Chen Models Research Articles

The curvilinear relationships between structural embeddedness and productive efficiency: An exploratory study

Existing network studies pay much attention to the benefits of structural embeddedness. However, Uzzi (1996) points out the marginal benefits of structural embeddedness may follow a pattern of diminishing returns. Using major customer disclosure data from Compustat, this study investigates the curvilinear relationships between multiple aspects of structural embeddedness and productive efficiency. Applying both Wang and Ho (2010) and Chen and Lin (2009) stochastic frontier models, we confirm the diminishing returns of eigenvector and betweenness centralities with a partial support for interconnectedness. Implications from our findings can help decision makers better understand the effects of structural embeddedness and help devise network structural change strategies for productive efficiency.

A Nonextensive Electrolyte UNIQUAC Model for Prediction of Mean Activity Coefficients of Binary Electrolyte Solutions

In this work, an electrolyte-UNIQUAC model was developed by replacement of Boltzmann weight binary interaction parameters by the nonextensive Tsallis weight. A summation of the long-range electrostatic term (Debye-Huckel equation) and a short-range interaction term were considered in the calculation of thermodynamic properties. A framework proposed by Chen and co-workers was employed for the derivation of the local mole fractions. Application of the nonextensive theory increased the degree of freedom of the present model (T-E-UNIQUAC). Furthermore, the strength of the model lies in its ability to calculate individual activity coefficients of ions. The applicability of the T-E-UNIQUAC model has been tested using aqueous electrolyte solutions and results have been compared with Messnaoui, Chen and Pitzer models.

Thermodynamic properties and water desorption isotherms of Golden Delicious apples

Moisture desorption isotherms of apples (Golden Delicious) were determined. Experimental desorption data were fitted by 13 mathematical models. The influence of temperature on desorption isotherms was more indicated at high water activities. The GAB, Halsey, Modified Halsey, Oswin, Chen, Ferro Fontan, Lewicki (1998) and Lewicki (2000) models correlated well to the experimental desorption data over the entire field of temperatures and water activities explored. In order to well understand the water properties and to evaluate the energy requirements for transport phenomena during processing, the thermodynamic properties were evaluated from the experimental desorption data. The enthalpy-entropy compensation theory was validated by the variation of net isosteric heat versus differential entropy. This variation provided the isokinetic temperature and the free energy indicating that the desorption process in apple was a non-spontaneous and enthalpy-driven mechanism.

Ground-state structures, physical properties and phase diagram of carbon-rich nitride C5N

Using the ab initio evolutionary algorithm, four thermodynamically stable C5N phases (, -1, -2 and C2/m) are uncovered. The structures of the , -1 and -2 phases possess layered features. The dense C2/m phase with 3D strong covalent bond network is calculated to be superhard with hardness of 75.4 and 83.9 GPa by Chen and Gao models. The electronic properties of the C5N is diverse including conductor (), indirect semiconductor (-1 and -2) and direct semiconductor (C2/m). Pressure-induced phase transition sequences and critical pressure points are calculated to be → -1 → -2 at 2.7 and 34 GPa, respectively. Though the C2/m phase is metastable at zero temperature, our established pressure-temperature phase diagram of C5N shows that the C2/m becomes stable under high temperatures and pressures. The pressure-temperature phase diagram will give theoretical guidance for further experimental synthesis of different C5N phases.

Developing a new solar radiation estimation model based on Buckingham theorem

Abstract While the value of solar radiation can be expressed physically in the days without clouds, this expression becomes difficult in cloudy and complicated weather conditions. In addition, solar radiation measurements are often not taken in developing countries. In such cases, solar radiation estimation models are used. Solar radiation prediction models estimate solar radiation using other measured meteorological parameters those are available in the stations. In this study, a solar radiation estimation model was obtained using Buckingham theorem. This theory has been shown to be useful in predicting solar radiation. In this study, Buckingham theorem is used to express the solar radiation by derivation of dimensionless pi parameters. This derived model is compared with temperature based models in the literature. MPE, RMSE, MBE and NSE error analysis methods are used in this comparison. Allen, Hargreaves, Chen and Bristow-Campbell models in the literature are used for comparison. North Dakota’s meteorological data were used to compare the models. Error analysis were applied through the comparisons between the models in the literature and the model that is derived in the study. These comparisons were made using data obtained from North Dakota’s agricultural climate network. In these applications, the model obtained within the scope of the study gives better results. Especially, in terms of short-term performance, it has been found that the obtained model gives satisfactory results. It has been seen that this model gives better accuracy in comparison with other models. It is possible in RMSE analysis results. Buckingham theorem was found useful in estimating solar radiation. In terms of long term performances and percentage errors, the model has given good results.

Sorption isotherm and state diagram for indica rice starch with and without soluble dietary fiber

Moisture sorption isotherms and state diagrams for indica rice starch (IRS) and indica rice starch-soluble dietary fiber (IRS-SDF) were developed to investigate the effect of SDF on the stability of IRS. Sorption isotherms of IRS and IRS-SDF were determined by the static gravimetric method and the data were modeled by Guggenheim–Anderson–de Boer (GAB) model. The GAB monolayer moisture contents were calculated to be 7.43 and 8.37 g/100g (dry basis) for IRS and IRS-SDF, respectively. The state diagram was composed of the glass transition line and freezing curve, which were fitted according to Gordon–Taylor and Chen models, respectively. The ultimate maximum-freeze-concentration conditions were calculated as characteristic glass transition temperature (Tg’) of −42.5 °C and −31.5 °C with characteristic solids content (Xs') being 0.71 and 0.72 g/g (wet basis), and characteristic temperature of end point of freezing (Tm’) being −18.2 °C and −13.8 °C for IRS and IRS-SDF, respectively. The state diagrams and sorption isotherms of IRS and IRS-SDF have great significance for evaluating storage stability, optimizing drying and freezing processes.

Unexpected ground-state structures and properties of carbon nitride C3N at ambient and high pressures

Thermodynamically stable C3N phases in the pressure range of 0–100GPa are searched and calculated by ab initio evolutionary algorithm and density functional theory. Ground-state C3N phase at ambient pressure is predicted to be a layered structure with Cmmm space group. The previously reported Fd3¯m phase exists only in the narrow pressure range of 6.1–7.4GPa, which transforms into a P3¯m1 phase at 7.4GPa and then into a R3¯m phase at 40.7GPa. The increasing pressure drives the R3¯m phase to a C2/c phase at 76.9GPa. Dynamical and mechanical stabilities of C3N phases are checked and confirmed by phonon dispersion and elastic constant calculations. It is diverse of the electronic properties of C3N with Cmmm (zero band gap), P3¯m1 (wide gap of 4.55eV), R3¯m (wide gap of 4.54eV) and C2/c (narrow gap of 1.94eV). The three-dimensional strong covalent C2/c C3N is superhard with high hardness of 79.5 and 86.9GPa calculated by Chen and Gao models. Pressure-temperature phase diagram of C3N is firstly established and shows high temperature can significantly decrease synthesis pressures of C2/c phase, which gives a theoretical guidance for further experimental synthesis.

Non-hollow-core Cybister trabeculae and compressive properties of two biomimetic models of beetle forewings

In 2006, the forewing trabeculae of Cybister tripunctatus Olivier (i.e., Cybister) beetles were reported to be hollow, and a biomimetic structural model (i.e., Song's model) was reported to exhibit better compressive mechanical properties than a solid-core trabecula-honeycomb model (i.e., Chen's model). To test these assertions, the current study first observed the trabecular microstructure of the Cybister beetle and confirmed that the trabeculae are solid. Second, the finite element method (FEM) was used to perform a contrast analysis of the compressive mechanical properties of Song's and Chen's biomimetic models. The results indicated that Chen's model exhibited better compressive mechanical properties. These findings, which are completely opposite of Song's findings, were obtained because the comparison models designed for use in Song's study were not comparable to that of Chen's model in terms of the core volumes. This study will benefit the development of beetle forewing biomimetic research.

The effect of HZSM-5 catalyst particle size on kinetic models of methanol to gasoline conversion

In chemical reactions, a principal and influential parameter is particle size. The effect of HZSM-5 catalyst particle size on the kinetics of methanol conversion to gasoline has been investigated in this work. Three catalysts of 90nm (Z1), 1μm (Z2) and 4μm (Z3) containing Si/Al ratios of 25, and a 90nm sized with a Si/Al ratio of 50 (Z4) were prepared. The experiments were performed at temperatures of 598, 623 and 648K at different feed rates. Chen and Schipper kinetic models, credible models for MTG process, were chosen to study the catalyst particle size effect on MTG process kinetics. R2 adj. values for each of the models show that the experimental data are in relatively good agreement with Chen and Schipper models. In order to check the results of modeling with experimental data, objective function parameter was applied as a criterion and Z1 catalyst showed the best performance using Chen model, closely followed by Z2.

State diagrams for mixtures of low molecular weight carbohydrates

The objective of this research was to develop state diagrams of model food systems prepared with several fructose/glucose/sucrose mass fractions as a basis for studying products obtained from natural juices. A total of 16 sugar mixtures, including pure components as well as binary and ternary mixtures, with freezable and reduced moisture, were prepared and subjected to thermal analysis using differential scanning calorimetry (DSC), where freezing points (Tm and Tm′) and glass transitions values (Tg, Tg′) were determined. The expected decrease of Tg due to the plasticizing effect of water and the depression of Tm as a function of increasing solids concentration were adequately predicted (R2 > 0.94) using the Gordon–Taylor and Chen models, respectively. Results showed that Tg and Tm depression were influenced significantly by sugar composition (p < 0.05). Tm′ and Tg′ values ranged from −42.8 to −32.4 °C and from −55.9 to −43.1 °C, respectively and were also sugar composition dependent (p < 0.05). Constructed state diagram showed that solid mass fraction at the maximal-freeze-concentration condition (xs′) varied from 0.772 to 0.842 (g solids/g sample), however, significant effect of the sugar composition was not observed for xs′ values.

A neural network model for remote sensing of diffuse attenuation coefficient in global oceanic and coastal waters: Exemplifying the applicability of the model to the coastal regions in Eastern China Seas

For global oceanic and coastal waters, a multilayer back propagation neural network (MBPNN) is developed to retrieve the diffuse attenuation coefficient for the downwelling spectral irradiance at the wavelength 490nm (Kd(490)). The applicability of Lee's quasi-analytical algorithm-based semi-analytical model, Wang's switching model, Chen's semi-analytical model, Jamet's neural network model, and the MBPNN model is evaluated using the NASA bio-optical marine algorithm dataset (NOMAD) and the Eastern China Seas dataset. Based on the comparison of Kd(490) predicted by these five models, with field measurements taken in global oceanic and coastal waters, it is found that the MBPNN model provides a stronger performance than the Lee, Wang, Chen, and Jamet's models. The atmospheric effects on the MODIS data are eliminated using near-infrared band-based and shortwave infrared band-based combined models, and the Kd(490) is quantified from the MODIS data after atmospheric correction using the MBPNN model. The study results indicate that the MBPNN model produces ~28% uncertainty in estimating Kd(490) from the MODIS data. Finally, an exemplification of the applicability of the model to the coastal regions in the Eastern China Seas is carried out. Our results suggest that the Kd(490) shows a large variation in the Eastern China Seas, ranging from 0.02 to 4.0m−1, with an average value of ~0.17m−1.

A Temperature-Based Model for Estimating Monthly Average Daily Global Solar Radiation in China

Since air temperature records are readily available around the world, the models based on air temperature for estimating solar radiation have been widely accepted. In this paper, a new model based on Hargreaves and Samani (HS) method for estimating monthly average daily global solar radiation is proposed. With statistical error tests, the performance of the new model is validated by comparing with the HS model and its two modifications (Samani model and Chen model) against the measured data at 65 meteorological stations in China. Results show that the new model is more accurate and robust than the HS, Samani, and Chen models in all climatic regions, especially in the humid regions. Hence, the new model can be recommended for estimating solar radiation in areas where only air temperature data are available in China.

An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain

BackgroundThe aim of this study was to evaluate the calibration and discriminatory power of three predictive models of breast cancer risk.MethodsWe included 13,760 women who were first-time participants in the Sabadell-Cerdanyola Breast Cancer Screening Program, in Catalonia, Spain. Projections of risk were obtained at three and five years for invasive cancer using the Gail, Chen and Barlow models. Incidence and mortality data were obtained from the Catalan registries. The calibration and discrimination of the models were assessed using the Hosmer-Lemeshow C statistic, the area under the receiver operating characteristic curve (AUC) and the Harrell’s C statistic.ResultsThe Gail and Chen models showed good calibration while the Barlow model overestimated the number of cases: the ratio between estimated and observed values at 5 years ranged from 0.86 to 1.55 for the first two models and from 1.82 to 3.44 for the Barlow model. The 5-year projection for the Chen and Barlow models had the highest discrimination, with an AUC around 0.58. The Harrell’s C statistic showed very similar values in the 5-year projection for each of the models. Although they passed the calibration test, the Gail and Chen models overestimated the number of cases in some breast density categories.ConclusionsThese models cannot be used as a measure of individual risk in early detection programs to customize screening strategies. The inclusion of longitudinal measures of breast density or other risk factors in joint models of survival and longitudinal data may be a step towards personalized early detection of BC.

Comparison of six particle size distribution models on the goodness-of-fit to particulate matter sampled from animal buildings

Lognormal distribution is often used as a default model for regression analysis of particle size distribution (PSD) data; however, its goodness-of-fit to particle matter (PM) sampled from animal buildings and its comparison to other PSD models have not been well examined. This study aimed to evaluate and to compare the goodness-of-fit of six PSD models to total suspended particulate matter (TSP) samples collected from 15 animal buildings. Four particle size analyzers were used for PSD measurement. The models' goodness-of-fit was evaluated based on adjusted R 2, Akaike's information criterion (AIC), and mean squared error (MSE) values. Results showed that the models' approximation of measured PSDs differed with particle size analyzer. The lognormal distribution model offered overall good approximations to measured PSD data, but was inferior to the gamma and Weibull distribution models when applied to PSD data derived from the Horiba and Malvern analyzers. Single-variable models including the exponential, Khrgian-Mazin, and Chen's empirical models provided relatively poor approximations and, thus, were not recommended for future investigations. A further examination on model-predicted PSD parameters revealed that even the best-fit model of the six could significantly misestimate mean diameter, median diameter, and variance. However, compared with other models, the best-fit model still offered the relatively best estimates of mean and median diameters, whereas the best predicted variances were given by the gamma distribution model. Implications Particulate matter from animal buildings contains a large portion of coarse particles and, thus, has different particle size distribution (PSD) than ambient aerosols. The findings suggest a default use of the prevalent lognormal distribution model can lead to significant misestimates of PSD parameters such as mean diameter, median diameter, and variance. Other models, such as the gamma and Weibull distribution models, may be considered as alternative options. Because of a complexity in the shape of measured PSD profiles, the goodness-of-fit for each of the different PSD models must be compared before selecting one for data regression, parameter estimation, and reporting.

Information Contents of Open Interest Value Ratio about KOSPI 200 Index Return

This paper investigates the lead-lag relationship between the call-put options open interest value ratio and the KOSPI 200 Index returns. In addition, we tried to find whether the open interest value ratio has the information contents about KOSPI 200 Index return. When estimating call-put options open interest value ratio, we use Chen, Lung, and Tay (2005, 2009) models. The sample period covers from January 5, 1998 to December 28, 2006 with the closing price returns of KOSPI 200 Index and the open interest of the KOSPI 200 options. We use statistical methodology such as VAR (vector autoregressive model), Granger causality test, impulse response and variance decomposition model for the dynamic empirical tests. Followings are the major findings and implications drawn from the empirical analysis of the Korean options market. Most previous researches claims that options open interest can provide the information contents to estimate the KOSPI 200 spot price movement. However, unlike the results of most previous researches, we found that the call-put options open interest value ratio does not have the information contents predicting the KOSPI 200 index return where as KOSPI 200 spot price leads the call-put options open interest value ratio.

Influence of Atmospheric Helium Plasma on the Surface Energy of Jute Fibres and the Performance of Resulting Composites

In this work, an atmospheric pressure glow discharge helium plasma treatment was employed to modify the surface properties of jute fibres. The resulting bio-composites showed an increase in flexural properties and interlaminar shear strength (ILSS) compared to composites produced using untreated jute fibres. To understand the reason behind the ILSS improvement, the acid–base properties of jute fibres were determined by contact angle analysis using the capillary rise method. The results were fitted further to van Oss–Chaudhury–Good (vOCG) and Chang–Qin–Chen (CQC) models to determine the Lifshitz–van der Waals (LW) and acid–base components of surface energy. Surface energy determined by the vOCG model revealed that plasma treatment of jute fibre resulted in a 22% increase in total surface energy, a 19% increase in the LW component and a 24% increase in the acid–base component of surface energy. The increase in the acid–base component is due to the significant increase (69%) in the electron-accepting (γ + S) parameter. On the other hand, the CQC model clearly indicates an amphoteric nature of the fibre surface based on opposite signs of the acid and base principal values (P a S and P b S). Overall, the results indicated that increases in both LW and acid–base components were responsible for improvement in the properties of the composites.

Fuzzy Nonlinear Models for New Product Development Using Four-Phase Quality Function Deployment Processes

Quality function deployment (QFD) frameworks are useful tools for constructing a new product development (NPD) plan that enables the clear itemization of customer needs and the systematic evaluation of each solution to maximize customer satisfaction. A complete QFD process includes four sequential phases in which four important decision outcomes are determined for NPD, namely, the fulfillment levels of design requirements (DRs), part characteristics, process parameters, and production requirements. Unlike prior studies which have focused only on determining DRs, this paper extends Chen and Ko's models to consider the close link between the four phases in NPD using the means-end chain concept to build up a series of fuzzy nonlinear programming models for determining the fulfillment levels of each decision outcome for customer satisfaction. In addition, this paper incorporates risk analysis, which is treated as the constraint in the models, into the QFD process. To deal with the vague nature of product development processes, fuzzy sets are applied for both QFD and risk analysis. A numerical example is used to demonstrate the applicability of the proposed model.

Avaliação de Ações e Números Contábeis: Aplicação dos modelos de Zhang (2000) e Zhang e Chen (2007) no mercado brasileiro

Este trabalho investiga a relação entre os números contábeis e os retornos das ações no mercado brasileiro. Para isso baseia-se no modelo teórico desenvolvido por Zhang (2000) e no modelo empírico de Zhang e Chen (2007). Os modelos demonstram que o retorno das ações pode ser escrito como função das seguintes variáveis: lucro líquido, variação da rentabilidade, capital investido, variação da oportunidade de crescimento e taxa de desconto. De maneira geral os resultados empíricos encontrados no mercado brasileiro são condizentes com as relações descritas pelo modelo teórico, similarmente às relações encontradas no mercado norte-americano. A proximidade dos resultados e dos coeficientes estatísticos obtidos nos diferentes testes (pooled, Fama-Macbeth e dados em painel) suportam a robustez dos resultados.

A gradient-based algorithm for semiparametric models with missing covariates

In the parametric regression model, the covariate missing problem under missing at random is considered. It is often desirable to use flexible parametric or semiparametric models for the covariate distribution, which can reduce a potential misspecification problem. Recently, a completely nonparametric approach was developed by [H.Y. Chen, Nonparametric and semiparametric models for missing covariates in parameter regression, J. Amer. Statist. Assoc. 99 (2004), pp. 1176–1189; Z. Zhang and H.E. Rockette, On maximum likelihood estimation in parametric regression with missing covariates, J. Statist. Plann. Inference 47 (2005), pp. 206–223]. Although it does not require a model for the covariate distribution or the missing data mechanism, the proposed method assumes that the covariate distribution is supported only by observed values. Consequently, their estimator is a restricted maximum likelihood estimator (MLE) rather than the global MLE. In this article, we show the restricted semiparametric MLE could be very misleading in some cases. We discuss why this problem occurs and suggest an algorithm to obtain the global MLE. Then, we assess the performance of the proposed method via some simulation experiments.

A new calculation approach for dielectric space charge characterization from nano to macro-scales by the thermal step method

In this paper, a new calculation approach is developed and applied, for the first time, to different dielectric scales using the thermal step method (TSM). The thermal aspect of the approach concerns insulating materials from nano to macro-scales. The theoretical approach is based on the ballistic-diffuse transport taking into account the relaxation time of heat phonons and their effects on the trapped space charges. These effects are revealed on the temporal evolution of the induced image charges that constitute the electrical signal of the thermal step method. The temperature distributions and the TSM induced image charges are obtained by applying the numerical finite element method, the Newmark direct integration, the mean value theorem and the composite Simpson approximation. In a first step, the results of the temperature distributions are validated with those obtained by Boltzmann and Chen models for nano-polyethylene sample. In a second step, our approach is validated with TSM previous works using the Fourier's model for space charge characterizations in the micro and macro-dielectrics. The validated results of the TSM induced image charges for micro and macro-scales show good agreements, and prove that our approach is a consistent tool to be applied in space charge characterizations for different dielectric scales.