Sustainable Growth and Ethics: a Study of Business Ethics in Vietnam Between Business Students and Working Adults

Sustainable growth is not only the ultimate goal of business corporations but also the primary target of local governments as well as regional and global economies. One of the cornerstones of sustainable growth is ethics. An ethical organizational culture provides support to achieve sustainable growth. Ethical leaders and employees have great potential for positive influence on decisions and behaviors that lead to sustainability. Ethical behavior, therefore, is expected of everyone in the modern workplace. As a result, companies devote many resources and training programs to make sure their employees live according to the high ethical standards. This study provides an analysis of Vietnamese business students’ level of ethical maturity based on gender, education, work experience, and ethics training. The results of data from 260 business students compared with 704 working adults in Vietnam demonstrate that students have a significantly higher level of ethical maturity. Furthermore, gender and work experience are significant factors in ethical maturity. While more educated respondents and those who had completed an ethics course did have a higher level of ethical maturity, the results were not statistically significant. Analysis of the results along with suggestions and implications are provided

Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach

Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in HAR. Although these methods are fast and easy for implementation, they still have some limitations due to poor performance in a number of situations. In this paper, we propose a novel method based on the ensemble learning to boost the performance of these machine learning methods for HAR

Initial correlations in nonequilibrium Falicov-Kimball model

The Keldysh boundary problem in a nonequilibrium Falicov-Kimball model in infinite dimensions is studied within the truncated and self-consistent perturbation theories, and the dynamical mean-field theory. Within the model the system is started in equilibrium, and later a uniform electric field is turned on. The Kadanoff-Baym-Wagner equations for the nonequilibrium Green functions are derived, and numerically solved. The contributions of initial correlations are studied by monitoring the system evolution. It is found that the initial correlations are essential for establishing full electron correlations of the system and independent on the starting time of preparing the system in equilibrium. By examining the contributions of the initial correlations to the electric current and the double occupation, we find that the contributions are small in relation to the total value of those physical quantities when the interaction is weak, and significantly increase when the interaction is strong. The neglect of initial correlations may cause artifacts in the nonequilibrium properties of the system, especially in the strong interaction case

Chemical pre-processing of cluster galaxies over the past 10 billion years in the IllustrisTNG simulations

We use the IllustrisTNG simulations to investigate the evolution of the mass-metallicity relation (MZR) for star-forming cluster galaxies as a function of the formation history of their cluster host. The simulations predict an enhancement in the gas-phase metallicities of star-forming cluster galaxies (10^9< M_star<10^10 M_sun) at z<1.0 in comparisons to field galaxies. This is qualitatively consistent with observations. We find that the metallicity enhancement of cluster galaxies appears prior to their infall into the central cluster potential, indicating for the first time a systematic "chemical pre-processing" signature for {\it infalling} cluster galaxies. Namely, galaxies which will fall into a cluster by z=0 show a ~0.05 dex enhancement in the MZR compared to field galaxies at z<0.5. Based on the inflow rate of gas into cluster galaxies and its metallicity, we identify that the accretion of pre-enriched gas is the key driver of the chemical evolution of such galaxies, particularly in the stellar mass range (10^9< M_star<10^10 M_sun). We see signatures of an environmental dependence of the ambient/inflowing gas metallicity which extends well outside the nominal virial radius of clusters. Our results motivate future observations looking for pre-enrichment signatures in dense environments.Comment: 5 pages, 4 figures, accepted for publication in MNRAS Letter

Cellulose-Chitosan-Keratin Composite Materials: Synthesis, Immunological and Antibacterial Properties

Novel composites were synthesized from keratin (KER), cellulose (CEL) and chitosan (CS). The method is recyclable because majority (\u3e88%) of [BMIm+Cl-], an ionic liquid (IL), used as the sole solvent, was recovered for reuse. Experimentally, it was confirmed that unique properties of each component remain intact in the composites, namely bactericide (from KER and CS) and anti-inflammatory property (from KER). Specifically, the composites were examined for their anti-inflammatory influence on macrophages. The cells were imaged and immunophenotyped to determine the quantity using the macrophage marker CD11b. The 75:25 [KER+CS] composite was found to have the least amount of CD11b macrophages compared to other composites. Bactericidal assays indicated that all composites, except the 25:75 [KER+CS], substantially reduce the growth of organisms such as vancomycin resistant Enterococcus (VRE) and Eschericia coli. The results clearly indicate that the composites possess all properties needed for effective use as a wound dressin

Effects of Diet Composition and Insulin Resistance Status on Plasma Lipid Levels in a Weight Loss Intervention in Women.

BackgroundOptimal macronutrient distribution of weight loss diets has not been established. The distribution of energy from carbohydrate and fat has been observed to promote differential plasma lipid responses in previous weight loss studies, and insulin resistance status may interact with diet composition and affect weight loss and lipid responses.Methods and resultsOverweight and obese women (n=245) were enrolled in a 1-year behavioral weight loss intervention and randomly assigned to 1 of 3 study groups: a lower fat (20% energy), higher carbohydrate (65% energy) diet; a lower carbohydrate (45% energy), higher fat (35% energy) diet; or a walnut-rich, higher fat (35% energy), lower carbohydrate (45% energy) diet. Blood samples and data available from 213 women at baseline and at 6 months were the focus of this analysis. Triglycerides, total cholesterol, and high- and low-density lipoprotein cholesterol were quantified and compared between and within groups. Triglycerides decreased in all study arms at 6 months (P&lt;0.05). The walnut-rich diet increased high-density lipoprotein cholesterol more than either the lower fat or lower carbohydrate diet (P&lt;0.05). The walnut-rich diet also reduced low-density lipoprotein cholesterol in insulin-sensitive women, whereas the lower fat diet reduced both total cholesterol and high-density lipoprotein cholesterol in insulin-sensitive women (P&lt;0.05). Insulin sensitivity and C-reactive protein levels also improved.ConclusionsWeight loss was similar across the diet groups, although insulin-sensitive women lost more weight with a lower fat, higher carbohydrate diet versus a higher fat, lower carbohydrate diet. The walnut-rich, higher fat diet resulted in the most favorable changes in lipid levels.Clinical trial registrationURL: http://www.clinicaltrials.gov. Unique identifier: NCT01424007

Regularizing Face Verification Nets For Pain Intensity Regression

Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is limited by small datasets with labels of patient-reported pain intensities. Fortunately, fine-tuning from a data-extensive pre-trained domain, such as face verification, can alleviate this problem. In this paper, we propose a network that fine-tunes a state-of-the-art face verification network using a regularized regression loss and additional data with expression labels. In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification. The proposed regularized deep regressor is applied to estimate the pain expression intensity and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving the state-of-the-art performance. A weighted evaluation metric is also proposed to address the imbalance issue of different pain intensities.Comment: 5 pages, 3 figure; Camera-ready version to appear at IEEE ICIP 201

Long-range epidemic spreading with immunization

We study the phase transition between survival and extinction in an epidemic process with long-range interactions and immunization. This model can be viewed as the well-known general epidemic process (GEP) in which nearest-neighbor interactions are replaced by Levy flights over distances r which are distributed as P(r) ~ r^(-d-sigma). By extensive numerical simulations we confirm previous field-theoretical results obtained by Janssen et al. [Eur. Phys. J. B7, 137 (1999)].Comment: LaTeX, 14 pages, 4 eps figure

On the calculation of the bandgap of periodic solids with MGGA functionals using the total energy

During the last few years, it has become more and more clear that functionals of the meta generalized gradient approximation (MGGA) are more accurate than GGA functionals for the geometry and energetics of electronic systems. However, MGGA functionals are also potentially more interesting for the electronic structure, in particular, when the potential is nonmultiplicative (i.e., when MGGAs are implemented in the generalized Kohn-Sham framework), which may help to get more accurate bandgaps. Here, we show that the calculation of bandgap of solids with MGGA functionals can also be done very accurately in a non-self-consistent manner. This scheme uses only the total energy and can, therefore, be very useful when the self-consistent implementation of a particular MGGA functional is not available. Since self-consistent MGGA calculations may be difficult to converge, the non-self-consistent scheme may also help to speed up the calculations. Furthermore, it can be applied to any other types of functionals, for which the implementation of the corresponding potential is not trivial