Quitting the Boss? The Role of Manager Influence Tactics and Employee Emotional Engagement in Voluntary Turnover

Employees commonly cite their managers’ behavior as the primary reason for quitting their jobs. We sought to extend turnover research by investigating whether two commonly used influence tactics by managers affect their employees’ voluntary turnover and whether employees’ emotional engagement and job satisfaction mediate this relationship. We tested our hypotheses using survey data collected at two time points from a sample of financial services directors and objective lagged turnover data. Using multilevel path modeling, we found that managers’ use of pressure and inspirational appeals had opposite effects on employee voluntary turnover and that employees’ emotional engagement was a significant and unique mediating mechanism even when job satisfaction, the traditional attitudinal predictor of turnover, was also included in the path model. Our findings contribute to turnover research by demonstrating a relationship between specific managerial behaviors and employee turnover and shed light on a key mediating mechanism that explains these effects

Construction of the Racist Republican

Minorities have gained more civil rights with the cooperation of both major political parties in the United States, yet the actions of the Republican Party are often conflated with racism. This is partially the result of clashes in ideological visions, which explain the different political positions of partisans. However, during his 1980 run for the White House, a concerted effort was made to tie Ronald Reagan to racism, as he was accused of pandering to white Southerners. Therefore, this thesis also focuses on “Southern strategies” used by both the Republican and Democratic parties to exploit race, which have spilled into the new millennium

Big Data Analytics in the Internet-Of-Things And Cyber-Physical Systems

Lv, Z.; Song, H.; Lloret, J.; Kim, D.; De Souza, J. (2019). Big Data Analytics in the Internet-Of-Things And Cyber-Physical Systems. IEEE Access. 7:18070-18075. https://doi.org/10.1109/ACCESS.2019.2895441S1807018075

The Extended-window Channel Estimator For Iterative Channel-and-symbol Estimation

The application of the expectation-maximization (EM) algorithm to channel estimation results in a well-known iterative channel-and-symbol estimator (ICSE). The EM-ICSE iterates between a symbol estimator based on the forward-backward recursion (BCJR equalizer) and a channel estimator, and may provide approximate maximum-likelihood blind or semiblind channel estimates. Nevertheless, the EM-ICSE has high complexity, and it is prone to misconvergence. In this paper, we propose the extended-window (EW) estimator, a novel channel estimator for ICSE that can be used with any soft-output symbol estimator. Therefore, the symbol estimator may be chosen according to performance or complexity specifications. We show that the EW-ICSE, an ICSE that uses the EW estimator and the BCJR equalizer, is less complex and less susceptible to misconvergence than the EM-ICSE. Simulation results reveal that the EW-ICSE may converge faster than the EM-ICSE. © 2005 Hindawi Publishing Corporation.200529299Barry, J.R., Lee, E.A., Messerschmitt, D.G., (2003) Digital Communications, , Kluwer Academic Publishers, Norwell, Mass, USA, 3rd editionAyadi, J., De Carvalho, E., Slock, D.T.M., Blind and semi-blind maximum likelihood methods for FIR multichannel identification (1998) Proc. IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP'98), 6, pp. 3185-3188. , Seattle, Wash, USA, MayFeder, M., Catipovic, J.A., Algorithms for joint channel estimation and data recovery-application to equalization in underwater communications (1991) IEEE J. Oceanic Eng., 16 (1), pp. 42-55Kaleh, G.K., Vallet, R., Joint parameter estimation and symbol detection for linear or nonlinear unknown channels (1994) IEEE Trans. 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Biomechanical analysis of posterior lumbar interbody aages

A popular yet effective surgical treatment used in medical field to treat degenerative disc disease related to low back pain is known as the lumbar interbody fusion procedure. One of the main aims of posterior lumbar interbody fusion (PLIF), using cages or bone grafts are to provide stability during body movements and mechanical support between two vertebrae, maintain disc height and to help out with the fusion process. This procedure restores disc height, enlarges the stenotic foramen, stabilises the spine, and provides mechanical strength between vertebrae

Functional Magnetic Resonance Imaging of Motor Cortex Activation in Schizophrenia

Previous fMRI studies of sensorimotor activation in schizophrenia have found in some cases hypoactivity, no difference, or hyperactivity when comparing patients with controls; similar disagreement exists in studies of motor laterality. In this multi-site fMRI study of a sensorimotor task in individuals with chronic schizophrenia and matched healthy controls, subjects responded with a right-handed finger press to an irregularly flashing visual checker board. The analysis includes eighty-five subjects with schizophrenia diagnosed according to the DSM-IV criteria and eighty-six healthy volunteer subjects. Voxel-wise statistical parametric maps were generated for each subject and analyzed for group differences; the percent Blood Oxygenation Level Dependent (BOLD) signal changes were also calculated over predefined anatomical regions of the primary sensory, motor, and visual cortex. Both healthy controls and subjects with schizophrenia showed strongly lateralized activation in the precentral gyrus, inferior frontal gyrus, and inferior parietal lobule, and strong activations in the visual cortex. There were no significant differences between subjects with schizophrenia and controls in this multi-site fMRI study. Furthermore, there was no significant difference in laterality found between healthy controls and schizophrenic subjects. This study can serve as a baseline measurement of schizophrenic dysfunction in other cognitive processes

Working memory deficits in adults with ADHD: is there evidence for subtype differences?

BACKGROUND: Working memory performance is important for maintaining functioning in cognitive, academic and social activities. Previous research suggests there are prevalent working memory deficits in children with attention deficit hyperactivity disorder (ADHD). There is now a growing body of literature characterizing working memory functioning according to ADHD subtypes in children. The expression of working memory deficits in adults with ADHD and how they vary according to subtype, however, remains to be more fully documented. METHODS: This study assessed differences in working memory functioning between Normal Control (NC) adults (N = 18); patients with ADHD, Combined (ADHD-CT) Type ADHD (N = 17); and ADHD, Inattentive (ADHD-IA) Type (N = 16) using subtests from the Wechsler Adult Intelligence Scale-III and Wechsler Memory Scale-III and the Paced Auditory Serial Addition Task (PASAT). RESULTS: The ADHD groups displayed significant weaknesses in contrast to the NC group on working memory tests requiring rapid processing and active stimulus manipulation. This included the Letter-Number-Sequencing test of the Wechsler scales, PASAT omission errors and the longest sequence of consecutive correct answers on the PASAT. No overall ADHD group subtype differences emerged; however differences between the ADHD groups and the NC group varied depending on the measure and the gender of the participants. Gender differences in performance were evident on some measures of working memory, regardless of group, with males performing better than females. CONCLUSION: In general, the data support a dimensional interpretation of working memory deficits experienced by the ADHD-CT and ADHD-IA subtypes, rather than an absolute difference between subtypes. Future studies should test the effects of processing speed and load on subtype performance and how those variables interact with gender in adults with ADHD

Low-complexity soft-decision feedback turbo equalization for multilevel modulations

This dissertation proposes two new decision feedback equalization schemes suitable for multilevel modulation systems employing turbo equalization. One is soft-decision feedback equalization (SDFE) that takes into account the reliability of both soft a priori information and soft decisions of the data symbols. The proposed SDFE exhibits lower signal to noise ratio (SNR) threshold that is required for water fall bit error rate (BER) and much faster convergence than the near-optimal exact minimum mean square error linear equalizer (Exact-MMSE-LE) for high-order constellation modulations. The proposed SDFE also offers a low computational complexity compared to the Exact-MMSE-LE. The drawback of the SDFE is that its coefficients cannot reach the matched filter bound (MFB) and therefore after a large number of iterations (e.g. 10), its performance becomes inferior to that of the Exact-MMSE-LE. Therefore, soft feedback intersymbol interference (ISI) canceller-based (SIC) structure is investigated. The SIC structure not only exhibits the same low complexity, low SNR threshold and fast convergence as the SDFE but also reaches the MFB after a large number of iterations. Both theoretical analysis and numerical simulations demonstrate why the SIC achieves MFB while the SDFE cannot. These two turbo equalization structures are also extended from single-input single-output (SISO) systems to multiple-input multiple-output (MIMO) systems and applied in high data-rate underwater acoustic (UWA) communications --Abstract, page iv