KSU Traffic: Optimizing Campus Flow

Traffic is a problem in all major cities and while it can be improved, it will likely never be eliminated. Both the Kennesaw and Marietta campus of Kennesaw State University (KSU) experience traffic delays during peak class periods. To remedy this problem on the KSU Marietta campus, data was collected at different hours of the day over a few weeks at the South Marietta Pkwy/West Main Entrance SE intersection. This data was used in simulations to determine whether the best solution to decrease traffic would be to alter the timing of the traffic lights or construct a right turning lane out of campus. Based on analysis, including a cost-benefit economic interpretation, it is suggested that constructing a right turning lane out of campus is the best solution to the problem

Aircraft Parameter Estimation using Feedforward Neural Networks With Lyapunov Stability Analysis

Aerodynamic parameter estimation is critical in the aviation sector, especially in design and development programs of defense-military aircraft. In this paper, new results of the application of Artificial Neural Networks (ANN) to the field of aircraft parameter estimation are presented. The performances of Feedforward Neural Network (FFNN) with Backpropagation and FFNN with Backpropagation using Recursive Least Square (RLS) are investigated for aerodynamic parameter estimation. The methods are validated on flight data simulated using MATLAB implementations. The normalized Lyapunov energy functional has been used to derive the convergence conditions for both the ANN-based estimation algorithms. The estimation results are compared on the basis of performance metrics and computation time. The performance of FFNN-RLS has been observed to be approximately 10% better than FFNN-BPN. Simulation results from both algorithms have been found to be highly satisfactory and pave the way for further applications to real flight test data

Case report on splenic abscess with pleural effusion caused by enteric fever

Splenic abscess is an infrequent complication of enteric fever caused by Salmonella typhi. The incidence rate ranges from 0.14-2%. Clinical manifestations are often nonspecific and may be presented as fever with left upper quadrant abdominal pain and a palpable tender mass. Diagnosis is often difficult and splenic abscess management is based on surgical interventions and antibiotic therapy. In this case report we would like to highlight splenic abscess with left reactive pleural effusion as a rare complication of Salmonella typhi infection

QuantifyMe: An Open-Source Automated Single-Case Experimental Design Platform

Smartphones and wearable sensors have enabled unprecedented data collection, with many products now providing feedback to users about recommended step counts or sleep durations. However, these recommendations do not provide personalized insights that have been shown to be best suited for a specific individual. A scientific way to find individualized recommendations and causal links is to conduct experi ments using single-case experimental design; however, properly designed single-case experiments are not easy to conduct on oneself. We designed, developed, and evaluated a novel platform, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using their smartphones. We provide software for the platform that we used (available for free on GitHub), which provides the methodological elements to run many kinds of customized studies. In this work, we evaluate its use with four different kinds of personalized investigations, examining how variables such as sleep duration and regularity, activity, and leisure time affect personal happiness, stress, productivity, and sleep efficiency. We conducted a six-week pilot study (N = 13) to evaluate QuantifyMe. We describe the lessons learned developing the platform and recommendations for its improvement, as well as its potential for enabling personalized insights to be scientifically evaluated in many individuals, reducing the high administrative cost for advancing human health and wellbeing. Keywords: single-case experimental design; mobile health; wearable sensors; self-experiment; self-trackin

Prognostic value of echocardiographic parameters in congenital diaphragmatic hernia: a systematic review and meta-analysis.

BACKGROUND: Prognostication of mortality and decision to offer extracorporeal membrane oxygenation (ECMO) treatment in infants with congenital diaphragmatic hernia (CDH) can inform clinical management. OBJECTIVE: To summarise the prognostic value of echocardiography in infants with CDH. METHODS: Electronic databases Ovid MEDLINE, Embase, Scopus, CINAHL, the Cochrane Library and conference proceedings up to July 2022 were searched. Studies evaluating the prognostic performance of echocardiographic parameters in newborn infants were included. Risk of bias and applicability were assessed using the Quality Assessment of Prognostic Studies tool. We used a random-effect model for meta-analysis to compute mean differences (MDs) for continuous outcomes and relative risk (RR) for binary outcomes with 95% CIs. Our primary outcome was mortality; secondary outcomes were need for ECMO, duration of ventilation, length of stay, and need for oxygen and/or inhaled nitric oxide. RESULTS: Twenty-six studies were included that were of acceptable methodological quality. Increased diameters of the right and left pulmonary arteries at birth (mm), MD 0.95 (95% CI 0.45 and 1.46) and MD 0.79 (95% CI 0.58 to 0.99), respectively) were associated with survival. Left ventricular (LV) dysfunction, RR 2.40, (95% CI 1.98 to 2.91), right ventricular (RV) dysfunction, RR 1.83 (95% CI 1.29 to 2.60) and severe pulmonary hypertension (PH), RR 1.69, (95% CI 1.53 to 1.86) were associated with mortality. Left and RV dysfunctions, RR 3.30 (95% CI 2.19 to 4.98) and RR 2.16 (95% CI 1.85 to 2.52), respectively, significantly predicted decision to offer ECMO treatment. Limitations are lack of consensus on what parameter is optimal and standardisation of echo assessments. CONCLUSIONS: LV and RV dysfunctions, PH and pulmonary artery diameter are useful prognostic factors among patients with CDH

EFFECTS OF A PROPRIETARY BLEND RICH IN GLYCOSIDE BASED STANDARDIZED FENUGREEK SEED EXTRACT (IBPR) ON INFLAMMATORY MARKERS DURING ACUTE ECCENTRIC RESISTANCE EXERCISE IN YOUNG SUBJECTS

  Objective: To assess the efficacy of a proprietary blend rich in glycoside based standardized fenugreek seed extract (400 mg) and minor quantities of curcumin and cinnamon (25 mg each) supplementation (IBPR) on inflammatory markers related to skeletal muscle soreness using double-blind placebo control, parallel design.Methods: A total of 20 healthy non-resistance trained young male and female subjects were assigned to ingest either IBPR or matching placebo for 14 days before the eccentric exercise bout. Subjects were instructed to perform 24 sets with 10 eccentric knee extensor repetitions (with one leg at 30°/s on an isokinetic device). Subjects had their blood drawn at baseline, immediately post, 1 hr, 3 hrs, and 24 hrs post-eccentric exercise. Efficacy in terms of serum levels of anti-inflammatory cytokines interleukin-10 (IL-10), pro-inflammatory cytokines (IL-1ra, IL-1b, IL-6, and tumor necrosis factor) and safety in terms of kidney function (blood urea nitrogen (BUN), serum creatinine, BUN to creatinine ratio), and differential leukocyte count were measured. The data of each parameter were analyzed by two-way repeated measure ANOVA.Results: Significant time-dependent effects were observed in IL1b, IL6, and creatinine values from baseline whereas significant treatment dependent effect was seen in IL-1ra. IBPR was found to be safe and well tolerated.Conclusion: IBPR supplementation showed a significant anti-inflammatory efficacy on eccentric exercise-induced inflammatory markers of skeletal muscle soreness in non-resistance trained subjects

Health system barriers influencing perinatal survival in mountain villages of Nepal: implications for future policies and practices

Background: This paper aims to examine the health care contexts shaping perinatal survival in remote mountain villages of Nepal. Health care is provided through health services to a primary health care level\u2014comprising district hospital, village health facilities and community-based health services. The paper discusses the implications for future policies and practice to improve health access and outcomes related to perinatal health. The study was conducted in two remote mountain villages in one of the most remote and disadvantaged mountain districts of Nepal. The district is reported to rank as the country\u2019s lowest on the Human Development Index and to have the worst child survival rates. The two villages provided a diversity of socio-cultural and health service contexts within a highly disadvantaged region. Methods: The study findings are based on a qualitative study of 42 interviews with women and their families who had experienced perinatal deaths. These interviews were supplemented with 20 interviews with health service providers, female health volunteers, local stakeholders, traditional healers and other support staff. The data were analysed by employing an inductive thematic analysis technique. Results: Three key themes emerged from the study related to health care delivery contexts: (1) Primary health care approach: low focus on engagement and empowerment; (2) Quality of care: poor acceptance, feeling unsafe and uncomfortable in health facilities; and (3) Health governance: failures in delivering health services during pregnancy and childbirth. Conclusions: The continuing high perinatal mortality rates in the mountains of Nepal are not being addressed due to declining standards in the primary health care approach, health providers\u2019 professional misbehaviour, local health governance failures, and the lack of cultural acceptance of formalised care by the local communities. In order to further accelerate perinatal survival in the region, policy makers and programme implementers need to immediately address these contextual factors at local health service delivery points

[S IV] in the NGC 5253 Supernebula: Ionized Gas Kinematics at High Resolution

The nearby dwarf starburst galaxy NGC 5253 hosts a deeply embedded radio-infrared supernebula excited by thousands of O stars. We have observed this source in the 10.5{\mu}m line of S+3 at 3.8 kms-1 spectral and 1.4" spatial resolution, using the high resolution spectrometer TEXES on the IRTF. The line profile cannot be fit well by a single Gaussian. The best simple fit describes the gas with two Gaussians, one near the galactic velocity with FWHM 33.6 km s-1 and another of similiar strength and FWHM 94 km s-1 centered \sim20 km s-1 to the blue. This suggests a model for the supernebula in which gas flows towards us out of the molecular cloud, as in a "blister" or "champagne flow" or in the HII regions modelled by Zhu (2006).Comment: Accepted for publication in the Astrophysical Journal 4 June 201

Rosin Based Composites for Additive Manufacturing

Acknowledgements: This work is supported by the Fundação para a Ciência e a Tecnologia (FCT) and Centro2020 through the Project references: UID/Multi/04044/2013; PAMI – ROTEIRO/0328/2013 (Nº 022158) and MATIS (CENTRO-01-0145-FEDER-000014 – 3362).Rosins are the non-volatile exudates of pine resins with hydrophobic characteristics that are widely used as a precursor for many industrial applications. In this paper we discuss the nature, process and its applications as a matrix for a composite material for additive manufacturing. The composite material has been tailored to chemical and mechanical properties with respect to their applications.info:eu-repo/semantics/publishedVersio

Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40X magnification in 2.5 seconds per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi