Abstract
Acoustic Event Detection and Classification (AEDC) aims to identify and classify specific audio events within audio signals. AEDC has applications in various fields, including security systems, scene monitoring, smart hospitals, environmental monitoring, and more. The process of AEDC typically involves steps that include audio signal processing to extract relevant features from the input, a machine learning model to recognise patterns in the extracted features and a classifier to detect events. Recent research on AEDC has increasingly focused on features based on the frequency distribution of the Mel-frequency cepstral coefficients (MFCCs). In this study, the feature extraction is performed based on Cochleogram, which involves the analysis of audio signals using Gammatone filters. Cochleogram features are inspired by the human cochlea, part of the inner ear responsible for converting sound vibrations into electrical signals sent to the brain. A two-dimensional (2D) feature is extracted from the Cochleogram using Welchs spectral density estimation and then converted into a frequency spectrum. The frequency distribution of different cochleogram filter banks is then used as a one-dimensional (1D) feature. The proposed classification method uses a 1D Convolutional Neural Network (CNN), which is less complex than traditional 2D CNNs. The proposed method was evaluated using the URBAN-SED dataset, and its performance was compared against the related state-of-the-art methods. The results showed the competitiveness of the cochleogram over Mel-based features such as MFCC in AEDC if the deep learning algorithm is properly designed and trained.

Abstract

Abstract
Effective ventilation in confined spaces like subway cabins is vital for maintaining air quality, thermal comfort, and controlling airborne infections. This study employs Computational Fluid Dynamics (CFD) and neural network-based multi-objective optimization to improve subway cabin ventilation. Key parameters such as airflow velocity, temperature, Age of Air (AOA), Predicted Mean Vote (PMV), and Percentage of People Dissatisfied (PPD) were analyzed to identify optimal ventilation configurations. The key innovation of this study is an end-to-end framework that couples CFD-based airflow and heat transfer simulation with explicit airborne particle tracking and a DOE-driven multi-objective optimization to simultaneously improve fresh-air circulation, thermal comfort (PMV/PPD), and reduce particle dispersion. The results show that increasing inlet velocity to 0.6 m/s reduces AOA by 26% (from 56.8 to 42 s) compared to 0.4 m/s, significantly enhancing air freshness. Scenarios with dual-inlet configurations, particularly in the center and sides of the cabin floor, achieved the lowest AOA (29 s), indicating efficient ventilation. Sensitivity analysis reveals inlet velocity has the greatest impact, accounting for 80% of the variations in AOA and 60% in pressure differences. Optimized scenarios also balanced thermal comfort with PMV values around-0.37 and reduced PPD to 15.6%. By leveraging advanced optimization techniques, this study provides actionable insights for improving ventilation efficiency, thermal comfort, and health safety in urban transit systems. These findings demonstrate the effectiveness of simulation-based strategies for public transportation improvements.

Abstract
This study examines the role of online health communities in Brazil dedicated to cannabis treatments for chronic diseases as platforms for evidence-based activism. Using a mixed-methods approach, the research combines qualitative analysis with computational techniques, including Latent Dirichlet Allocation (LDA) topic modeling, to analyze six online groups from WhatsApp and Facebook. Key themes emerging from the analysis include treatment per pathology, treatment effects, access barriers, peer support, and advocacy efforts. The findings reveal how these communities act as epistemic networks, where patients and caregivers co-produce knowledge by sharing personal experiences and engaging in dialogue with healthcare professionals. This study highlights how online health communities transform experience sharing into structured evidence, enabling collective action to address barriers such as limited access to cannabis-based treatments. It underscores the potential of digital platforms to empower patients, foster collaboration with healthcare professionals, and influence health governance.

Abstract

Abstract
Online health communities enable patients and caregivers to share experiences, seek advice, and collaboratively generate knowledge about treatments and condition. However, accessing relevant information often proves challenging due to platform limitations like insufficient search functionalities. A previous study identified key topics discussed in Brazilian online health groups centered on cannabis treatments for chronic diseases. Building on these findings, this study introduces a proof-of-concept chatbot designed to enhance access to the collective knowledge within these communities. The chatbot prototype, built using Google Dialogflow, was tailored to provide contextually relevant, accurate, and user-friendly responses. A user study involving 38 participants evaluated its performance, showing high user satisfaction, task completion rates, and trust in the information provided. The results highlight the chatbot's potential enhance knowledge accessibility, promote patient engagement, and support evidence-based activism by organizing and disseminating community-generated content effectively.