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

Unraveling the senses of Phytophthora; leads to novel control strategies?

Oomycetes cause devastating diseases on plants and animals. They cause major yield losses in many crop plants and their control heavily depends on agrochemicals. This is certainly true for the potato late blight pathogen Phytophthora infestans. Strong concerns about adverse effects of agrochemicals on food safety and environment are incentives for the development of novel, environmental friendly control strategies preferably based on natural products. Cyclic lipopeptides (CLPs) were recently discovered as a new class of natural compounds with strong activities against oomycetes including Phytophthora. CLPs lyse zoospores, inhibit mycelial growth and effectively reduce late blight disease. In order to unravel how Phytophthora senses CLPs and other environmental signals we follow two approaches. On the one hand, we monitor genome wide changes in gene expression induced by CLPs with the aim to identify the cellular pathways targeted by CLPs. On the other hand, we analyse components of ubiquitous signal transduction pathways with the aim to identify features that are unique for Phytophthora or oomycetes and, hence, could be suitable targets for novel anti-oomycete agents. Mining and comparing whole genome sequences have revealed that Phytophthora harbours many novel phospholipid modifying enzymes, unique for oomycetes. They have aberrant combinations of catalytic and regulatory domains occasionally combined with transmembrane domains. The latter resemble receptors that might be activated by extracellular ligands. Phospholipids, the substrates of these enzymes, are structural membrane components that also function in signalling. Together these findings open new avenues of research aimed at target-discovery in oomycetes

Efficient ARQ retransmission schemes for two-way relay networks.

In this paper, we investigate different practical automatic repeat request (ARQ) retransmission protocols for two-way wireless relay networks based on network coding (NC). The idea of NC is applied to increase the achievable throughput for the exchange of information between two terminals through one relay. Using NC, throughput efficiency is significantly improved due to the reduction of the number of retransmissions. Particularly, two improved NC-based ARQ schemes are designed based on go-back-N and selective-repeat (SR) protocols. The analysis of throughput efficiency is then carried out to find the best retransmission strategy for different scenarios. It is shown that the combination of improved NC-based SR ARQ scheme in the broadcast phase and the traditional SR ARQ scheme in the multiple access phase achieves the highest throughput efficiency compared to the other combinations of ARQ schemes. Finally, simulation results are provided to verify the theoretical analysis

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

Valence Bond Entanglement and Fluctuations in Random Singlet Phases

The ground state of the uniform antiferromagnetic spin-1/2 Heisenberg chain can be viewed as a strongly fluctuating liquid of valence bonds, while in disordered chains these bonds lock into random singlet states on long length scales. We show that this phenomenon can be studied numerically, even in the case of weak disorder, by calculating the mean value of the number of valence bonds leaving a block of $L$ contiguous spins (the valence-bond entanglement entropy) as well as the fluctuations in this number. These fluctuations show a clear crossover from a small $L$ regime, in which they behave similar to those of the uniform model, to a large $L$ regime in which they saturate in a way consistent with the formation of a random singlet state on long length scales. A scaling analysis of these fluctuations is used to study the dependence on disorder strength of the length scale characterizing the crossover between these two regimes. Results are obtained for a class of models which include, in addition to the spin-1/2 Heisenberg chain, the uniform and disordered critical 1D transverse-field Ising model and chains of interacting non-Abelian anyons.Comment: 8 pages, 6 figure

Gaseous optical contamination of the spacecraft environment: A review

Interactions between the ambient atmosphere and orbiting spacecraft, sounding rockets, and suborbital vehicles, and with their effluents, give rise to optical (extreme UV to LWIR) foreground radiation which constitutes noise that raises the detection threshold for terrestrial and celestial radiations, as well as military targets. Researchers review the current information on the on-orbit optical contamination. Its source species are created in interaction processes that can be grouped into three categories: (1) Reactions in the gas phase between the ambient atmosphere and desorbates and exhaust; (2) Reactions catalyzed by exposed ram surfaces, which occur spontaneously even in the absence of active material releases from the vehicles; and (3) Erosive excitative reactions with exposed bulk (organic) materials, which have recently been identified in the laboratory though not as yet observed on spacecraft. Researchers also assess the effect of optical pumping by earthshine and sunlight of both reaction products and effluents

Early Recognition of Burn- and Trauma-Related Acute Kidney Injury: A Pilot Comparison of Machine Learning Techniques.

Severely burned and non-burned trauma patients are at risk for acute kidney injury (AKI). The study objective was to assess the theoretical performance of artificial intelligence (AI)/machine learning (ML) algorithms to augment AKI recognition using the novel biomarker, neutrophil gelatinase associated lipocalin (NGAL), combined with contemporary biomarkers such as N-terminal pro B-type natriuretic peptide (NT-proBNP), urine output (UOP), and plasma creatinine. Machine learning approaches including logistic regression (LR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and deep neural networks (DNN) were used in this study. The AI/ML algorithm helped predict AKI 61.8 (32.5) hours faster than the Kidney Disease and Improving Global Disease Outcomes (KDIGO) criteria for burn and non-burned trauma patients. NGAL was analytically superior to traditional AKI biomarkers such as creatinine and UOP. With ML, the AKI predictive capability of NGAL was further enhanced when combined with NT-proBNP or creatinine. The use of AI/ML could be employed with NGAL to accelerate detection of AKI in at-risk burn and non-burned trauma patients

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

Sea state bias in altimeter sea level estimates determined by combining wave model and satellite data

This study documents a method for increasing the precision of satellite-derived sea level measurements. Results are achieved using an enhanced three-dimensional (3-D) sea state bias (SSB) correction model derived from both Jason-1 altimeter ocean observations (i.e., sea state and wind) and estimates of mean wave period from a numerical ocean wave model, NOAA’s WAVEWATCH III. A multiyear evaluation of Jason-1 data indicates sea surface height variance reduction of 1.26 (±0.2) cm2 in comparison to the commonly applied two-parameter SSB model. The improvement is similar for two separate variance reduction metrics and for separate annual data sets spanning 2002–2004. Spatial evaluation of improvement shows skill increase at all latitudes. Results indicate the new model can reduce the total Jason-1 and Jason-2 altimeter range error budgets by 7.5%. In addition to the 2-D (two-dimensional) and 3-D model differences in correcting the range for wavefield variability, mean model regional differences also occur across the globe and indicate a possible 1–2 cm gradient across ocean basins linked to the zonal variation in wave period (short fetch and period in the west, swells and long period in the east). Overall success of this model provides first evidence that operational wave modeling can support improved ocean altimetry. Future efforts will attempt to work within the limits of wave modeling capabilities to maximize their benefit to Jason-1 and Jason-2 SSB correction methods

Whisper-to-speech conversion using restricted Boltzmann machine arrays

Whispers are a natural vocal communication mechanism, in which vocal cords do not vibrate normally. Lack of glottal-induced pitch leads to low energy, and an inherent noise-like spectral distribution reduces intelligibility. Much research has been devoted to processing of whispers, including conversion of whispers to speech. Unfortunately, among several approaches, the best reconstructed speech to date still contains obviously artificial muffles and suffers from an unnatural prosody. To address these issues, the novel use of multiple restricted Boltzmann machines (RBMs) is reported as a statistical conversion model between whisper and speech spectral envelopes. Moreover, the accuracy of estimated pitch is improved using machine learning techniques for pitch estimation within only voiced (V) regions. Both objective and subjective evaluations show that this new method improves the quality of whisper-reconstructed speech compared with the state-of-the-art approaches