Does organizational performance affect employee turnover? A re‐examination of the turnover–performance relationship

A common problem with using organizational performance as the dependent variable is the ignoring of feedback effect. The current conceptualization of the turnover–performance relationship is mostly unidirectional, focusing on how turnover affects organizational performance. Only a few scholars have investigated the possible reverse relationship between turnover and performance. Aiming to further the research on the feedback effect of organizational performance, this study employed cross‐lagged structural equation models that are especially suitable for modelling the possible reverse relationships between variables. Data were collected from public elementary and middle schools in New York City over a three‐year period. The results consistently show that organizational performance was negatively related to subsequent employee turnover. This research contributes to the development of a more valid and comprehensive understanding of the relationship between employee turnover and organizational performance.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154640/1/padm12648_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154640/2/padm12648.pd

Studying dawn-dusk asymmetries of Mercury's magnetotail using MHD-EPIC simulations

MESSENGER has observed a lot of dawn-dusk asymmetries in Mercury's magnetotail, such as the asymmetries of the cross-tail current sheet thickness and the occurrence of flux ropes, dipolarization events and energetic electron injections. In order to obtain a global pictures of Mercury's magnetotail dynamics and the relationship between these asymmetries, we perform global simulations with the magnetohydrodynamics with embedded particle-in-cell (MHD-EPIC) model, where Mercury's magnetotail region is covered by a PIC code. Our simulations show that the dawnside current sheet is thicker, the plasma density is larger, and the electron pressure is higher than the duskside. Under a strong IMF driver, the simulated reconnection sites prefer the dawnside. We also found the dipolarization events and the planetward electron jets are moving dawnward while they are moving towards the planet, so that almost all dipolarization events and high-speed plasma flows concentrate in the dawn sector. The simulation results are consistent with MESSENGER observations

A novel deflection shape function for rectangular capacitive micromachined ultrasonic transducer diaphragms

The final publication is available at Elsevier via http://dx.doi.org/10.2174/1874347101206010001. © 2015. This version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A highly accurate analytical deflection shape function that describes the deflection profiles of capacitive micromachined ultrasonic transducers (CMUTs) with rectangular membranes under electrostatic pressure has been formulated. The rectangular diaphragms have a thickness range of 0.6–1.5 μm and a side length range of 100–1000 μm. The new deflection shape function generates deflection profiles that are in excellent agreement with finite element analysis (FEA) results for a wide range of geometry dimensions and loading conditions. The deflection shape function is used to analyze membrane deformations and to calculate the capacitances between the deformed membranes and the fixed back plates. In 50 groups of random tests, compared with FEA results, the calculated capacitance values have a maximum deviation of 1.486% for rectangular membranes. The new analytical deflection function can provide designers with a simple way of gaining insight into the effects of designed parameters for CMUTs and other MEMS-based capacitive type sensors.National Basic Research Program of China under Grant 2014CB845302 and by National Natural Science Foundation (NNSF) of China under Grants 61374036, 61273121, and Natural Science Foundation of Guangdong Province under Grant 2014A030313237, and by Natural Science and Engineering Research Council of Canada

SINCO: A Novel structural regularizer for image compression using implicit neural representations

Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression. An image can be compressed by training an INR model with fewer weights than the number of image pixels to map the coordinates of the image to corresponding pixel values. While traditional training approaches for INRs are based on enforcing pixel-wise image consistency, we propose to further improve image quality by using a new structural regularizer. We present structural regularization for INR compression (SINCO) as a novel INR method for image compression. SINCO imposes structural consistency of the compressed images to the groundtruth by using a segmentation network to penalize the discrepancy of segmentation masks predicted from compressed images. We validate SINCO on brain MRI images by showing that it can achieve better performance than some recent INR methods

EFFECTS OF SHOD AND BAREFOOT RUNNING ON THE IN VIVO KINEMATICS OF THE FIRST METATARSOPHALANGEAL JOINT

The purpose of this study is to investigate the differences of the first metatarsophalangeal joint’s 6 degree-of-freedom (6DOF) kinematics during shod and barefoot conditions by using a high-speed dual fluoroscopic imaging system (DFIS). Fifteen healthy male runners were recruited. Computed tomography (CT) scans were taken of each participant’s right foot for the construction of 3D models and local coordinate system. The fluoroscopic images of the right foot during the stance period were acquired under shod and barefoot condition with rearfoot strike pattern Radiographic images were acquired at 100 Hz while the participants ran at a speed of 3±5% m/s in a track and 6DOF kinematics were calculated by 2D-3D registration. Paired sample t-test was used to compare the kinematic characteristics of the first MTPJ 6DOF kinematics between shod and barefoot. Compared with barefoot, wearing shoes 1) decreased the peak medial, posterior, and superior translation of the first MTPJ during stance (P ＜ 0.05); 2) decreased maximum extension angle, minimum extension angle, and flexion/extension range of motion of the first MTPJ during stance (P ＜ 0.05); 3) increased minimum adduction angle of the first MTPJ during stance (P ＜ 0.05). It suggests that shoes may affect the function of the first MTPJ and increase the risk of hallux valgus. Our study makes up for the deficiency of traditional motion measurement methods that only focus on the sagittal flexion and extension movement of the first MTPJ and provides a more comprehensive understanding of the potential relationship between joint motion and injurie

EFFECTS OF SHOD AND BAREFOOT CONDITIONS ON MEDIAL LONGITUDINAL ARCH ANGLE DURING RUNNING

The structure of the medial longitudinal arch (MLA) affects the spring-like function of the foot and is crucial to running performance. The purpose of this study was to investigate the differences in the MLA angle between barefoot and shod conditions by using a high-speed dual fluoroscopic imaging system (DFIS). Computed tomography was taken of each participant’s right foot for the construction of 3D models and local coordinate systems. Fifteen participants ran with or without running shoes at 3 m/s±5% speed. We recorded foot kinematics using DFIS. After the process of 3D-2D registration, MLA angles were calculated. Compared to barefoot, wearing shoes 1) decreased the initial landing MLA angle, maximum MLA angle and range of motion of the MLA angle (p \u3c 0.05); 2) decreased the MLA angles during 0%-70% of the stance phase (p \u3c 0.05). It suggests that shoes limit the MLA compression and recoil and its spring-like function

A Plug-and-Play Image Registration Network

Deformable image registration (DIR) is an active research topic in biomedical imaging. There is a growing interest in developing DIR methods based on deep learning (DL). A traditional DL approach to DIR is based on training a convolutional neural network (CNN) to estimate the registration field between two input images. While conceptually simple, this approach comes with a limitation that it exclusively relies on a pre-trained CNN without explicitly enforcing fidelity between the registered image and the reference. We present plug-and-play image registration network (PIRATE) as a new DIR method that addresses this issue by integrating an explicit data-fidelity penalty and a CNN prior. PIRATE pre-trains a CNN denoiser on the registration field and "plugs" it into an iterative method as a regularizer. We additionally present PIRATE+ that fine-tunes the CNN prior in PIRATE using deep equilibrium models (DEQ). PIRATE+ interprets the fixed-point iteration of PIRATE as a network with effectively infinite layers and then trains the resulting network end-to-end, enabling it to learn more task-specific information and boosting its performance. Our numerical results on OASIS and CANDI datasets show that our methods achieve state-of-the-art performance on DIR

Adaptive output regulation for a class of nonlinear systems with guaranteed transient performance

This paper is dedicated to adaptive output regulation for a class of nonlinear systems with asymptotic output tracking and guarantee of prescribed transient performance. With the employment of internal model principle, we first transform this problem into a specific adaptive stabilization problem with output constraints. Then, by integrating the time-varying Barrier Lyapunov Function (BLF) technique together with the high gain feedback method, we develop an output-based control law to solve the constrained stabilization problem and consequently confine the output tracking error to a predefined arbitrary region. The output-based control law enables adaptive output regulation in the sense that, under unknown exosystem dynamics, all the closed-loop system signals are bounded whilst the controlled output constraints are not violated. Finally, efficacy of the proposed design is illustrated through a simulation example