Gateway Battered Women\u27s Services Website Development Project

The development of a website for a non-profit organization concerned with domestic violence is described, including the needs assessment, methodology, implementation, finalization, and evaluation. Gateway Battered Women\u27s Services (GBWS), in Aurora, Colorado, provides shelter, advocacy, and counseling for women and children affected by domestic violence. Its goal is the elimination of personal and societal violence against all women and children through education, support services, and promoting social change within the community. GBWS needed a web presence, a database, and an online counseling service to distribute information on domestic violence to potential clients and volunteers and to solicit donations. A questionnaire was used to evaluate the results and to formulate recommendations. The recommendations were implemented where possibl

COVID-19 pandemic’s impact on eating habits in Saudi Arabia

Background: COVID-19 virus has been reported as a pandemic in March 2020 by the WHO. Having a balanced and healthy diet routine can help boost the immune system, which is essential in fighting viruses. Public Health officials enforced lockdown for residents resulting in dietary habits change to combat sudden changes. Design and Methods: A cross-sectional study was conducted through an online survey to describe the impact of the COVID-19 pandemic on the eating habits, quality and quantity of food intake among adults in Saudi Arabia. SPSS version 24 was used to analyze the data. Comparison between general dietary habits before and during COVID-19 for ordinal variables was performed by Wilcoxon Signed Rank test, while McNemar test was performed for nominal variables. The paired samples t-test was used to compare the total scores for food quality and quantity before and during COVID-19 periods.Results: 2706 adults residing in Riyadh completed the survey. The majority (85.6%) of the respondents reported eating home-cooked meals on a daily basis during COVID-19 as compared to 35.6% before (p<0.001). The mean score for the quality of food intake was slightly higher (p=0.002) before the COVID-19 period (16.46±2.84) as compared to the during period (16.39±2.79). The quantity of food mean score was higher (p<0.001) during the COVID-19 period (15.70±2.66) as compared to the before period (14.62±2.71).Conclusion: Dietary habits have changed significantly during the COVID-19 pandemic among Riyadh residents. Although some good habits increased, the quality and the quantity of the food was compromised. Public Health officials must focus on increased awareness on healthy eating during pandemics to avoid negative consequences. Future research is recommended to better understand the change in dietary habits during pandemics using a detailed food frequency questionnaire

Determination of Urban Public Transport Demand by Processing Electronic Travel Ticket Data

Determination of transport demand is one of the key factors to solve many transportation problems. Existing methods for obtaining information about passenger mobility have significant shortcomings; currently, methods based on the collection, integration and analysis of big data (Urban computing, big data) are being increasingly used.Within the framework of this approach, a methodology has been developed for determining the passenger traffic by public transport from the operations of validating electronic travel tickets: smart cards, transport cards, magnetic cards, mobile phones or other electronic devices (Electronic Gadgets), their details are recorded in the Automated Fare Collection (AFC).In this work we have taken into account that the passenger can travel one, two or more segments before paying for the trip. On some routes, payment is made at the end of the trip.The article presents a methodology based on defining and evaluating the set of acceptable variants of the connectedness of the passenger trips' sequence, which takes into account many factors that influence the choice of travel routes by the passenger. For example, unlike existing solutions, the possibility of paying for travel at any point of the route is taken into account, not necessarily immediately after boarding the vehicle.Approbation of the considered methodology was carried out on the data of the Krasnoyarsk public transport system for April 2019. It has been proved that passenger traffic from the validation of electronic travel tickets allows us to estimate the parameters of public transport demand within the limits of acceptable statistical errors

Sparsity via new Bayesian Lasso

Lasso estimate as the posterior mode assuming that the parameter has prior density as double exponential distribution [1]. In this paper, we proposed Scale Mixture of Normals mixing with Rayleigh (SMNR) density on their variances to represent the double exponential distribution. Hierarchical model formulation presented with Gibbs sampler under SMNR as alternative Bayesian analysis of minimization problem of classical lasso. We conducted two simulation examples to explore path solution of the Ridge, Lasso, Bayesian Lasso, and New Bayesian Lasso (R, L, BL, NBL) regression methods through the prediction accuracy using the bias of the estimates with different sample sizes, bias indicates that the lasso regression perform well, followed by the NBL. The Median Mean Absolute Deviations (MMAD) used to compared the perform of the regression methods using real data, MMAD indicates that the proposed method (NBL) perform better than the others

Bayesian estimation and variables selection for binary composite quantile regression

In this paper, Bayesian hierarchical model proposed to estimate the coefficients of the composite quantile regression model when the response variable is binary. For selecting variables in binary composite quantile regression lasso the adaptive lasso penalty is derived in a Bayesian framework. Simulation study and real data examples are used to examine the performance of the proposed methods compared to the other existing methods. We conclude that the proposed method is comparable

Bayesian extensions on Lasso and adaptive Lasso Tobit regressions

Since lasso method launched, a lot of applications and extensions were run on it which made it to become deeply widely used in various discipline. In this paper, we proposed the Scale Mixture of Normals mixing with Rayleigh (SMNR) distribution on their variances to represent the double exponential distribution. Hierarchical model formula have derived with Gibbs sampler for SMNR. The proposed models; Bayesian Tobit Adaptive Lasso (BTAL) and Bayesian Tobit Lasso (BTL) models are illustrated using simulation example and a real data example through the prediction accuracy using the estimated relative efficiency with different sample. This is the first work that discussed regularization regression models under SMNR

The Sultanate of Oman in the international Order between 1980 and 1991

The importance of the study stems from the uniqueness of the Omani experience and the foreign policy from the rest of the Gulf Cooperation Council countries, which will show the characteristics of the Sultanate as a different element. Its policy is neutral, and there is a lack of sovereign ambition despite having all the potentials to do so, such as strategic position, economic and military strength. The research concluded that Oman\u27s policy was based on peaceful and cooperative relations with all countries of the world; with the aim of preserving its sovereignty from any international interference, while maintaining its strategic position

New Bayesian Lasso Composite Quantile Regression

In this paper, we propose a new Bayesian lasso inference scheme for variable selection in composite quantile regression model (C Quantile Reg). The suggested approach is to construct a hierarchical structure within the Gibbs sampling under the assumption that the residual term comes from skew Laplace distribution (asymmetric Laplace distribution) and  assign scale mixture uniform (SMU) as prior distributions on the coefficients of composite quantile regression model. Our proposed method was compared to some other existing methods by testing the performance of these methods through simulation studies and real data examples

Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy. Objective: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. Methods: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data. Results: DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p 15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001). Conclusions: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF