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Entropic Origins and Quantum Dissolution of Temporal Structure
Full text linkFindings conceptually investigate the quantum mechanical nature of time and its relationship with the second law of thermodynamics. At the quantum level, the distinction between past and future dissolves, and the present moment, as commonly understood, ceases to exist. Time\u27s directional flow—the arrow of time —arises not from the fundamental laws themselves, but from boundary conditions and observer-dependent coarse-graining.
Faculty Sponsor: Professor Raul Barre
Asthma Exacerbation in Pregnancy and the Effects on the Fetus
Full text linkAsthma is one of the most common chronic conditions affecting pregnant women and poses notable risks to both maternal and fetal health. Inadequate asthma control during pregnancy is associated with increased complications such as preeclampsia, preterm delivery and low birth weight. As a result, effective asthma management is essential throughout gestation. This paper explores the physiological changes in pregnancy that influence asthma, the potential consequences of uncontrolled symptoms and evidence-based treatment approaches. Current clinical guidelines emphasize the importance of inhaled corticosteroids and bronchodilators while minimizing systemic steroid use. Patient education, regular monitoring and a multidisciplinary approach are key to optimizing outcomes. Ensuring proper asthma management during pregnancy reduces complications and improves both maternal and neonatal health
AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
Full text linkContext: Respiratory morbidity is a leading cause of children’s consultations with general practitioners. Auscultation, the act of listening to breath sounds, is a crucial diagnostic method for respiratory system diseases. Problem: Parents and caregivers often lack the necessary knowledge and experience to identify subtle differences in children’s breath sounds. Furthermore, obtaining reliable feedback from young children about their physical condition is challenging. Methods: The use of a human–artificial intelligence (AI) tool is an essential component for screening and monitoring young children’s respiratory diseases. Using clinical data to design and validate the proposed approaches, we propose novel methods for recognizing and classifying children’s breath sounds. Different breath sound signals were analyzed in the time domain, frequency domain, and using spectrogram representations. Breath sound detection and segmentation were performed using digital signal processing techniques. Multiple features—including Mel–Frequency Cepstral Coefficients (MFCCs), Linear Prediction Coefficients (LPCs), Linear Prediction Cepstral Coefficients (LPCCs), spectral entropy, and Dynamic Linear Prediction Coefficients (DLPCs)—were extracted to capture both time and frequency characteristics. These features were then fed into various classifiers, including K-Nearest Neighbor (KNN), artificial neural networks (ANNs), hidden Markov models (HMMs), logistic regression, and decision trees, for recognition and classification. Main Findings: Experimental results from across 120 infants and preschoolers (2 months to 6 years) with respiratory disease (30 asthma, 30 croup, 30 pneumonia, and 30 normal) verified the performance of the proposed approaches. Conclusions: The proposed AI system provides a real-time diagnostic platform to improve clinical respiratory management and outcomes in young children, thereby reducing healthcare costs. Future work exploring additional respiratory diseases is warranted
