This analysis successfully applied multiple text mining techniques to examine mental health discussions on Reddit across different time periods. Key achievements: Exploratory Data Analysis - identified the most frequent terms and themes in ADHD subreddit Clustering - grouped similar terms and posts to reveal underlying patterns in mental health discourse Topic Modeling - discovered latent topics using LDA, revealing distinct discussion themes Sentiment Analysis - tracked emotional tone across periods using multiple lexicons (Bing, AFINN, NRC) Text Networks - visualized word co-occurrence patterns through bigram networks

This analysis examines the impact of AI adoption in enterprises on employment rates and economic performance in European Union (EU) countries, recognizing the significant implications of AI for economic structures and employment dynamics. The primary objective is to understand how higher AI adoption in enterprises in EU countries affects the employment rate of people aged 20–64, assuming that AI technologies complement human labour and create new employment opportunities. A secondary objective is to investigate whether this relationship exhibits gender-specific effects, hypothesizing a stronger positive impact on female employment rates compared to male. The research addresses the urgent economic problem of managing the disruptive consequences of AI while harnessing its benefits, especially given the uncertainty surrounding its full impact and transitional challenges. The methodology employs a general-to-specific modelling approach using a panel dataset covering the 27 EU countries from 2021–2024. Regarding the main hypothesis (H1), the analysis established a robust modelling framework for assessing AI’s impact on overall employment, consistently identifying panel and random effects. For the secondary hypothesis (H2), the results are mixed and very nuanced, indicating a complex and sector-specific relationship between AI adoption and gender-specific employment rates. While some sectors, such as water supply, sewerage, waste management, and information and communication showed a stronger positive and statistically significant effect of AI adoption on female employment rates, supporting H2, other sectors contradicted this. Specifically, the AI hiring index, manufacturing, and professional, scientific and technical activities showed a negative effect, which was stronger for female employment. These results underline the need for targeted policies that address gender-specific challenges and employment opportunities arising from the adoption of AI across sectors.

Poor air quality is one of Poland’s most serious environmental and health problems. In winter the dominant source is low-stack emission - domestic heating with solid fuels - and long-term exposure to particulate matter is associated with thousands of premature deaths per year. State monitoring (GIOŚ) relies on a sparse network of stations, only a few per voivodeship. The question is how to reconstruct a credible, continuous concentration field over the whole country from a small number of point measurements. The aim of this project is to apply spatial machine learning methods to reconstruct the 2024 annual concentration surface for Poland from station measurements and to assess its health consequences through a population exposure analysis.