Measuring the Impact of Operational Stress: The Relevance of Assessing Stress-related Health Across the Deployment Cycle

ABSTRACT Introduction Mental health issues from intense or prolonged stress are a common concern in regard to military deployment. Deployments can objectively vary in stress exposure, but it is the individuals’ perception of that stress that affects sustainability, mental health, and combat fitness, which calls for the need of a protocol to evaluate and maintain a current estimation of stress impact. So, how can we assess the impact of stressors during different phases of deployment? Materials and Methods We used three psychological self-rating forms, the PSS14—Perceived Stress Scale, SMBM—Shirom Melamed Burnout Measure, and KSQ—Karolinska Sleep Questionnaire, to measure the impact of stress before (T1), during (T2), and at homecoming (T3). We also wanted to see if T1 or T2 results could predict T3 results to be able to better prepare the homecoming program.The forms were handed out to Swedish soldiers deployed in Mali in 2017. The forms were collected as a way to assess the status of the mental health load at three timepoints based on the personnel function as a way to assess the current “psychological fitness level”. Results The results show that stress measured using PSS14 was high at homecoming. The same result was observed for SMBM. No measures from T1 or T2 could however predict the T3 results. Conclusions Taken together, we found that screening of all contingent staff is relatively easy and provides personnel with relevant data on mental health and stress at the current time. We also found that test results correlated between T1 and T2 but not with T3. This indicates that there might be different stressors that affect staff at different timepoints.

Temperature-field-angle dependent critical current estimation of commercial second generation high temperature superconducting conductor using double hidden layer Bayesian regularized neural network

We estimated the critical current of the second generation (2G) high temperature superconducting (HTS) conductor using neural network fitting methods. The critical current of 2G HTS conductors depends on magnetic field strength and angle as well as on temperature, Ic(T, B, θ). Moreover, the critical current values vary for 2G HTS conductors from different manufacturers. In this study, we addressed three challenging issues in critical current assessment by neural network fitting methods, namely 90-degree asymmetry, a wide range of temperature-field-angle dependence, and different manufacturer conductor differences. Prediction models for three commercial HTS conductors were trained and evaluated by convergence, accuracy, and robustness. The linear regression correlation coefficient R was approximately equal to 1 for the three models. The critical current estimation obtained from the proposed method was compared with the critical current estimation from the interpolation method at different fixed temperatures using a multi-width no-insulation magnet. The model computation speed was also discussed. The proposed model needed only 2.7 s to compute 10 million data sets. Therefore, the convergence, accuracy, reliability, and speed of the proposed method prove that it can be used in a wide range of industrial applications and academic fields.

Control Strategy Research of Based on Resonance Feedforward and Current Estimate of the Photovoltaic Grid Inverter

Due to poor quality of grid-connected current and large impedance value of grid are caused in weak grid environment. Therefore, a control strategy combining multiple resonant feedforward and current estimation method is adopted in this paper. Firstly, in order to make the positive feedback channel have the feedback effect only at the main background harmonics, the low-order harmonics of the grid are extracted by using the method of multi-resonance feedforward control. At the same time, under the premise that the suppression effect of LCL natural resonant frequency remains unchanged, the current estimation method is added into the control strategy, so as to reduce the system cost. Finally, the simulation results show that thus verifying the correctness and effectiveness of the control strategy.

Overlooked current estimation biases arising from the Lagrangian Argo trajectory derivation method

An Argo simulation system is used to provide synthetic Lagrangian trajectories based on the Estimating the Circulation and Climate of the Ocean model, Phase II (ECCO2). In combination with ambient Eulerian velocity at the reference layer (1000 m) from the model, quantitative metrics of the Lagrangian trajectory-derived velocities are computed. The result indicates that the biases induced by the derivation algorithm are strongly linked with ocean dynamics. In low latitudes, Ekman currents and vertically sheared geostrophic currents influence both the magnitude and the direction of the derivation velocity vectors. The maximal shear-induced biases exist near the equator with the amplitudes reaching up to about 1.2 cm s-1. The angles of the shear biases are pronounced in the low latitude oceans, ranging from -8° to 8°. Specifically, the study shows an overlooked bias from the float drifting motions that mainly occurs in the western boundary current and Antarctic circumpolar current (ACC) regions. In these regions, a recently reported horizontal acceleration measured via Lagrangian floats is significantly associated with the strong eddy-jet interactions. The acceleration could induce an overestimation of Eulerian current velocity magnitudes. For the common Argo floats with a 9-day float parking period, the derivation speed biases induced by velocity acceleration would be as large as 3 cm s-1, approximately 12% of the ambient velocity. It might have implications to map the mean mid-depth ocean currents from Argo trajectories, as well as understand the dynamics of eddy-jet interactions in the ocean.

Vector Current Measurement Using Doppler Scatterometry with Optimally Selected Observation Azimuths

The Doppler scatterometer is a new style of remote sensing tool that can provide current measurements over a wide swath for rapid global coverage. The existing current estimation method for Doppler scatterometry uses the maximum likelihood method to jointly derive the wind and current fields but shows high computational complexity. Moreover, the current radial speeds measured along two arbitrary observation azimuths are used to derive the vector current according to the parallelogram rule, which is not applicable for the case where two observation azimuths are not perpendicular. In this paper, a vector current velocity inversion method using an optimally selected observation azimuth combination—as well as a general current velocity calculation method—is proposed for Doppler scatterometry. Firstly, current radial speeds along several different observation azimuths are estimated using an interferometric phase difference matching method with low computational complexity. Then, two current radial components of each point are arbitrarily selected to estimate a preliminary current direction using the proposed vector current velocity derivation method. Finally, two observation azimuths that have the smallest intersection angles with the preliminarily estimated current direction are selected for vector current velocity determination. With the Ocean Surface Current Analyses Real-time (OSCAR) data as current input, vector current estimation experiments were conducted based on simulation analysis using an instrument conceptual design model for a pencil-beam scatterometer. The results show that the standard deviation of the estimated current velocity magnitude is 0.06 m/s. Compared with the reported results obtained by the existing method, the inversion accuracy of velocity magnitude is improved by 67%.

State price density estimation with an application to the recovery theorem

This article introduces a model to estimate the risk-neutral density of stock prices derived from option prices. To estimate a complete risk-neutral density, current estimation techniques use a single mathematical model to interpolate option prices on two dimensions: strike price and time-to-maturity. Instead, this model uses B-splines with at-the-money knots for the strike price interpolation and a mixed lognormal function that depends on the option expiration horizon for the time-to-maturity interpolation. The results of this “hybrid” methodology are significantly better than other risk-neutral density extrapolation methods when applied to the recovery theorem.

An Efficiency Analysis of Conventional and Islamic Banking in Pakistan

The current estimation is involved to regulate the efficiency analysis between Islamic banking and conventional banking by reference to GDP growth, total reserve as dependent variables, and total assets, total deposits, total investment, and total liabilities as autonomous variables. Time series data is collected from 1990 to 2019, Islamic banking has increased facts and lending in Pakistan. Most people believe in Islamic banking philosophy as it is Islamic shariah based with full reserves, total assets, total deposits, total investment, and total liabilities, Considered the descriptive variables independent variable in which the coefficient of the total assets, the total deposit had a highly significant impact on GDP growth rate. Pakistan had a dynamic role regarding the banking sector when assets of the banking sector increases and GDP Growth also increased in Pakistan. Autoregressive lagged estimation model and economic effectiveness on the supply side and Islamic banking relationship in Pakistan, GDP growth is positive and significant.