Saint Michael's College

Abstract: The rapid growth of mobile data traffic, fueled by a variety of user applications and diverse service needs, has created significant challenges for managing traffic efficiently in next-generation wireless networks. Typically, traditional models for traffic classification and prediction function as separate tasks, which leads to a heavier computational load and less efficient inference. This paper introduces a cohesive solution that employs multi-task learning (MTL) at the mobile edge, allowing for simultaneous traffic classification and prediction in a way that is both resource-efficient and context-aware. The proposed edge-based MTL architecture utilizes shared representations through deep neural networks, facilitating the joint learning of related tasks. This approach not only boosts task performance by leveraging the connections between tasks but also greatly reduces latency and bandwidth usage by processing data closer to its source. By implementing the model on edge servers situated near base stations, we effectively eliminate the need to send data to centralized clouds, achieving real-time intelligence. Comprehensive experiments conducted on real-world mobile traffic datasets show that our model achieves impressive classification accuracy while keeping prediction error rates low. Additionally, when compared to traditional single-task learning models, our edge-based MTL method enhances generalization, shortens training time, and supports adaptive learning in dynamic mobile environments. This research lays the groundwork for a promising framework for intelligent traffic management in 5G and beyond, fostering efficient resource allocation, network slicing, and quality of service (QoS) guarantees across various traffic scenarios.

Our qualitative study examined educators’ perceptions of teaching during the COVID-19 pandemic through the lens of burnout theory. Using the three components of burnout as theorized by Maslach and Leiter (2016), we analyzed teachers’ self-reports of emotional exhaustion, cynicism/depersonalization, and reduced sense of professional self-efficacy. We interviewed 16 teachers who taught through the emergency remote-learning phase of the pandemic in March 2020 and remained in the same district when prepandemic practices returned. We conducted semistructured interviews in person and on Zoom with special educators, integrated arts teachers, school counselors, and general classroom teachers. We describe the unanticipated finding that teachers experienced an increase in perceived professional efficacy, which they attributed to certain pandemic-imposed structures and work conditions. We offer potential implications for teacher education, strengthening teacher resilience, and future research.

According to the prevalent 'Amyloid Hypothesis,' the underlying cause for neurodegeneration in Alzheimer Disease (AD) is attributed to the accumulation of misfolded Amyloid ß and tau protein in the form of extracellular sticky plaques and neurofibrillary tangles respectively. These protein accumulations are thought to be caused by impaired waste removal. In an alternative hypothesis, we have proposed the existence of an extensive glial canal system that is likely formed by myelinated aquaporin-4 (AQP4)-expressing tanycytes and removes cellular waste from the hippocampal formation. Here, we demonstrate that tanycyte-derived waste-internalizing receptacles are immunoreactive for Aß and emanate from specialized nucleus-like organelles in the following referred to as 'tanysomes.' Utilizing RNA-scope in situ hybridization, we demonstrate that these receptacle-forming 'tanysomes' express RNA for AQP4 and the Aß-related genes, amyloid precursor protein, and presenilin 1. These findings suggest that Aß is likely synthesized where receptacle formation is observed and that Aß may play an important structural role in receptacle formation. In AD-affected hippocampus excessive amounts of Aß-immunoreactive waste receptacles emerge from tanysomes and have the appearance of plaques in Aß-immunolabeled hippocampus. Moreover, we demonstrate that the same receptacle-forming organelles exhibit strong immunolabeling for hyperphosphorylated tau protein in AD-affected tissue. We postulate that both proteins may play important structural roles in waste uptake and that hypertrophic swelling of impaired tanycytes in AD-affected brain may be due to obstructions of this extensive interconnected glial canal system.

BACKGROUND: The COVID-19, as a global health threat, prompted countries to impose measures to limit its transmission. One of the common mental disorders currently being investigated as a consequence of the COVID-19 pandemic is major depression. However, there is a disconcerting issue in assessing depression amid the pandemic. It has been noted that most studies investigating depression did not examine the psychometric soundness of their scales in the context of the pandemic. To address this gap, this study intends to examine the validity, reliability, and utility of Patient Health Questionnaire-9 (PHQ-9) as a measure of COVID-19 pandemic-related depression.METHODS: Utilizing 3032 teachers, this study established evidence of validity. To demonstrate construct-related validity, we examined and identified the best-fitting model from the five a-priori models. To evince criterion-related validity, the major depressive disorder (MDD) best-fitting model was correlated with mental health outcomes. The reliability of PHQ-9 was substantiated by analyzing internal consistency of the items.RESULTS: The results show PHQ-9 to be construct-valid as evidenced by excellent fit indices of the two-factor model 2a. The findings confirm this model to best represent MDD symptoms. Criterion-related evidence of validity was demonstrated with the significant relationship of model 2a’s two factors (somatic and non-somatic factors) to psychological distress, somatic symptoms, and depression. The scale is reliable as established by higher internal consistency coefficients.CONCLUSIONS: Overall, the findings suggest that PHQ-9 is a valid, reliable, and utilizable scale in assessing depression symptoms in the context of a pandemic or health-related epidemic. Additionally, the data supporting model 2a could be a basis in developing a more nuanced intervention addressing the MDD’s two-factor symptom structure.