Exploring the Relationship of Ethical Leadership with Job Satisfaction, Organizational Commitment, and Organizational Citizenship Behavior

The impact of ethics on recent leadership practices has assumed a prominent role in both practical and theoretical discussions of organizational leadership successes and failures. A leader\u27s ability to affect followers\u27 attitudes and behaviors is important in this pursuit because it can result in greater job performance (Tanner, Brugger, Van Schie, & Lebherz, 2010). Ethical leadership may provide an effective approach for fostering positive employee outlooks and actions. Employees respond positively to the ethical leader\u27s principled leadership, altruism, empowerment, and reward systems, suggesting that improved employee attitudes and work-related behaviors may follow (Brown & Trevino, 2006). Three established measures of attitudes and behaviors are employee job satisfaction, organizational commitment, and organizational citizenship behavior. The following research study examined the potential of ethical leadership to foster higher levels of these outcomes and found that employees led by highly ethical leaders reported greater job satisfaction and organizational commitment than did employees led by less ethical leaders. No significant difference was reported among employees regarding the impact of ethical leadership on their level of organizational citizenship behavior. These findings suggest both theoretical and practitioner level insights

Investigating Retrieval Method Selection with Axiomatic Features

We consider algorithm selection in the context of ad-hoc information retrieval. Given a query and a pair of retrieval methods, we propose a meta-learner that predicts how to combine the methods' relevance scores into an overall relevance score. Inspired by neural models' different properties with regard to IR axioms, these predictions are based on features that quantify axiom-related properties of the query and its top ranked documents. We conduct an evaluation on TREC Web Track data and find that the meta-learner often significantly improves over the individual methods. Finally, we conduct feature and query weight analyses to investigate the meta-learner's behavior

The auxiliary region method: A hybrid method for coupling PDE- and Brownian-based dynamics for reaction-diffusion systems

Reaction-diffusion systems are used to represent many biological and physical phenomena. They model the random motion of particles (diffusion) and interactions between them (reactions). Such systems can be modelled at multiple scales with varying degrees of accuracy and computational efficiency. When representing genuinely multiscale phenomena, fine-scale models can be prohibitively expensive, whereas coarser models, although cheaper, often lack sufficient detail to accurately represent the phenomenon at hand. Spatial hybrid methods couple two or more of these representations in order to improve efficiency without compromising accuracy. In this paper, we present a novel spatial hybrid method, which we call the auxiliary region method (ARM), which couples PDE and Brownian-based representations of reaction-diffusion systems. Numerical PDE solutions on one side of an interface are coupled to Brownian-based dynamics on the other side using compartment-based "auxiliary regions". We demonstrate that the hybrid method is able to simulate reaction-diffusion dynamics for a number of different test problems with high accuracy. Further, we undertake error analysis on the ARM which demonstrates that it is robust to changes in the free parameters in the model, where previous coupling algorithms are not. In particular, we envisage that the method will be applicable for a wide range of spatial multi-scales problems including, filopodial dynamics, intracellular signalling, embryogenesis and travelling wave phenomena.Comment: 29 pages, 14 figures, 2 table

Interferograms, Schlieren, and Shadowgraphs Constructed from Real- and Ideal-Gas, Two- and Three-Dimensional Computed Flowfields

The construction of interferograms, schlieren, and shadowgraphs from computed flowfield solutions permits one-to-one comparisons of computed and experimental results. A method for constructing these images from both ideal- and real-gas, two- and three-dimensional computed flowfields is described. The computational grids can be structured or unstructured, and multiple grids are an option. Constructed images are shown for several types of computed flows including nozzle, wake, and reacting flows; comparisons to experimental images are also shown. In addition, the sensitivity of these images to errors in the flowfield solution is demonstrated, and the constructed images can be used to identify problem areas in the computations

Look before you leap: a confidence-based method for selecting species criticality while avoiding negative populations in τ\tau-leaping

The stochastic simulation algorithm was introduced by Gillespie and in a different form by Kurtz. There have been many attempts at accelerating the algorithm without deviating from the behavior of the simulated system. The crux of the explicit $\tau$-leaping procedure is the use of Poisson random variables to approximate the number of occurrences of each type of reaction event during a carefully selected time period, $\tau$. This method is acceptable providing the leap condition, that no propensity function changes “significantly” during any time-step, is met. Using this method there is a possibility that species numbers can, artificially, become negative. Several recent papers have demonstrated methods that avoid this situation. One such method classifies, as critical, those reactions in danger of sending species populations negative. At most, one of these critical reactions is allowed to occur in the next time-step. We argue that the criticality of a reactant species and its dependent reaction channels should be related to the probability of the species number becoming negative. This way only reactions that, if fired, produce a high probability of driving a reactant population negative are labeled critical. The number of firings of more reaction channels can be approximated using Poisson random variables thus speeding up the simulation while maintaining the accuracy. In implementing this revised method of criticality selection we make use of the probability distribution from which the random variable describing the change in species number is drawn. We give several numerical examples to demonstrate the effectiveness of our new metho

The pseudo-compartment method for coupling PDE and compartment-based models of diffusion

Spatial reaction-diffusion models have been employed to describe many emergent phenomena in biological systems. The modelling technique most commonly adopted in the literature implements systems of partial differential equations (PDEs), which assumes there are sufficient densities of particles that a continuum approximation is valid. However, due to recent advances in computational power, the simulation, and therefore postulation, of computationally intensive individual-based models has become a popular way to investigate the effects of noise in reaction-diffusion systems in which regions of low copy numbers exist. The stochastic models with which we shall be concerned in this manuscript are referred to as `compartment-based'. These models are characterised by a discretisation of the computational domain into a grid/lattice of `compartments'. Within each compartment particles are assumed to be well-mixed and are permitted to react with other particles within their compartment or to transfer between neighbouring compartments. We develop two hybrid algorithms in which a PDE is coupled to a compartment-based model. Rather than attempting to balance average fluxes, our algorithms answer a more fundamental question: `how are individual particles transported between the vastly different model descriptions?' First, we present an algorithm derived by carefully re-defining the continuous PDE concentration as a probability distribution. Whilst this first algorithm shows strong convergence to analytic solutions of test problems, it can be cumbersome to simulate. Our second algorithm is a simplified and more efficient implementation of the first, it is derived in the continuum limit over the PDE region alone. We test our hybrid methods for functionality and accuracy in a variety of different scenarios by comparing the averaged simulations to analytic solutions of PDEs for mean concentrations.Comment: MAIN - 24 pages, 10 figures, 1 supplementary file - 3 pages, 2 figure

Scanning Raman spectroscopy for characterizing compositionally spread films

Composition-spread La1-xSrxMnO3 thin films were prepared by pulsed laser deposition technique from LaMnO3 and SrMnO3 targets. The films were epitaxial with a continuous variation of the out-of-plane lattice parameter along the direction of composition gradient. Scanning Raman spectroscopy has been employed as a nondestructive tool to characterize the composition-spread films. Raman spectra showed the variation of the structural, Jahn Teller distortions and the presence of coexisting phases at particular compositions that are in agreement with the previous observation on the single-crystal samples. Raman spectra on the continuous composition-spread film also reveal the effect of disorder and strain on the compositions