Few-layer transition metal dichalcogenides (TMDs) and their combination as van der Waals heterostructures provide a promising platform for high-performance optoelectronic devices. However, the ultrathin thickness of TMD flakes limits efficient light trapping and absorption, which triggers the hybrid construction with optical resonant cavities for enhanced light absorption. The optical structure enriched photodetectors can also be wavelength-and polarization-sensitive but require complicated fabrication. Herein, a new-type TMD-based photodetector embedded with nanoslits is proposed to enhance light trapping. Taking ReS 2 as an example, strong anisotropic Mie-type optical responses arising from the intrinsic in-plane anisotropy and nanoslit-enhanced anisotropy are discovered. Owing to the nanoslit-enhanced optical resonances and band engineering, excellent photodetection performances are demonstrated with high responsivity of 27 A W −1 and short rise/decay times of 3.7/3.7 ms. More importantly, through controlling the angle between the nanoslit orientation and the polarization direction to excite different resonant modes, polarization-sensitive photodetectors with anisotropy ratios from 5.9 to 12.6 can be achieved, representing one of the most polarization-sensitive TMD-based photodetectors. The depth and orientation of nanoslits are demonstrated crucial for optimizing the anisotropy ratio. The findings bring an effective scheme to construct high-performance and polarization-sensitive photodetectors.
Although knowledge is arguably an organization’s most important resource, many organizations still practice knowledge hiding. This study explores how an organization’s motivational climate—mediated by work alienation among its members—influences knowledge hiding from the perspective of the conservation of resources (COR) theory. Specifically, we establish hypotheses that the performance and mastery climates, mediated by work alienation, have positive and negative effects on knowledge hiding, respectively. To verify these hypotheses, we conducted a survey among members of Chinese companies, through which 200 responses were collected through a two-wave panel design. The results of the analysis demonstrated that motivational climate, as an antecedent of knowledge hiding, has a significant effect on work alienation. We also found that work alienation mediated the relationship between (a) performance climate, and (b) mastery climate and knowledge hiding. Based on these findings, we discuss the research implications and limitations while suggesting directions for future studies.
Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles (IoV). Mixture models are appropriate to describe complex spatial-temporal data. By calculating the expectation of hidden variables in vehicle communication, Expectation Maximization (EM) algorithm solves the maximum likelihood estimation of parameters, and then obtains the mixture model of vehicle communication opportunities. However, the EM algorithm requires multiple iterations and each iteration needs to process all the data. Thus its computational complexity is high. A parameter estimation algorithm with low computational complexity based on Bin Count (BC) and Differential Evolution (DE) (PEBCDE) is proposed. It overcomes the disadvantages of the EM algorithm in solving mixture models for big data. In order to reduce the computational complexity of the mixture models in the IoV, massive data are divided into relatively few time intervals and then counted. According to these few counted values, the parameters of the mixture model are obtained by using DE algorithm. Through modeling and analysis of simulation data and instance data, the PEBCDE algorithm is verified and discussed from two aspects, i.e., accuracy and efficiency. The numerical solution of the probability distribution parameters is obtained, which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.
The high computational complexity of H.264 poses as a challenge for it's feasibility in power-constrained devices. In this paper, the key modules of the encoder which can be power-optimized are analyzed at first. Based on the analysis results, a novel computation resource allocation method is proposed. The algorithm can allocate the computation resource of the encoder adaptive to available battery power and video contents. Experimental results show the proposed algorithm can highly reduce the computation resource while maintaining video encoding quality.
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