Developing a scalable training model in global mental health: pilot study of a video-assisted training Program for Generalist Clinicians in Rural Nepal.

BackgroundIn low- and middle-income countries, mental health training often includes sending few generalist clinicians to specialist-led programs for several weeks. Our objective is to develop and test a video-assisted training model addressing the shortcomings of traditional programs that affect scalability: failing to train all clinicians, disrupting clinical services, and depending on specialists.MethodsWe implemented the program -video lectures and on-site skills training- for all clinicians at a rural Nepali hospital. We used Wilcoxon signed-rank tests to evaluate pre- and post-test change in knowledge (diagnostic criteria, differential diagnosis, and appropriate treatment). We used a series of 'Yes' or 'No' questions to assess attitudes about mental illness, and utilized exact McNemar's test to analyze the proportions of participants who held a specific belief before and after the training. We assessed acceptability and feasibility through key informant interviews and structured feedback.ResultsFor each topic except depression, there was a statistically significant increase (Δ) in median scores on knowledge questionnaires: Acute Stress Reaction (Δ = 20, p = 0.03), Depression (Δ = 11, p = 0.12), Grief (Δ = 40, p < 0.01), Psychosis (Δ = 22, p = 0.01), and post-traumatic stress disorder (Δ = 20, p = 0.01). The training received high ratings; key informants shared examples and views about the training's positive impact and complementary nature of the program's components.ConclusionVideo lectures and on-site skills training can address the limitations of a conventional training model while being acceptable, feasible, and impactful toward improving knowledge and attitudes of the participants

Effect of a liver cancer education program on hepatitis B screening among Asian Americans in the Baltimore-Washington metropolitan area, 2009-2010

IntroductionAsian Americans have the highest incidence of hepatocellular carcinoma (HCC), the major form of primary liver cancer, of all ethnic groups in the United States. Chronic hepatitis B virus (HBV) infection is the most common cause of HCC, and as many as 1 in 10 foreign-born Asian Americans are chronically infected with HBV. We tested the effectiveness of a culturally tailored liver cancer education program for increasing screening for HBV among Chinese, Korean, and Vietnamese Americans residing in the Baltimore–Washington metropolitan area, from November 2009 through June 2010. MethodsWe used a cluster randomized controlled trial to recruit volunteer participants from community-based organizations (CBOs) in the Baltimore–Washington metropolitan area. We selected 877 participants by using a pretest survey. People were eligible to participate if they had not attended a hepatitis B–related education program in the past 5 years. The intervention group (n = 441) received a 30-minute educational program, and the control group (n = 436) received an educational brochure. After attending the educational program, the intervention group completed a post-education survey. Six months later, participants in both groups were followed up by telephone. Receipt of HBV screening was the outcome measure. ResultsApproximately 79% (n = 688) of participants completed the 6-month follow-up telephone survey. Among those who had not had HBV screening at baseline (n = 446), the adjusted odds of self-reported receipt of HBV screening at the 6-month follow-up to the educational program were significantly higher for the intervention group than for the control group (odds ratio = 5.13; 95% confidence interval, 3.14–8.39; P \u3c .001). Chinese Americans and Vietnamese Americans had significantly higher odds of having HBV screening in the 6-month period than Korean Americans. ConclusionCulturally tailored education programs that increase liver cancer awareness can be effective in increasing HBV screening among underserved Asian American populations

Plans for Crash-Tested Bridge Railings for Longitudinal Wood Decks

In the past decade, bridge railing design criteria have moved away from static-load design and have focused on full-scale crash testing as a more appropriate and reliable means of evaluating bridge railings. The five bridge railing plans presented reflect the results of a cooperative research project between the Midwest Roadside Safety Facility, University of Nebraska-Lincoln; the USDA Forest Service, Forest Products Laboraotry; and the Federal Highway Administration. The project objective was to develop and crash test bridge railings and approach railing transitions for longitudinal wood bridge decks. The bridge railings were completed in accordance with AASHTO Performance Levell, Performance Level 2, and NCHRP Report 350 Test Level 4 (TL-4). Approach railings were tested or adapted from previous testing in accordance with NCHRP Report 230. Full drawing sets are provided in customary U.S. and SI units of measure. The testing procedures, results, and drawings have been approved by the Federal Highway Administration Federal-Aid and Design Office for use on Federal-aid highway projects

Plans for Crash-Tested Wood Bridge Railings for Concrete Decks

As part of a continuing cooperative research between the Midwest Roadside Safety Facility (MwRSF); the USDA Forest Service, Forest Products Laboratory (FPL); and the Federal Highway Administration (FHWA), several crashworthy wood bridge railings and approach railing transitions have been adapted for use on concrete bridge decks. These railings meet testing and evaluation criteria outlined in National Cooperative Research Program (NCHRP) Report 350, Recommended Procedures for the Safety Performance Evaluation of Highway Features, and include a glued-laminated timber (glulam) rail, with and without a curb, at Test Level- 2 (TL-2), a glulam rail with curb at TL-4, and a glulam curb rail for low-volume roads at TL-1. In adapting the railings from a wood deck to a concrete deck, the critical consideration was railing attachment to the deck. A comparable connection was obtained by an analysis of maximum loads measured by field instrumentation during crash testing or by equating the ultimate capacity of connections used on the wood deck to those required for a concrete deck. For the convenience of the user, full drawing sets are provided in customary U.S. and S.I. units

Plans for Crash-Tested Wood Bridge Railings for Concrete Decks

As part of a continuing cooperative research between the Midwest Roadside Safety Facility (MwRSF); the USDA Forest Service, Forest Products Laboratory (FPL); and the Federal Highway Administration (FHWA), several crashworthy wood bridge railings and approach railing transitions have been adapted for use on concrete bridge decks. These railings meet testing and evaluation criteria outlined in National Cooperative Research Program (NCHRP) Report 350, Recommended Procedures for the Safety Performance Evaluation of Highway Features, and include a glued-laminated timber (glulam) rail, with and without a curb, at Test Level- 2 (TL-2), a glulam rail with curb at TL-4, and a glulam curb rail for low-volume roads at TL-1. In adapting the railings from a wood deck to a concrete deck, the critical consideration was railing attachment to the deck. A comparable connection was obtained by an analysis of maximum loads measured by field instrumentation during crash testing or by equating the ultimate capacity of connections used on the wood deck to those required for a concrete deck. For the convenience of the user, full drawing sets are provided in customary U.S. and S.I. units

Architecting Enterprise Applications for the Cloud: The Unicorn Universe Cloud Framework

© Springer International Publishing AG, part of Springer Nature 2018. Recent IT advances that include extensive use of mobile and IoT devices and wide adoption of cloud computing are creating a situation where existing architectures and software development frameworks no longer fully support the requirements of modern enterprise application. Furthermore, the separation of software development and operations is no longer practicable in this environment characterized by fast delivery and automated release and deployment of applications. This rapidly evolving situation requires new frameworks that support the DevOps approach and facilitate continuous delivery of cloud-based applications using micro-services and container-based technologies allowing rapid incremental deployment of application components. It is also becoming clear that the management of large-scale container-based environments has its own challenges. In this paper, we first discuss the challenges that developers of enterprise applications face today and then describe the Unicorn cloud framework (uuCloud) designed to support the development and deployment of cloud-based applications that incorporate mobile and IoT devices. We use a doctor surgery reservation application “Lekar” case study to illustrate how uuCloud is used to implement a large-scale cloud-based application

Measurement of Two-Photon Exchange Effect by Comparing Elastic e ± p Cross Sections

Background: The electromagnetic form factors of the proton measured by unpolarized and polarized electron scattering experiments show a significant disagreement that grows with the squared four-momentum transfer (Q2) . Calculations have shown that the two measurements can be largely reconciled by accounting for the contributions of two-photon exchange (TPE). TPE effects are not typically included in the standard set of radiative corrections since theoretical calculations of the TPE effects are highly model dependent, and, until recently, no direct evidence of significant TPE effects has been observed. Purpose: We measured the ratio of positron-proton to electron-proton elastic-scattering cross sections in order to determine the TPE contribution to elastic electron-proton scattering and thereby resolve the proton electric form factor discrepancy. Methods: We produced a mixed simultaneous electron-positron beam in Jefferson Lab\u27s Hall B by passing the 5.6-GeV primary electron beam through a radiator to produce a bremsstrahlung photon beam and then passing the photon beam through a convertor to produce electron-positron pairs. The mixed electron-positron (lepton) beam with useful energies from approximately 0.85 to 3.5 GeV then struck a 30-cm-long liquid hydrogen (LH2) target located within the CEBAF Large Acceptance Spectrometer (CLAS). By detecting both the scattered leptons and the recoiling protons, we identified and reconstructed elastic scattering events and determined the incident lepton energy. A detailed description of the experiment is presented. Results: We present previously unpublished results for the quantity R2γ , the TPE correction to the elastic-scattering cross section, at Q2 ≈ 0.85 and 1.45 GeV2 over a large range of virtual photon polarization ɛ . Conclusions: Our results, along with recently published results from VEPP-3, demonstrate a nonzero contribution from TPE effects and are in excellent agreement with the calculations that include TPE effects and largely reconcile the form-factor discrepancy up to Q2 ≈ 2 GeV2 . These data are consistent with an increase in R2γ with decreasing ɛ at Q2 ≈ 0.85 and 1.45 GeV2 . There are indications of a slight increase in R2γ with Q2

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page