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Enhancing Postpartum Depression Screening Rates: An Educational Intervention For Primary Care Pediatric Providers: A Quality Improvement Project
Full text linkThis quality improvement (QI) project sought to increase postpartum depression (PPD) screening rates at a single pediatric primary care practice setting by implementing an educational intervention for pediatric providers. Postpartum depression is a common and debilitating mental health condition affecting many mothers worldwide. Postpartum depression impacts many women in the United States and affects all ethnicities, although rates may vary based on demographic and socioeconomic factors. Despite the American Academy of Pediatrics\u27 recommendations for maternal PPD screening at one, two, four, and six months, well-child visits, national screening rates remain low, with a large number of cases undiagnosed. The current literature revealed significant gaps in the knowledge and confidence of primary care pediatric providers regarding PPD screening protocols, which result in missed opportunities for early maternal mental health intervention during routine infant care visits. The PICOT question guiding this investigation was: Among primary pediatric providers in a single pediatric setting (P), did providing an educational session on postpartum depression screening guidelines (I)
increase postpartum depression screening rates (O) compared to no specific education (C) within 30 days (T)? This QI project employed a pre-post intervention design with primary care providers in a pediatric setting, including nurse practitioners, who constituted the population of interest. The intervention consisted of a single educational session on PPD screening, along with an evidence-based assessment tool, the Edinburgh Postnatal Depression Scale. A retrospective and prospective chart audit was conducted comparing PPD screening rates before and after the educational intervention. Data collection occurred 30 days post-implementation, and PPD screening rates increased from .08% pre-intervention to 95% post-intervention, representing a statistically significant improvement in provider adherence to screening protocols. Educational interventions targeting pediatric providers may effectively improve PPD screening compliance in primary care settings. Implementation of structured educational programming, combined with standardized screening protocols and systematic monitoring, demonstrated potential for addressing current gaps in PPD identification during routine pediatric visits. Future research should focus on the longitudinal sustainability of these improvements and potential impacts on maternal treatment, engagement, and outcomes.
Keywords: postpartum depression, PPD screening, American Academy of Pediatrics, AAP, Edinburgh Postnatal Depression Scale, maternal mental health, well child visits, pediatric primary care providers, PPD educational sessio
Transforming Education Through the Cohort Model in Graduate Schools of Education
Full text linkThe cohort model has emerged as a transformative approach in graduate schools of education for fostering a collaborative learning environment, providing professional networking opportunities, increasing community, and fostering a sense of belonging. This analysis is framed within Mead, Vygotsky’s, and Wenger’s theoretical framework on the interconnectedness of social interaction and cognitive development. Increased engagement, learning opportunities, promoting student success, enhanced leadership development, and improved program components are noted benefits of utilizing the cohort model in graduate schools of education. These authors noted some challenges to the cohort model, such as group dynamics and cohesion, diversity and inclusion, rigid structure, pressure to conform, resources and faculty challenges, retention and attrition, and compatibility with adult learners. The future implications of graduate programs utilizing a cohort model are replete with leveraging student support, sharing of experiences, and social integration. The gaps that were identified resonated in the areas of intentional cohort design, faculty training, integrated support systems, and continuous assessment. More research is needed in these areas to augment the transformative aspect of the cohort model in graduate schools of education
From Diversity To Novelty: Unsupervised Learning For Novel Transposon Identification And Evolutionary Mapping For Large-Scale Genomic Datasets
No full textTransposons play a pivotal role in genome evolution and contribute to genome expansion, with novel transposon discovery offering valuable insights into genetic function and its implications for health and diseases. This study focused on identifying novel transposons within a large-scale genomic dataset using unsupervised machine learning approaches to uncover hidden patterns and detect elements that deviated from known transposon groups. To address the challenge of data scale and to deal with the computational complexity, two complementary approaches were adopted: first, analyzing the entire dataset to picture the broad spectrum of diversity; second, a strategic reduction and filtering method to produce a manageable dataset that enabled efficient identification of novel elements.
The density-based clustering algorithm called Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was applied to both the full and filtered datasets due to its robust capacity to detect outliers. In our study, transposons were considered as outliers that do not fit into any well-defined clusters and are highly suitable for pinpointing potentially different elements. That is how outliers represent promising candidates for novel transposes. In addition to detecting novel transposons, the clustering patterns revealed meaningful phylogenetic relationships among transposon groups, shedding light on their evolutionary trajectories and biological interconnections. This integrated method significantly enhanced the detection of novel transposons, deepening understanding of their impact on genomic architecture and their potential roles in human health. Ultimately, these findings offer a more nuanced view of genome dynamics and expand the landscape of functional genomics research.
Index Terms: Bioinformatics, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) Clustering, high performance computing, transposons, unsupervised learning
(SI15-068) Advanced Numerical Methods for the Solution of Nonlinear Fisher Equation
No full textThis paper examines the use of advanced numerical techniques to approximate solutions of the Fisher equation with higher-order accuracy. This technique integrates the method of lines with a strong stability-preserving Runge–Kutta scheme of orders four and five stages (SSPRK-54) for the numerical formulation. This scheme is then tested on two examples and the results show that it is more efficient than existing methods and requires less computing power. These equations are widely used across scientific and engineering disciplines, with particular relevance in biomedical studies, such as estimating the boundary size of tumors. The difficulties arising from their nonlinear nature are effectively addressed through advanced numerical methods
Performance Comparison Of Mobile GPUS For Object Detection In Edge Computing
No full textIn recent years, deep learning models for computer vision applications like YOLO (You Only Look Once) have become widely used for real-time object detection tasks. Mobile GPUs such as the NVIDIA Jetson series are highly promising for deploying these models in resource-constrained edge computing environments. The objective of this thesis is to examine and compare the performance of object detection of different versions of YOLO models deployed on different NVIDIA Jetson mobile GPUs using different versions of the COCO datasets. This allows for evaluation of the four performance metrics, including detection accuracy, model size, execution time, and power consumption, which emphasizes different aspects across combinations of various YOLO versions, edge devices, and datasets. The experimental results offer important insights into the trade-off among the choices of different YOLO versions and mobile GPUs and their practical potential. Findings demonstrate that the YOLOv8 models maintain similar accuracy levels across all Jetson GPUs, where the accuracy of the models reaches 78% on the COCO8 dataset, underscoring their effectiveness for real-time applications in edge computing. Additionally, this analysis reveals significant variations in power consumption, with the Jetson AXG Orin showing an efficient balance between performance in inference time and energy usage, where the power consumption is only 11.05 W and the inference time is 78.1 ms even while running the most complex YOLOv8x model. This study will help researchers and practitioners to gain insight and select the best combination of mobile GPU and YOLO version for future object detection tasks in edge computing.
Index Terms: benchmarking, deep learning, edge computing, embedded systems, mobile GPUs, object detection, YOLOv8
(R2115) Analysis of Single Server Queueing System with Differentiated Vacations and Differentiated Breakdowns
No full textThis research work considers a single server queueing model with differentiated vacations. In addition there is a possibility of two types of failures when the server is in a busy period; namely hard failure and soft failure. In the time of soft failure server may work with a slow service rate. We analyzed as a Quasi-Birth-and-Death (QBD) process, using the matrix geometric method, the steady state probability vector of the number of customers in the queue and the stability conditions are produced. Busy period analysis of the proposed model in given. The effects of various parameters on the system performance measures are illustrated numerically
(R2132) A Multi Server Markovian Working Vacation Queue with Randomly Varying Environment
No full textIn this article, we consider a multi server Markovian queueing system with working vacation. During busy period, the arrival and service completion are generated by K distinct randomly varying environments. At a service completion epoch, if no customer in the system, the servers take vacation, the vacation policy is multiple vacation policy and the vacation period follows negative exponential distribution. In addition, during vacation period the servers serve customers if they arrive. Based on the vacation termination point we define two Models. For the two models, the steady state probability vector of number of customers in the queue, the stability condition and some performance measures are derived. Some illustrative examples are also provided
(R2130) CUSUM-test for Unconditional Variance Change Detection in Bilinear GARCH Models
No full textWe examine CUSUM-type test for detecting changes in unconditional variance within Bilinear GARCH models. We derive the asymptotic distribution of the test statistic under both null and alternative hypotheses and assess test effectiveness in identifying single structural breaks. Simulation studies support our theoretical results and demonstrate the practical utility of the test
(R2084) Clarification of Mathematical Criteria for Conservative Forces and Vector Fields with a Point Singularity in Engineering Studies
No full textThe paper deals with a comprehensive analysis of existing mathematical criteria for conservative vector fields including algebraic and geometrical interpretation of Extended Green’s Theorem. Special attention is devoted to the fields containing a point singularity, which represents the primary challenge both in teaching Calculus and in carrying out scientific research in general in computer engineering. Examples of real-world physical fields such as gravitational, electric and magnetic ones are used to appreciate the proposed clarification while teaching various interchangeable disciplines such as mathematics, physics and engineering. Other examples from scientific research literature are provided as well to support the necessity of the study. The detailed analysis of Extended Green’s theorem has been also performed to avoid frequent misunderstandings arising in various topics of vector calculus, mechanics, physics and pure mathematics. The research conducted has as a main objective to improve university curricula in engineering majors based on the current requirement of joining science, technology, engineering and mathematics (STEM curriculum). Besides, it contributes to a more efficient way of approaching geometrical singularities, which is extremely important in any computer programing