Helsinki Metropolia University of Applied Sciences

The first time-resolved leader spectra associated with a downward Terrestrial Gamma-ray Flash (TGF) observed by the Telescope Array Surface Detector (TASD) were recently reported by \citeA{kieu2024first}. Building on that study, this paper presents th

This paper presents a comprehensive comparative analysis of Natural Language Processing (NLP) methods for automated toxicity detection in online gaming chats. Traditional machine learning models with embeddings, large language models (LLMs) with zero-shot and few-shot prompting, fine-tuned transformer models, and retrieval-augmented generation (RAG) approaches are evaluated. The evaluation framework assesses three critical dimensions: classification accuracy, processing speed, and computational costs. A hybrid moderation system architecture is proposed that optimizes human moderator workload through automated detection and incorporates continuous learning mechanisms. The experimental results demonstrate significant performance variations across methods, with fine-tuned DistilBERT achieving optimal accuracy-cost trade-offs. The findings provide empirical evidence for deploying cost-effective, efficient content moderation systems in dynamic online gaming environments.

Abstract Background Measles remains a major public health issue, especially in regions with low vaccination rates and limited healthcare access. Timely outbreak prediction is crucial for effective intervention and resource allocation. Traditional epidemiological models struggle with real-time predictions due to reliance on historical data and limited analysis. Artificial intelligence (AI) offers a data-driven approach to capturing complex outbreak patterns and improving prediction accuracy. Methods This study developed and evaluated machine learning (ML) models to predict measles outbreaks at the community level, using real-world data collected by community volunteers in Ethiopia. The dataset includes demographic, vaccination status, location, clinical, and image data, providing a comprehensive assessment of outbreak risk factors. Five ML models-Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbors (KNN)-were assessed using metrics such as area under the receiver operating curve (AUC), precision, recall, and F1-score. Results The results demonstrate that machine learning models can effectively predict measles outbreaks, with the top-performing model, K-Nearest Neighbors (KNN), achieving an AUC of 0.87 (95% CI: 0.84-0.90). Among the five models tested, KNN outperformed the others, showing the highest predictive accuracy, and the difference in AUC was statistically significant (p < 0.05). Conclusions The model's ability to capture complex epidemiological factors underscores its potential to enhance surveillance and early warning systems. This research highlights the effectiveness of ML-based measles prediction in resource-limited settings. Integrating these models into public health strategies can improve early detection and reduce measles impact. Future work will focus on refining the model, incorporating real-time data, and expanding evaluation metrics for practical deployment. Key messages • Machine learning enables accurate early measles detection fusing different data sources, supporting faster response and better health outcomes in communities. • Machine learning offers a powerful tool for predicting outbreaks early, filling critical gaps in low-resource health systems.

This demo presents "Who Killed Helene Pumpulivaara?", an innovative interactive heritage experience that combines crime mystery narrative with XR technology to address key challenges in digital heritage interpretation. Our work makes six significant contributions: (1) the discovery of a "Historical Uncanny Valley" effect where varying fidelity levels between AI-generated and authentic content serve as implicit markers distinguishing fact from interpretation; (2) an accessible production pipeline combining mobile photography with AI tools that democratizes XR heritage creation for resource-limited institutions; (3) a spatial storytelling approach that effectively counters decontextualization in digital heritage; (4) a multi-platform implementation strategy across web and VR environments; (5) a practical model for AI-assisted heritage content creation balancing authenticity with engagement; and (6) a pathway toward spatial augmented reality for future heritage interpretation. Using the historic Finlayson Factory in Tampere, Finland as a case study, our implementation demonstrates how emerging technologies can enrich the authenticity of heritage experiences, fostering deeper emotional connections between visitors and the histories embedded in place.

Abstract Objectives To achieve good general health, the status of good sexual health is needed. Nonetheless, sexual problems are common and concern all people regardless of race, gender, or sexual orientation. Often a general practitioner (GP) is the first healthcare contact for patients. However, as concerns and problems of sexuality are often highly intimate, they can be hard to bring up by patients themselves. Therefore, healthcare providers should actively initiate the topic. Hence, it is crucial that GPs are familiar with and fluent on how to bring up, diagnose, and treat concerns of sexuality. Methods A web-based questionnaire assessing practice patterns in diagnosing and treating sexual problems was utilized with a random sample of 1000 GPs in Finland. The gender, age, and weekly number of patients dealt with sexual issues were also assessed. The study covered three fields: 1) attitudes toward sexual problems, 2) frequency of inquiring of various sexual problems, and 3) practice patterns in the treatment of sexual problems. Results Altogether 402 (43.5%) GPs (age range of 27–65 years, 75% female and 25% male) responded. The female GPs were more likely to consider that diagnosing male sexual problems was difficult, but no difference according to gender emerged in diagnosing female sexual problems. The female GPs were also less likely to prescribe medications for sexual problems, and to find the treatments they had prescribed as effective. The male GPs more likely frequently inquired about erectile dysfunction and increased libido, whereas the female GPs inquired about dyspareunia. The older GPs more frequently inquired of various sexual problems compared to the younger GPs. The more patients the GPs dealt with sexual issues, the more frequent they were inquiring of and treating various sexual problems: ordering further exams, prescribing drug or non-drug therapy, and considering treatments to be effective. Conclusions GPs’ self-reported practice patterns in managing patients’ sexual problems are disorganized leading to under-diagnosing and under-treating the issue. National clinical practice guidelines are needed. In addition, sexual medicine education should be increased. Conflicts of Interest All authors have no conflicts of interest pertaining to this abstract.

SUMARIO: 1. INTRODUCCIÓN. EXPERIENCIAS MÁS ALLÁ DE LA LEY DE MOVILIDAD SOSTENIBLE.— 2. LA REGULACIÓN: EL EQUILIBRIO PARA GOBERNAR LA MICROMOVILIDAD COMPARTIDA: 2.1 Los operadores privados. Aportando innovación y servicio; 2.2 Las administraciones locales. Velando por el espacio público; 2.3 Elementos sujetos a colaboración público-privada.— 3. CARACTERÍSTICAS Y RIESGOS DE LOS MODELOS DE REGULACIÓN IMPLANTADOS: 3.1 Modelo Barcelona: número de vehículos limitado, número de operadores ilimitado; 3.2 Modelo París: una regulación basada en numerus clausus; 3.3 Modelo Berlín: una laissez faire policy; 3.4 Modelo Barcelona metropolitana: un sorteo flexible.— 4. CONCLUSIONES. LA NECESIDAD DE UN MARCO NORMATIVO COMÚN.— 5. BIBLIOGRAFÍA.