Saint Vincent College

accurate localization of sensor nodes is essential for the effective operation of Underwater Acoustic Sensor Networks (UASNs). Conventional wireless sensor network techniques cannot be directly applied underwater due to severe attenuation of radio waves and the difficulty of achieving precise time synchronization. To address these challenges, this study employs Received Signal Strength (RSS) as the primary distance-estimation metric, as it does not require clock synchronization and provides more reliable measurements in acoustic environments. We propose an enhanced localization algorithm capable of operating under clock asynchronization and node mobility. The algorithm estimates inter-node distances using RSS with the support of the Lambert-W function, enabling robust computation even in nonlinear conditions. Node mobility is incorporated by calculating velocity through the Durbin algorithm, after which initial coordinates and velocities are used to predict the final node positions at any given time. The improved approach meets the critical requirements of UASNs, including higher localization accuracy, efficient processing for dynamic nodes, and reduced computational cost, making it a practical and effective solution for real-time underwater applications.

In the contemporary recruitment environment, the evaluation of a candidate's stress and confidence remains highly subjective. This paper proposes an intelligent system, the AI-Powered Interview Stress and Confidence Analyzer, designed to provide objective metrics by analyzing both facial expressions and vocal biomarkers. While the system architecture includes both modalities, the core contribution of this paper is a robust methodology for the vocal analysis component, which addresses the critical limitations of existing approaches that rely on acted emotional data. Our proposed solution for the Face module centers on creating a novel, context-specific dataset of authentic interview audio. We detail the complete system's architecture, including the system architecture employs a Convolutional Neural Network (CNN) to interpret facial emotions. For the vocal analysis component, the framework is designed to use Mel-Frequency Cepstral Coefficients (MFCCs), a feature set that has proven effective for classifying stress and emotion in speech. This paper outlines a clear path toward developing a comprehensive, nonintrusive tool to complement traditional interview procedures, providing holistic, data-driven insights for recruiters and candidates.

“Know that in place of a heart I carry a tongue,” writes the unnamed poetic speaker of Mexican poet Balam Rodrigo’s Central American Book of the Dead. This documentary poetic text alternates between the voices of Central American immigrants journeying north and a subtle yet bold revision of Fray Bartolomé de las Casas’s A Brief Account of the Destruction of the Indies, with some words from the Friar’s 1552 text replaced by other words that reflect the realities of twenty-first century immigrants traveling north. Interspersed with de la Casas’s texts are persona poems in which we are invited to listen to the ghosts of immigrants who have suffered tragic deaths. This essay explores the ways that, crossing borders between time and space while drawing strength from his Christian faith, Rodrigo resists the erasure of Indigenous peoples, honors their journeys, and invites readers into solidarity.

In this paper, we examined the effects of experiential learning combined with collaborative teaching on the mathematics achievement of deaf learners in specialized classrooms. A quasi-experimental design was employed, comparing pre-test and post-test scores of a control group that received traditional instruction with those of an experimental group taught through experiential learning using collaborative methods. Both groups showed improvement, but the experimental group demonstrated significantly greater gains. Findings confirm the effectiveness of learner-centered approaches in enhancing engagement and understanding among deaf students. The study aligns with global education reform efforts that advocate for inclusive, adaptive teaching practices. It highlights the importance of innovative pedagogies in addressing diverse learner needs and promoting equitable outcomes, particularly in specialized and cross-cultural educational settings.

Abstract Introduction Objective markers of sleepiness that can be collected passively, such as through voice data, can be helpful for detecting sleepiness in individuals with suspected sleep disorder and in situations in which sleepiness can pose health or occupational risk. We assessed verbal reaction time (VRT) as a vocalic marker of sleepiness in older adults with history of insomnia and Benzodiazepines receptor agonist (BZRA) use who were undergoing cognitive testing to determine whether VRT accurately reflected patient-perceived sleepiness. Methods Adults aged > 55 years without a diagnosis of dementia were recruited from within a BZRA deprescribing clinical trial (NCT03687086) into this ancillary study designed to test the feasibility of cognitive testing using out-of-office, self-directed mobile apps. Participants’ working and episodic memory were assessed through recorded verbal responses to Verbal Paired Associates (VPA) tests, and patient reported sleepiness was assessed using ecological momentary assessments (EMA) of self-reported sleepiness (range: 1[not at all] to 4[more prominent]). Using a Generalized Additive Model (GAM), we examined the association between VRT during VPA testing and sleepiness, adjusting for sex, age, race, education, test parameters (difficulty, pass/fail status), caffeine intake, cognition, mood, and recent BZRA use (yes/no). Results 2230 audio samples from 19 patients were analyzed. VRT was operationalized as the time duration between recording start time and first epoch of speech. In the GAM model, longer (delayed) VRTs were positively associated with greater EMA sleepiness (p=0.00151). Our prediction model achieved 95% accuracy in classifying sleepiness levels after adjusting for other variables. Conclusion We found that longer VRTs are correlated with more self-reported sleepiness during cognitive testing. Voice data have the potential be used as a marker of sleepiness in patients undergoing cognitive testing and in situations during which a method for capturing sleepiness that does not require patient input can inform care. Support (if any) NIA R01AG057929, NHLBI K24HL143055, VA RCS20-191, UCLA CTSI UL1TR001881

This study addresses a critical issue in the context of Catholic schools in Zamboanga del Norte, Philippines: the relevance of faculty development programs in preparing teachers to meet the demands of a rapidly changing educational environment and the new generation of learners. As the education landscape evolves, it is essential to design holistic and relevant faculty development initiatives to adequately prepare educators for new challenges, including those related to globalization and the VUCAD (Volatility, Uncertainty, Complexity, Ambiguity, and Digitalization) environment. The study specifically focuses on a population consisting of seven school administrators serving as principals, sixty-one teachers, and 1,188 Grade 9 and 10 students from seven Catholic schools. The research objective is to design an integrated and relevant faculty development program by examining teachers’ teaching styles, performance, and professional development needs. A descriptive survey was employed to gather data from all the relevant stakeholders. The results revealed that Junior High School faculty consistently demonstrated six distinct teaching styles, with administrators and faculty perceiving these styles as always present, while students rated them as often demonstrated. Faculty performance was evaluated as very good, and while development needs were identified, they were considered occasional and not significantly detrimental to instructional effectiveness. The study concludes that faculty members exhibit competence and adaptability with minimal development needs. Based on these findings, this study proposes a faculty development program specifically designed to address Catholic high school educators' unique needs in the province.

BackgroundTo determine whether objective markers of sleepiness can be collected passively using voice data to detect sleepiness in individuals undergoing testing in situations where sleepiness is not the focal point of assessment. We assessed verbal reaction time (VRT) as a vocalic marker of subjective sleepiness in middle aged and older adults with history of insomnia and benzodiazepine-receptor-agonist (BZRA) use.MethodsAdults aged ≥55 without a diagnosis of dementia were recruited from a BZRA deprescribing clinical trial and enrolled in the present study that tested the feasibility of cognitive testing using out-of-office, self-directed mobile apps. Participants’ working/episodic memory were assessed through recorded verbal responses to Verbal Paired Associates (VPA) tests, and ecological momentary assessments (EMA) of self-reported sleepiness (1[not at all] to 4[more prominent]). Using a generalized additive model, we examined the association between VRT during VPA testing and self-reported sleepiness, adjusting for demographic, test parameters, caffeine intake, cognition, mood, and BZRA-use (p≤0.05 was considered significant). A stratified k-fold cross-validation/random forest (SKCV/RF) was performed to classify sleepiness levels, adjusting for other variables.ResultsWe analyzed 1,513 observations from 16 patients. VRT was operationalized as the time duration between recording start time and first speech epoch. Longer VRTs were positively associated with greater EMA sleepiness (p≤0.05). The SKCV/RF model yielded a mean F1-score of 0.80 ± 0.08 across folds.ConclusionsLonger VRTs correlated with greater self-reported sleepiness, indicating that voice data can be used as a marker of sleepiness in patients undergoing cognitive testing in out-of-office settings.

Alzheimer's Disease and Related Dementias (ADRD) afflict almost 7 million people in the USA alone. The majority of research in ADRD is conducted using post-mortem samples of brain tissue or carefully recruited clinical trial patients. While these resources are excellent, they suffer from lack of sex/gender, and racial/ethnic inclusiveness. Electronic Health Records (EHR) data has the potential to bridge this gap by including real-world ADRD patients treated during routine clinical care. In this study, we utilize EHR data from a cohort of 70,420 ADRD patients diagnosed and treated at Penn Medicine. Our goal is to uncover important risk features leading to three types of Neuro-Degenerative Disorders (NDD), including Alzheimer's Disease (AD), Parkinson's Disease (PD) and Other Dementias (OD). We employ a variety of Machine Learning (ML) Methods, including uni-variate and multivariate ML approaches and compare accuracies across the ML methods. We also investigate the types of features identified by each method, the overlapping features and the unique features to highlight important advantages and disadvantages of each approach specific for certain NDD types. Our study is important for those interested in studying ADRD and NDD in EHRs as it highlights the strengths and limitations of popular approaches employed in the ML community. We found that the uni-variate approach was able to uncover features that were important and rare for specific types of NDD (AD, PD, OD), which is important from a clinical perspective. Features that were found across all methods represent features that are the most robust.

His research interests include autonomous vehicle pedestrian avoidance algorithms as well as the development of threaded hands-

Transposant sur le plan politique le problème épineux de l’union du corps et de l’esprit, cet article s’interroge sur ce qui produit la cohésion du corps politique, à travers une enquête sur la genèse et la survie de l’institution étatique au prisme de la notion d’esprit de corps. Si Bourdieu s’appuie sur les travaux de Kantorowicz pour penser la genèse de l’État moderne à travers la sécularisation progressive de l’idée d’incarnation du corps collectif par le souverain sous la forme d’une adhésion sans faille au corps collectif incorporé qui se donne à voir dans le lien indéfectible unissant entre eux par-delà le temps et la mort les membres des grands corps de l’État ; Deleuze et Guattari, dans leur hétérogenèse de l’État, mobilisent Ibn Khaldûn, qui confère à l’esprit de corps un rôle moteur dans l’histoire des États, leur croissance comme leur déclin. Le croisement de ces deux corpus met au jour l’ambivalence de l’esprit de corps, ferment d’union et de cohésion du corps politique nécessaire à la naissance comme à la survie de l’État, mais qui, par sa nature même, peut le fragiliser. Soit que ce lien qui unit les individus se distende au sein de la société civile à mesure que les relations contractuelles supplantent les liens affectifs. Soit qu’en favorisant une autonomisation de certains groupes au sein même de l’appareil d’État et de la société, il hante la machine de l’État comme un fantôme et menace son unité et la préservation de l’intérêt général dont il est le garant.