434 research outputs found
A computer program of master sintering curve model to accurately predict sintering results
No full textConsiderations of dental treatment for patient with congenital heart disease: A Case Report.
No full textSurface plasmon enhanced energy transfer between type I CdSe/ZnS and type II CdSe/ZnTe quantum dots
No full textApplication of the EM-algorithm for Bayesian Network Modelling to Improve Forest Growth Estimates
Full text linkAbstractLeaf area index (LAI) is a biophysical variable that is related to atmosphere-biosphere exchange of CO2. One way to obtain LAI value is by the Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical products (LAI MODIS). The LAI MODIS has been used to improve the physiological principles predicting growth (3-PG) model within a Bayesian Network (BN) set-up. The MODIS time series, however, contains gaps caused by persistent clouds, cloud contamination, and other retrieval problems. We therefore formulated the EM-algorithm to estimate the missing MODIS LAI values. The EM-algorithm is applied to three different cases: successive and not successive two winter seasons, and not successive missing MODIS LAI during the time study of 26 successive months at which the performance of the BN is assessed. Results show that the MODIS LAI is estimated such that the maximum value of the mean absolute error between the original MODIS LAI and the estimated MODIS LAI by EM-algorithm is 0.16. This is a low value, and shows the success of our approach. Moreover, the BN output improves when the EM-algorithm is carried out to estimate the inconsecutive missing MODIS LAI such that the root mean square error reduces from 1.57 to 1.49. We conclude that the EM-algorithm within a BN can handle the missing MODIS LAI values and that it improves estimation of the LAI
The Determinants of Equity Compensation for Audit Committee Members
No full text由於權益薪酬之使用漸趨廣泛，其相關討論增加，再加上審計委員會職責逐漸加重等因素，促使本研究欲找出哪些因素使得公司給予其審計委員會成員權益薪酬。研究發現代理問題較嚴重之公司傾向不給予權益薪酬。薪酬委員會中成員同時為審計委員會成員之比例則與給予權益薪酬之可能性呈顯著正向關係。而審計委員會中董事同時為其他公司之高階主管之比例越高則公司傾向不給予權益薪酬。本研究期能對審計委員會成員權益薪酬之相關議題做進一步的補充，幫助釐清採用權益薪酬之決定因素。The controversy around the rising use of equity-based compensation for audit committee members and the enhanced responsibilities of audit committee is the basis for this study to examine the factors that affect a firm’s use of stocks and stock options to remunerate audit committee members. Our results show that firms having more severe agency conflicts are less likely to give equity-based compensation to audit committee members. Furthermore, firms with more compensation committee members sitting on the audit committee are significantly more likely to compensate audit committee members with stocks and stock options. Moreover, when more audit committee members are also top managers of other companies, the probability of equity remuneration for audit committee members is lower. Given that prior studies find that equity-based compensation for audit committee members is associated with earnings management, accounting restatement, and internal control weakness, our study contributes to the literature by identifying the factors that contribute to the use of equity-based compensation for audit committee members
Improving forest growth estimates using a Bayesian network approach
Full text linkEstimating the contribution of forests to carbon sequestration is commonly done by applying forest growth models. Such models inherently use field observations, such as leaf area index (LAI), whereas relevant information is also available from remotely sensed images. The purpose of this study is to improve the LAI estimated from the physiological principles predicting growth (3-PG) model by combining its output with LAI derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. A Bayesian network (BN) approach is proposed to take care of the different structure of the inaccuracies in the two data sources. It addresses the bias in the 3-PG model and the noise of the ASTER images. Moreover, the EM algorithm is introduced into BN to estimate missing the LAI ASTER data, since they are not available for long time series due to the atmospheric conditions. This paper shows that the outputs obtained with the BN were more accurate than the 3-PG estimate, as the root mean square error reduces to 0.46, and the relative error to 5.86%. We conclude that the EM-algorithm within a BN can adequately handle missing LAI ASTER values, and BNs can improve the estimation of LAI values. Ultimately, this method may be used as a predicting model of LAI values, and handling the missing data of ASTER images time series
Improvement of spatio-temporal growth estimates in heterogeneous forests using Gaussian Bayesian networks
Full text linkCanopy leaf area index (LAI) is a quantitative measure of canopy foliar area. LAI values can be derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images. In this paper, MODIS pixels from a heterogeneous forest located in The Netherlands were decomposed using the linear mixture model using class fractions derived from a high-resolution aerial image. Gaussian Bayesian networks (GBNs) were applied to improve the spatio-temporal estimation of LAI by combining the decomposed MODIS images with a spatial version of physiological principles predicting growth (3PG) model output at different moments in time. Results showed that the spatial-temporal output obtained with the GBN was 40% more accurate than the spatial 3PG, with a root-mean-square error below 0.25. We concluded that the GBNs improved the spatial estimation of LAI values of a heterogeneous forest by combining a spatial forest growth model with satellite imagery
Spin Caloritronic Phenomena Driven by Spin-orbit Coupling
No full textIn this thesis, we report several effects in spintronics and spin caloritronics related to relativistic spin-orbit coupling. In Chapter 2, we discuss the relativistic spin caloritronicHall effects in terms of a semiclassical theory for anomalous thermoelectric effects in ferromagnetic metals due to spin-orbit scattering at impurities, including the anomalous Nernst and Ettingshausen effect, the planar thermalHall effects, and thermolectric anisotropic magnetoresistance. The linear response relations between the currents and driving forces are derived for out-of-plane and in-plane magnetizations, respectively. In the out-of-plane configuration, there are anomalous thermoelectric Hall effects linear to the spin-orbit constant, while the thermoelectric anisotropic magnetoresistance and the planar Hall effect in the in-plane configuration are of second order in the spin-orbit coupling. The extrinsic theory systemizes the competing effects/mechanisms from a microscopic point of view and identifies the parameters needed to describe experiments. We developed a diffusion theory in Chapter 3 for the spin Hall magnetoresistance (SMR) in multilayers made from an insulating magnet F such as yttriumiron garnet (YIG), and a normal metal N with spin-orbit interactions, such as platinum (Pt). In an N|F bilayer system, the SMR requires spin-flip in N and spin-transfer at the N|F interface. Our results explain the SMR both qualitatively and quantitatively with transport parameters that are consistent with other experiments. The degrees of spin accumulation in N that can be controlled by the magnetization direction is found to be very significant. In the presence of an imaginary part of the spin-mixing conductance Gi we predicted an AHE-like signal (SHAHE), which has been observed experimentally and can be explained with values of Gi that agree with first principles calculations. We furthermore analyzed F|N|F spin valves for parallel and perpendicular magnetization configurations. The SMR torques under applied currents in N are expected to lead to magnetization dynamics of N|F and F|N|F structures. In Chapter 4,we generalized the SMR theory in Chapter 3 to a thin-film made of a metallic ferromagnet and take into account the out-of-plane spin currents generated by the spinHall effect, which were disregarded in Chapter 2. We predict a new contribution to the anisotropic magnetoresistance by the simultaneous action of the anomalous Hall effect and its inverse. By diffusion theory, we compare this contribution with the conventional anisotropic magnetoresistance, demonstrating that they can be distinguished experimentally by studying its dependence on the film thickness. The extra contribution to the magnetoresistance has a magnetization dependence different from that of the conventional AMR. While the conventional AMR is usually positive, the new contribution is always negative. In order to analyze the effect of interface and boundary roughness that was disregarded in Chapter 3, we reports in Chapter 5 a Boltzmann study to quantify how the surface/interface scattering affects the spin Hall physics. In a bilayer system made of N and FI, we observe an AHE-like transverse voltage induced by the spin dependent scattering at the FI|N interface, which is competing with the imaginary SMR predicted in Chapter 3. We further show that the spin diffusion equation on which the SMR in Chapter 3 is based, has to be corrected by the surface/interface roughness in the limit of thin-films. Our model provides an approach to analyze the role of roughness in recent measurements on layered systems. Even though the theories developed in Chapters 3–5 are not directly related to spin caloritronics, they can be easily generalized for their thermoelectric analogues by the formulation spelt out in Chapter 2, and can be useful for prospective research in spintronics and spin caloritronics.Quantum NanoscienceApplied Science
Improving forest growth estimates using a Bayesian network approach
Full text linkEstimating the contribution of forests to carbon sequestration is commonly done by applying forest growth models. Such models inherently use field observations, such as leaf area index (LAI), whereas relevant information is also available from remotely sensed images. The purpose of this study is to improve the LAI estimated from the Physiological Principles Predicting Growth (3-PG) model by combining its output with LAI derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. A Bayesian network (BN) approach is proposed to take care of the different structure of the inaccuracies in the two data sources. It addresses the bias in the 3-PG model and the noise of the ASTER images. Moreover, the EM algorithm is introduced into BN to estimate missing the LAI ASTER data, since they are not available for long time series due to the atmospheric conditions. This paper shows that the outputs obtained with the BN were more accurate than the 3-PG estimate, as the root mean square error reduces to 0.46, and the relative error to 5.86 percent. We conclude that the EM-algorithm within a BN can adequately handle missing LAI ASTER values, and BNs can improve the estimation of LAI values. Ultimately, this method may be used as a predicting model of LAI values, and handling the missing data of ASTER images time series
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