Assessing the carry-over effects of both human capital and organizational forgetting on sustainability performance using dynamic data envelopment analysis

Many studies have documented that human capital, which is a result of professional knowledge accumulation, continuously improves sustainability performance over time. Organizational forgetting is the loss of such professional knowledge, and it results in lower sustainability performance. Thus, human capital and organizational forgetting can be respectively treated as good and bad carry-overs. Both human capital and organizational forgetting may reflect business cycle fluctuations. The data envelopment analysis model has not been employed to examine the impact of either human capital or organizational forgetting on sustainability performance in multi-stages. The aim of this study is to develop a three-stage approach to incorporate the carry-over effects of both human capital and organizational forgetting and the effects of business cycle fluctuations on overall and term sustainability performance using data from Taiwan’s 16 major industrial sectors. The study finds that the carry-over effects of human capital and organizational forgetting lead to accurate estimations of sustainability performance and illustrates that the development of the industrial economy is a critical factor for adjusting human capital. Governments should implement economic stabilization policies and increase investment in education and safe capital to improve human capital accumulation and enhance sustainability performance

Using categorical DEA to assess the effect of subsidy policies and technological learning on R&D efficiency of it industry

Government subsidies are an important policy tool that can help firms develop technological learning, and this technological learning effect plays a key role in firms’ research and development (R&D) efficiency. Thus, this study develops a two-stage approach to illustrate the effect of subsidy policies and technological learning on R&D efficiency in the information technology (IT) industry. The technological learning effect in 128 firms in the IT industry from 2008 to 2015 was measured using the learning experience curve. Subsequently, government R&D subsidy intensity was considered as a categorical variable, and this estimated result was treated as an intangible input into a data envelopment analysis (DEA) structure to evaluate R&D efficiency in 2015. This study makes three major contributions. First, the developed approach incorporates the effect of subsidy policies and technological learning into the DEA structure. Second, the empirical results demonstrate the appropriateness of incorporating subsidy policies and technological learning into evaluations of R&D efficiency. Finally, our results identify the key sources of inefficiency as a shortfall in the number of patents and a lack of technological learning. Based on these key findings, some improved strategies were recommended to decision makers. First published online 19 November 201

An efficient surrogate model for emulation and physics extraction of large eddy simulations

In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations and statistical modeling. In this paper, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as {simplifying assumptions} for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations.Comment: Submitted to JASA A&C

Single-crystalline δ-Ni2Si nanowires with excellent physical properties

[[abstract]]In this article, we report the synthesis of single-crystalline nickel silicide nanowires (NWs) via chemical vapor deposition method using NiCl2·6H2O as a single-source precursor. Various morphologies of δ-Ni2Si NWs were successfully acquired by controlling the growth conditions. The growth mechanism of the δ-Ni2Si NWs was thoroughly discussed and identified with microscopy studies. Field emission measurements show a low turn-on field (4.12 V/μm), and magnetic property measurements show a classic ferromagnetic characteristic, which demonstrates promising potential applications for field emitters, magnetic storage, and biological cell separation.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]電子版[[booktype]]紙