Health professionals' perspective on the promotion of e-mental health apps in the context of maternal depression.

Our study focuses on exploring (1) the intention of health professionals to use and recommend e-mental health applications, (2) how this intention of health professionals might be influenced, (3) which group of health professionals might be most accessible to promote e-mental health applications for maternal depression, and (4) for which tasks they rate them to be most useful. Based on a questionnaire informed by the theory of planned behavior, we collected 131 responses of U.S., Spanish, and Swiss health professionals in the field of pregnancy and maternal care (including psychologists, psychiatrists, midwives, and doctors) by means of an online survey. We analyzed the gathered data applying a structured equation model. Our study reveals that health professionals would in general intend to recommend and use e-mental health applications. However, their attitude towards e-mental health applications varies regarding the respective use cases and also differs among health professions. We offer three alternative propositions for private or public organizations, associations, or any other entity whose purpose is service to the community for introducing e-mental health applications into practice

Search for an anomalous excess of charged-current quasielastic νe interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction

We present a measurement of the νe-interaction rate in the MicroBooNE detector that addresses the observed MiniBooNE anomalous low-energy excess (LEE). The approach taken isolates neutrino interactions consistent with the kinematics of charged-current quasielastic (CCQE) events. The topology of such signal events has a final state with one electron, one proton, and zero mesons (1e1p). Multiple novel techniques are employed to identify a 1e1p final state, including particle identification that use two methods of Deep-Learning-based image identification and event isolation using a boosted decision-tree ensemble trained to recognize two-body scattering kinematics. This analysis selects 25 νe-candidate events in the reconstructed neutrino energy range of 200–1200 MeV, while 29.0±1.9(sys)±5.4(stat) are predicted when using νμ CCQE interactions as a constraint. We use a simplified model to translate the MiniBooNE LEE observation into a prediction for a νe signal in MicroBooNE. A Δχ2 test statistic, based on the combined Neyman–Pearson χ2 formalism, is used to define frequentist confidence intervals for the LEE signal strength. Using this technique, in the case of no LEE signal, we expect this analysis to exclude a normalization factor of 0.75 (0.98) times the median MiniBooNE LEE signal strength at 90% (2σ) confidence level, while the MicroBooNE data yield an exclusion of 0.25 (0.38) times the median MiniBooNE LEE signal strength at 90% (2σ) confidence level

Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation

Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and dQ/dx (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30% for charged-current νe interactions. This pattern recognition achieves 80–90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current νe (νμ) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions

The Impact of the COVID-19 Pandemic on State Court Proceedings: Five Key Findings

The University of Illinois System’s Institute for Government and Public Affairs and the National Center for State Courts jointly conducted the COVID-19 and the State Courts Study between August 2020 and July 2021. The first stage of the study involved focus groups of attorneys, judges, court administrators, court staff, jurors, and litigants in four states. This report describes some results of the second stage of the study, which involved nationwide surveys of judges, court personnel, and attorneys. The surveys asked participants questions about access to courts during the pandemic and their experiences with the new strategies courts adopted to continue hearing and processing cases. This report summarizes five key findings from the surveys concerning access to the courts. • • First, early in the pandemic, most attorneys thought that litigants’ access to judicial proceedings was worse than usual. • Second, attorneys reported that litigants’ experiences in courts improved after September 2020. Over time, participants believed that some early access difficulties abated. • Third, court personnel had a more positive view than did attorneys about the ability of individuals to participate in the judicial system during the pandemic. • Fourth, attorneys with practices concentrated in landlord-tenant law and criminal law perceived somewhat greater problems than did attorneys who practice in other areas of the law. • Fifth, while participants identified many benefits to online court proceedings, they also saw drawbacks. Assessing whether and under what circumstances to conduct court proceedings online after the pandemic is over will require careful consideration of benefits and downsides and balancing some competing factors.Ope

New CC0π GENIE model tune for MicroBooNE

Obtaining a high-quality interaction model with associated uncertainties is essential for neutrino experiments studying oscillations, nuclear scattering processes, or both. As a primary input to the MicroBooNE experiment’s next generation of neutrino cross section measurements and its flagship investigation of the MiniBooNE low-energy excess, we present a new tune of the charged-current pionless (CC0π) interaction cross section via the two major contributing processes—charged-current quasielastic and multinucleon interaction models—within version 3.0.6 of the GENIE neutrino event generator. Parameters in these models are tuned to muon neutrino CC0π cross section data obtained by the T2K experiment, which provides an independent set of neutrino interactions with a neutrino flux in a similar energy range to MicroBooNE’s neutrino beam. Although the fit is to muon neutrino data, the information carries over to electron neutrino simulation because the same underlying models are used in GENIE. A number of novel fit parameters were developed for this work, and the optimal parameters were chosen from existing and new sets. We choose to fit four parameters that have not previously been constrained by theory or data. Thus, this will be called a theory-driven tune. The result is an improved match to the T2K CC0π data with more well-motivated uncertainties based on the fit

Novel approach for evaluating detector-related uncertainties in a LArTPC using MicroBooNE data

Primary challenges for current and future precision neutrino experiments using liquid argon time projection chambers (LArTPCs) include understanding detector effects and quantifying the associated systematic uncertainties. This paper presents a novel technique for assessing and propagating LArTPC detector-related systematic uncertainties. The technique makes modifications to simulation waveforms based on a parameterization of observed differences in ionization signals from the TPC between data and simulation, while remaining insensitive to the details of the detector model. The modifications are then used to quantify the systematic differences in low- and high-level reconstructed quantities. This approach could be applied to future LArTPC detectors, such as those used in SBN and DUNE

Modelling and Analysis of Vibrations in a UAV Helicopter with a Vision System

The analysis of the nature and damping of unwanted vibrations on Unmanned Aerial Vehicle (UAV) helicopters are important tasks when images from on‐board vision systems are to be obtained. In this article, the authors model a UAV system, generate a range of vibrations originating in the main rotor and design a control methodology in order to damp these vibrations. The UAV is modelled using VehicleSim, the vibrations that appear on the fuselage are analysed to study their effects on the on‐board vision system by using Simmechanics software. Following this, the authors present a control method based on an Adaptive Neuro‐Fuzzy Inference System (ANFIS) to achieve satisfactory damping results over the vision system on board

Antenna Technology Shuttle Experiment (ATSE)

Numerous space applications of the future will require mesh deployable antennas of 15 m in diameter or greater for frequencies up to 20 GHz. These applications include mobile communications satellites, orbiting very long baseline interferometry (VLBI) astrophysics missions, and Earth remote sensing missions. A Lockheed wrap rip antennas was used as the test article. The experiments covered a broad range of structural, control, and RF discipline objectives, which is fulfilled in total, would greatly reduce the risk of employing these antenna systems in future space applications. It was concluded that a flight experiment of a relatively large mesh deployable reflector is achievable with no major technological or cost drivers. The test articles and the instrumentation are all within the state of the art and in most cases rely on proven flight hardware. Every effort was made to design the experiments for low cost

Medical Treatment of Radiological Casualties: Current Concepts

The threat of radiologic or nuclear terrorism is increasing, yet many physicians are unfamiliar with basic treatment principles for radiologic casualties. Patients may present for care after a covert radiation exposure, requiring an elevated level of suspicion by the physician. Traditional medical and surgical triage criteria should always take precedence over radiation exposure management or decontamination. External contamination from a radioactive cloud is easily evaluated using a simple Geiger-Müller counter and decontamination accomplished by prompt removal of clothing and traditional showering. Management of surgical conditions in the presence of persistent radioactive contamination should be dealt with in a conventional manner with health physics guidance. To be most effective in the medical management of a terrorist event involving high-level radiation, physicians should understand basic manifestations of the acute radiation syndrome, the available medical countermeasures, and the psychosocial implications of radiation incidents. Health policy considerations include stockpiling strategies, effective use of risk communications, and decisionmaking for shelter-in-place versus evacuation after a radiologic incident