Illinois Rape Myth Acceptance Scale. Results of a hierarchal regression analysis found no significant relationships between pornography use, sex education and RMA. This study suggests that the relationships between the variables may be more complex than previously thought and might require a more nuanced model. Policies aimed at promoting general media literacy might be more effective in reducing RMA than focusing specifically on pornography use. Focus should also be aimed at providing more resources and information about sex education both inside and outside of school