A Lexicalized Tree-Adjoining Grammar for Vietnamese

In this paper, we present the first sizable grammar built for Vietnamese using LTAG, developed over the past two years, named vnLTAG. This grammar aims at modelling written language and is general enough to be both application- and domain-independent. It can be used for the morpho-syntactic tagging and syntactic parsing of Vietnamese texts, as well as text generation. We then present a robust parsing scheme using vnLTAG and a parser for the grammar. We finish with an evaluation using a test suite

Greening textile industry in Vietnam

The textile and garment industry has made a remarkable contribution to the economic development of Vietnam and employs currently a large labor force of 2.5 million people.However, the textile industry is also seen as a most polluting and unsustainable industry due to the use of excessive amounts of materials and the release of large amounts of pollutants into the environment. In order to improve the environmental sustainability and effectiveness of the textile industry in Vietnam this study has looked into preventive measure, reuse/recycling options and improved end-of-pipe technologies, separately and in combination. The end-of-pipe treatment is the last step in the greening production model. Textile wastewater is very difficult to treat, especially regarding the high color intensity. Removal of color from textile wastewater was studied by varying the pH, the application of a biological treatment step, the application of coagulation/flocculation and of Advanced Oxidation Processes (O3, O3/H2O2, Fenton’s reagent). The coagulation process was very effective in color removal of insoluble dyestuffs (98%), but this process is not so suitable for wastewater containing only soluble dyestuffs (12-55%). Of the Advanced Oxidation Processes, the Fenton’s reagent process was the most effective method for color removal (81-98%)for the four types of wastewater tested. The decolorization with the ozone process at low pH (pH 5)showed that direct oxidation by molecular ozone is much more selective in color removal than the oxidation by hydroxyl radicals. The presence of colloidal particles caused a 12-fold increase for ozone needed to obtain the same color removal efficiency as for a wastewater without colloidal particles.Each of the investigated processes could only remove one or a few types of pollutants from the wastewater, with the consequence that effluents could not meet all the discharge regulations. The combination of an activated sludge process, and a coagulation and ozone process yielded the best color (45 Pt-Co) and COD (30 mg O2/L) removal at the lowest costs (0.3 €/m3), compared with all other tested combinations. Separate collection of wastewater streams in a factory can also strongly contribute to the efficiency and sustainability of wastewater treatment. In the wet processes of the textile industry 75% of the total water consumption is for rinsing purposes. Wastewater from most rinsing steps contains low amounts of pollutants and can be reused in other process stages or can be discharged without treatment. An industrial ecology zone model, integrating preventive cleaner production approaches, a waste exchange network for reuse and recycling, and new end-of-pipe technologies, has been developed and assessed in two case studies: the Thanh Cong Company and the Nhon Trach 2 Industrial Zone. The greening production model developed for the Thanh Cong Company, a large-scale textile company in Hochiminh city, included the combination of cleaner production, external waste exchange and end-of-pipe technology. The dyestuffs, auxiliary chemicals, water and energy consumption can be reduced significantly when the proposed cleaner production, the external waste exchange options and the improved end-of-pipe technologies are implemented. Total benefits in savings per day can be more than 1,000 US$. The industrial ecology zone model was designed in three steps. Firstly the greening production model developed for the Thanh Cong Company was applied to all textile enterprises in the industrial ecology zone that was considered. Secondly an outside waste exchange network was designed. The outside network includes reuse of waste plastics, waste paper and waste oil at recycling companies in the neighborhood. The last step is to treat solid waste and polluted air and to treat and reuse wastewater for irrigation (cotton cultivation), for use in sanitary systems and to water plants in the industrial zone. The case studies of the greening production model and of the industrial ecology zone model demonstrated that a successful industrial ecology practice not only depends on the interaction between enterprises inside but also on the interaction with the actor networks outside the industrial system:the economic networks, the social networks and the policy networks. These networks can contribute in different ways to the implementation of the models. In the case study of a large textile company the economic network is very important in the implementation of the greening production model and in the case study of an industrial ecology zone the policy network play the most important role in the implementation of the industrial ecology model. </p

A Lexicalized Tree-Adjoining Grammar for Vietnamese

In this paper, we present the first sizable grammar built for Vietnamese using LTAG, developed over the past two years, named vnLTAG. This grammar aims at modelling written language and is general enough to be both application- and domain-independent. It can be used for the morpho-syntactic tagging and syntactic parsing of Vietnamese texts, as well as text generation. We then present a robust parsing scheme using vnLTAG and a parser for the grammar. We finish with an evaluation using a test suite

Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach

Building's energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal operating strategy, which could be integrated to building's energy management system (BEMS). This paper proposes a prediction model for building energy consumption using support vector machine (SVM). Data-driven model, for instance, SVM is very sensitive to the selection of training data. Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model. In addition, to encompass thermal inertia of building, pseudo dynamic model is applied since it takes into account information of transition of energy consumption effects and occupancy profile. Relevant days data selection and whole training data model is applied to the case studies of Ecole des Mines de Nantes, France Office building. The results showed that support vector machine based on relevant data selection method is able to predict the energy consumption of building with a high accuracy in compare to whole data training. In addition, relevant data selection method is computationally cheaper (around 8 minute training time) in contrast to whole data training (around 31 hour for weekend and 116 hour for working days) and reveals realistic control implementation for online system as well.Comment: Proceedings of ECOS 2015-The 28th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems , Jun 2015, Pau, Franc

Trends in, projections of, and inequalities in reproductive, maternal, newborn and child health service coverage in Vietnam 2000-2030: A Bayesian analysis at national and sub-national levels

Background: To assess the reproductive, maternal, newborn and child health (RMNCH) service coverage in Vietnam with trends in 2000-2014, projections and probability of achieving targets in 2030 at national and sub-national levels; and to analyze the socioeconomic, regional and urban-rural inequalities in RMNCH service indicators. Methods: We used national population-based datasets of 44,624 households in Vietnam from 2000 to 2014. We applied Bayesian regression models to estimate the trends in and projections of RMNCH indicators and the probabilities of achieving the 2030 targets. Using the relative index, slope index, and concentration index of inequality, we examined the patterns and trends in RMNCH coverage inequality. Findings: We projected that 9 out of 17 health service indicators (53%) would likely achieve the 2030 targets at the national level, including at least one and four ANC visits, BCG immunization, access to improved water and adequate sanitation, institutional delivery, skilled birth attendance, care-seeking for pneumonia, and ARI treatment. We observed very low coverages and zero chance of achieving the 2030 targets at national and sub-national levels in early initiation and exclusive breastfeeding, family planning needs satisfied, and oral rehydration therapy. The most deprived households living in rural areas and the Northwest, Northeast, North Central, Central Highlands, and Mekong River Delta regions would not reach the 80% immunization coverage of DPT3, Polio3, Measles and full immunization. We found socioeconomic, regional, and urban-rural inequalities in all RMNCH indicators in 2014 and no change in inequalities over 15 years in the lowest-coverage indicators. Interpretation: Vietnam has made substantial progress toward UHC. By improving the government\u27s health system reform efforts, re-allocating resources focusing on people in the most impoverished rural regions, and restructuring and enhancing current health programs, Vietnam can achieve the UHC targets and other health-related SDGs

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Stereoselective pharmacokinetics of stable isotope (+/-)-[13C]-pantoprazole: Implications for a rapid screening phenotype test of CYP2C19 activity

AIMS: We have previously shown that the (±)-[(13) C]-pantoprazole breath test is a promising noninvasive probe of CYP2C19 activity. As part of that trial, plasma, breath test indices and CYP2C19 (*2, *3, and *17) genotype were collected. Here, we examined whether [(13) C]-pantoprazole exhibits enantioselective pharmacokinetics and whether this enantioselectivity is correlated with indices of breath test. METHODS: Plasma (-)- and (+)-[(13) C]-pantoprazole that were measured using a chiral HPLC were compared between CYP2C19 genotypes and correlated with breath test indices. RESULTS: The AUC( 0-∞) of (+)-[(13) C]-pantoprazole in PM (*2/*2, n = 4) was 10.1- and 5.6-fold higher that EM (*1/*1or *17, n = 10) and IM (*1/*2or *3, n = 10) of CYP2C19, respectively (P < 0.001). The AUC( 0-∞) of (-)-[(13) C]-pantoprazole only significantly differed between PMs and EMs (1.98-fold; P = 0.05). The AUC( 0-∞) ratio of (+)-/(-)-[(13) C]-pantoprazole was 3.45, 0.77, and 0.67 in PM, IM, and EM genotypes, respectively. Breath test index, delta over baseline show significant correlation with AUC( 0-∞) of (+)-[(13) C]-pantoprazole (Pearson's r = 0.62; P < 0.001). CONCLUSIONS: [(13) C]-pantoprazole exhibits enantioselective elimination. (+)-[(13) C]-pantoprazole is more dependent on CYP2C19 metabolic status and may serve as a more attractive probe of CYP2C19 activity than (-)-[(13) C]-pantoprazole or the racemic mixture