A Multilevel Scheduling MAC Protocol for Underwater Acoustic Sensor Networks(UASN)

Underwater acoustic sensor networks (UASNs) have attracted great attention in recent years and utilizes as a part of oceanic applications. This network has to deal with propagation delay, energy constraints and limited bandwidth which are strenuous for designing a Medium Access Control (MAC) protocol for underwater communication. There also exists an idle channel listening and overhearing problem which sets down the energy into starvation in the contention-based MAC protocols. Alternatively, lengthy time slots and time synchronization equated by schedule-based MAC protocols, outcomes the variable transmission delay and degrades the network performances. To iron out these problems, we propose a cluster-based MAC protocol, tagged as Multilevel Scheduling MAC (MLS-MAC) protocol for UASN in the paper. The cluster head is a decision maker for packet transmission and aids to inflate the lifetime of sensor nodes. To reinforce the channel efficiency, the multilevel scheduling in data phase is initiated with two queues depending on the applications fixed by the cluster head. The simulation result shows that the MLS-MAC has increased the network throughput and has decreased energy consumption

Data Analytics and Visualization for Virtual Simulation

Healthcare organizations attract a diversity of caregivers and patients by providing essential care. While interacting with people of various races, ethnicity, and economical background, caregivers need to be empathetic and compassionate. Proper training and exposure are needed to understand the patient’s background and handle different situations and provide the best care for the patient. With social determinants of health (SDOH) as the basis, the thesis focuses on providing exposure through “Wright LIFE (Lifelike Immersion for Equity) - A simulation-based training tool” to two such scenarios covering patients from the LGBTQIA+ community & autism spectrum disorder (ASD). This interactive tool helps to create mindfulness about the social and economic disparities faced by the patients through realistic and captivating gameplay. Though the primary focus of the “Wright LIFE” application is “Digital Learning”, it would help to understand how effective the application is in terms of improving the provider\u27s abilities. Through statistical evidence, the tool can be improved, which in turn will improve the user experience. For this analysis, during the simulation, we also focus on collecting the data gathered from the participants through surveys. The simulation includes different questionnaires where participants can provide feedback at various stages within the simulation. This then allows for a comparison between the participants’ responses to see the rate of improvement as a result of the simulation. To analyze the data from the participant\u27s responses, data analysis, and visualization tools help to represent the data using charts, infographics, animations, and many more to assist this in this analytic process. The analysis of the data can help to understand the trend of the participants’ responses to the questionnaire. The goal of the questionnaire is to collect participants’ responses to assess anxiety, frustration, and compassion levels pre- and post-simulation. A comparative analysis is then performed. This analysis shows that the provider’s anxiety and frustration decreased after the simulation whereas the compassion increased. This is an indication that the simulation can improve the provider’s experience while working with patients with biases. The data also helped to identify the users who actively participated in the survey based on demographic data like gender, profession, experience, and age. “A picture is worth a thousand words”. Through visualization, we can bring the data to life and provide a clear idea of what the data represents by giving visual context. Tableau is used for visualizing the survey data collected from the “SDOH” simulation consisting of responses from the providers before and after the interaction with the patients. The visualizations transform the raw data into simple and informative graphs to understand the behavioral trends and to check how the providers respond to the stories in the simulations. This allows us to determine the effectiveness of the simulation more efficiently

A pharmacovigilance study of anti-depressant agents in psychiatric patients at a tertiary care teaching hospital

Background: Anti-depressant drugs have great benefit in treating a many psychiatric disorders, including schizophrenia and bipolar disorder, although all these drugs are associated with many potential adverse effects. In this study, the occurrence of adverse effects like weight gain, sleep disturbances, dry mouth were assessed and reported in drug naïve patients Anti-depressant drugs.Methods: It is a prospective observational study of patients attending Psychiatry department in NRI General Hospital of age 10 to 80 years who were prescribed with anti-depressant drugs. The study was conducted for a period of 8 months from June 2018 to February 2018.Results: Among 86 patients prescribed with antidepressants, the occurrence of adverse drug reactions due to antidepressants was 60.78% with Selective serotonin reuptake inhibitors being the most common class of drugs implicated for adverse drug reactions followed by 24.49% with Tricyclic antidepressants. A total of 51 adverse drug reactions were noted of which weight gain was most common, closely followed by sleep disturbances and drowsiness. Out of 52 adverse drug reactions assessed for causality, 88.2% of the adverse drug reactions cases were probable, while 11.7% were possible. According to Hartwig and Siegel’s Scale 84.3% of the cases are found to be mild, 15.68% moderate.Conclusions: The study allows knowing information about the occurrence and pattern of adverse drug reactions associated with Anti-depressant drugs in the population thus reducing its incidence and protecting the user population from available harm

Changes in Preventative Health Care After Medicaid Expansion

BackgroundMedicaid expansion substantially increased health insurance coverage, but its effect on the delivery of preventative health care is unclear.ObjectiveThe objective of this study was to assess the impact of Medicaid expansion on the receipt of 15 different measures of preventive care including cancer screening, cardiovascular risk reduction, diabetes care, and other primary care measures.Research designWe performed serial cross-sectional analysis of Behavioral Risk Factor Surveillance System (BRFSS) survey data from 2012 to 2017. We used a quasi-experimental design with difference-in-differences (DiD) analyses to examine changes in preventative health care delivery over 3 time periods in Medicaid expansion compared with nonexpansion states.SubjectsWe included low-income (<138% federal poverty level) nonelderly (age younger than 65 y) adults residing in 46 US states.MeasuresOur predictor was residing in a Medicaid expansion state (24 states) versus nonexpansion state (19 states). Our primary outcomes were preventative health care services, which we categorized as cancer screening (breast cancer, cervical cancer, and colorectal cancer); cardiovascular risk reduction (serum cholesterol screening in low-risk groups, serum cholesterol monitoring in high-risk groups, and aspirin use); diabetes care (serum cholesterol monitoring, hemoglobin A1c monitoring, foot examination, eye examination, and influenza vaccination, and pneumonia vaccination); and other primary care measures [influenza vaccination, alcohol use screening, and human immunodeficiency virus (HIV) screening].ResultsSurvey responses from 500,495 low-income nonelderly adults from 2012 to 2017 were included in the analysis, representing 68.2 million US adults per year. Of the 15 outcomes evaluated, we did not detect statistically significant differences in cancer screening (3 outcomes), cholesterol screening or monitoring (2 outcomes), diabetes care (6 outcomes), or alcohol use screening (1 outcome) in expansion compared with nonexpansion states. Aspirin use (DiD 8.8%, P<0.001), influenza vaccination (DiD 1.4%, P=0.016), and HIV screening (DiD 1.9%, P=0.004) increased in expansion states compared with nonexpansion states.ConclusionsMedicaid expansion was associated with an increase in aspirin use, influenza vaccination, and HIV screening in expansion states. Despite improvements in access to care, including health insurance, having a primary care doctor, and routine visits, Medicaid expansion was not associated with improvements in cancer screening, cholesterol monitoring, diabetes care, or alcohol use screening. Our findings highlight implementation challenges in delivering high-quality primary care to low-income populations

Receipt of preventive health services and current cannabis users

Substance use is associated with greater barriers and reduced access to care. Little research, however, has examined the relationship between cannabis use and receipt of preventive health services. Using data from the 2017 Behavioral Risk Factor Surveillance System, we examined the association between current cannabis use and receipt of 12 preventive health services, adjusting for sociodemographic characteristics and access to care. In analyses that adjusted for sociodemographic factors and access to care, participants with current cannabis use had lower odds of being vaccinated for influenza (AOR = 0.67, 95% CI = 0.54-0.83) and higher odds of ever receiving HPV vaccination (AOR = 1.77, 95% CI = 1.06-2.96) and HIV screening (AOR = 2.34, 95% CI = 1.88-2.92) compared with those without cannabis use. Among the 12 preventive services examined, we found three differences in receipt of preventive services by cannabis use status. Cannabis use does not appear to be associated with significant underuse of preventive services