Estimation of volumetric flow rate in a square duct: Equal area versus log-Tchebycheff methods.

Accurate measurement of the volumetric airflow rates in a duct is critical to room comfort and energy saving in HVAC industry. Presently, the Equal Area and the Log-Tchebycheff methods are extensively used in practice. Both methods deduce the flow rate based on averaging discrete point velocities along the cross section while their difference is associated with the rules in specifying the measurement locations. This study aims at evaluating the Equal Area and the Log-Tchebycheff methods in deducing airflow rate in a 0.46 m square duct up to 40 Dh long, over a range of Reynolds number from 10,000 to 500,000. The numerical investigation evaluated the two methods for ideal flow conditions in the absence of practical imperfections. The airflow was simulated in a three-dimensional space using the commercial CFD code FLUENT with the RNG k-epsilon turbulence model. Based on the simulated flow field, the volumetric flow rates were calculated according to the Equal Area and the Log-Tchebycheff methods. It was observed that the Equal Area method overestimated the flow rate by 3.5 ∼ 4.7% while the Log-Tchebycheff method\u27s values fell within -0.4 ∼ 0.8% of the actual flow rates. (Abstract shortened by UMI.)Dept. of Mechanical, Automotive, and Materials Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .Z46. Source: Masters Abstracts International, Volume: 44-03, page: 1500. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

Reinforcement Learning Experience Reuse with Policy Residual Representation

Experience reuse is key to sample-efficient reinforcement learning. One of the critical issues is how the experience is represented and stored. Previously, the experience can be stored in the forms of features, individual models, and the average model, each lying at a different granularity. However, new tasks may require experience across multiple granularities. In this paper, we propose the policy residual representation (PRR) network, which can extract and store multiple levels of experience. PRR network is trained on a set of tasks with a multi-level architecture, where a module in each level corresponds to a subset of the tasks. Therefore, the PRR network represents the experience in a spectrum-like way. When training on a new task, PRR can provide different levels of experience for accelerating the learning. We experiment with the PRR network on a set of grid world navigation tasks, locomotion tasks, and fighting tasks in a video game. The results show that the PRR network leads to better reuse of experience and thus outperforms some state-of-the-art approaches.Comment: Conference version appears in IJCAI 201

Dimensionality Reduction for Classification: Comparison of Techniques and Dimension Choice

We investigate the effects of dimensionality reduction using different techniques and different dimensions on six two-class data sets with numerical attributes as pre-processing for two classification algorithms. Besides reducing the dimensionality with the use of principal components and linear discriminants, we also introduce four new techniques. After this dimensionality reduction two algorithms are applied. The first algorithm takes advantage of the reduced dimensionality itself while the second one directly exploits the dimensional ranking. We observe that neither a single superior dimensionality reduction technique nor a straightforward way to select the optimal dimension can be identified. On the other hand we show that a good choice of technique and dimension can have a major impact on the classification power, generating classifiers that can rival industry standards. We conclude that dimensionality reduction should not only be used for visualisation or as pre-processing on very high dimensional data, but also as a general preprocessing technique on numerical data to raise the classification power. The difficult choice of both the dimensionality reduction technique and the reduced dimension however, should be directly based on the effects on the classification power

Plastid structure and carotenogenic gene expression in red- and white-fleshed loquat (Eriobotrya japonica) fruits

Loquat (Eriobotrya japonica Lindl.) can be sorted into red- and white-fleshed cultivars. The flesh of Luoyangqing (LYQ, red-fleshed) appears red-orange because of a high content of carotenoids while the flesh of Baisha (BS, white-fleshed) appears ivory white due to a lack of carotenoid accumulation. The carotenoid content in the peel and flesh of LYQ was approximately 68 μg g−1 and 13 μg g−1 fresh weight (FW), respectively, and for BS 19 μg g−1 and 0.27 μg g−1 FW. The mRNA levels of 15 carotenogenesis-related genes were analysed during fruit development and ripening. After the breaker stage (S4), the mRNA levels of phytoene synthase 1 (PSY1) and chromoplast-specific lycopene β-cyclase (CYCB) were higher in the peel, and CYCB and β-carotene hydroxylase (BCH) mRNAs were higher in the flesh of LYQ, compared with BS. Plastid morphogenesis during fruit ripening was also studied. The ultrastructure of plastids in the peel of BS changed less than in LYQ during fruit development. Two different chromoplast shapes were observed in the cells of LYQ peel and flesh at the fully ripe stage. Carotenoids were incorporated in the globules in chromoplasts of LYQ and BS peel but were in a crystalline form in the chromoplasts of LYQ flesh. However, no chromoplast structure was found in the cells of fully ripe BS fruit flesh. The mRNA level of plastid lipid-associated protein (PAP) in the peel and flesh of LYQ was over five times higher than in BS peel and flesh. In conclusion, the lower carotenoid content in BS fruit was associated with the lower mRNA levels of PSY1, CYCB, and BCH; however, the failure to develop normal chromoplasts in BS flesh is the most convincing explanation for the lack of carotenoid accumulation. The expression of PAP was well correlated with chromoplast numbers and carotenoid accumulation, suggesting its possible role in chromoplast biogenesis or interconversion of loquat fruit

Metabolic engineering to simultaneously activate anthocyanin and proanthocyanidin biosynthetic pathways in Nicotiana spp

[EN] Proanthocyanidins (PAs), or condensed tannins, are powerful antioxidants that remove harmful free oxygen radicals from cells. To engineer the anthocyanin and proanthocyanidin biosynthetic pathways to de novo produce PAs in two Nicotiana species, we incorporated four transgenes to the plant chassis. We opted to perform a simultaneous transformation of the genes linked in a multigenic construct rather than classical breeding or retransformation approaches. We generated a GoldenBraid 2.0 multigenic construct containing two Antirrhinum majus transcription factors (AmRosea1 and AmDelila) to upregulate the anthocyanin pathway in combination with two Medicago truncatula genes (MtLAR and MtANR) to produce the enzymes that will derivate the biosynthetic pathway to PAs production. Transient and stable transformation of Nicotiana benthamiana and Nicotiana tabacum with the multigenic construct were respectively performed. Transient expression experiments in N. benthamiana showed the activation of the anthocyanin pathway producing a purple color in the agroinfiltrated leaves and also the effective production of 208.5 nmol (-) catechin/g FW and 228.5 nmol (-) epicatechin/g FW measured by the p-dimethylaminocinnamaldehyde (DMACA) method. The integration capacity of the four transgenes, their respective expression levels and their heritability in the second generation were analyzed in stably transformed N. tabacum plants. DMACA and phoroglucinolysis/HPLC-MS analyses corroborated the activation of both pathways and the effective production of PAs in T0 and T1 transgenic tobacco plants up to a maximum of 3.48 mg/g DW. The possible biotechnological applications of the GB2.0 multigenic approach in forage legumes to produce "bloatsafe" plants and to improve the efficiency of conversion of plant protein into animal protein (ruminal protein bypass) are discussed.This work was supported by grants BIO2012-39849-C02-01 and BIO2016-75485-R from the Spanish Ministry of Economy and Competitiveness (MINECO) (http://www.idi.mineco.gob.es/portal/site/MICINN) to LAC and a fellowship of the JAE-CSIC program to SF. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Fresquet-Corrales, S.; Roque Mesa, EM.; Sarrión-Perdigones, A.; Rochina, M.; López-Gresa, MP.; Díaz-Mula, HM.; Belles Albert, JM.... (2017). Metabolic engineering to simultaneously activate anthocyanin and proanthocyanidin biosynthetic pathways in Nicotiana spp. PLoS ONE. 12(9). https://doi.org/10.1371/journal.pone.0184839Se018483912

Mechanism of charge transfer and its impacts on Fermi-level pinning for gas molecules adsorbed on monolayer WS2

Density functional theory calculations were performed to assess changes in the geometric and electronic structures of monolayer WS2 upon adsorption of various gas molecules (H2, O2, H2O, NH3, NO, NO2, and CO). The most stable configuration of the adsorbed molecules, the adsorption energy, and the degree of charge transfer between adsorbate and substrate were determined. All evaluated molecules were physisorbed on monolayer WS2 with a low degree of charge transfer and accept charge from the monolayer, except for NH3, which is a charge donor. Band structure calculations showed that the valence and conduction bands of monolayer WS2 are not significantly altered upon adsorption of H2, H2O, NH3, and CO, whereas the lowest unoccupied molecular orbitals of O2, NO, and NO2 are pinned around the Fermi-level when these molecules are adsorbed on monolayer WS2. The phenomenon of Fermi-level pinning was discussed in light of the traditional and orbital mixing charge transfer theories. The impacts of the charge transfer mechanism on Fermi-level pinning were confirmed for the gas molecules adsorbed on monolayer WS2. The proposed mechanism governing Fermi-level pinning is applicable to the systems of adsorbates on recently developed two-dimensional materials, such as graphene and transition metal dichalcogenides.Published versio

Influence of Intermediate Principal Stress on Undrained Behavior of Intact Clay under Pure Principal Stress Rotation

This study presents the accumulations of the excess pore water pressure and the deformation as well as the noncoaxial behavior of intact soft clay subjected to pure principal stress rotation. Series of tests were carried out by using a dynamic hollow cylinder apparatus to highlight the influence of intermediate principal stress parameter b. It was found that the rate of PWP evolution was greatly influenced by b, but the influence was not monotonous. Specimens under the condition b = 0.75 had the highest accumulation of pore water pressure while under the condition b = 0 had the strongest resistance to the pore pressure generation. PWP accumulated mainly in the first cycle. The failure of specimens under principal stress rotation was controlled by the strain other than the pore pressure. The shear stiffness decreased more quickly with higher b value. The direction of the principal strain increment was strongly dependent on the principal stress increment orientation and less influenced by the b value and the number of cycles

Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone

The update of the Android system and the emergence of the dual-frequency GNSS chips enable smartphones to acquire dual-frequency GNSS observations. In this paper, the GPS L1/L5 and Galileo E1/E5a dual-frequency PPP (precise point positioning) algorithm based on RTKLIB and GAMP was applied to analyze the positioning performance of the Xiaomi Mi 8 dual-frequency smartphone in static and kinematic modes. The results showed that in the static mode, the RMS position errors of the dual-frequency smartphone PPP solutions in the E, N, and U directions were 21.8 cm, 4.1 cm, and 11.0 cm, respectively, after convergence to 1 m within 102 min. The PPP of dual-frequency smartphone showed similar accuracy with geodetic receiver in single-frequency mode, while geodetic receiver in dual-frequency mode has higher accuracy. In the kinematic mode, the positioning track of the smartphone dual-frequency data had severe fluctuations, the positioning tracks derived from the smartphone and the geodetic receiver showed approximately difference of 3–5 m

A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter ( PM 2.5 ) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration on the basis of meteorological data of previous days by formulating the prediction over 24 h as a multi-task learning (MTL) problem. This enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other and compare it with several typical regularizations for MTL, including standard Frobenius norm regularization, nuclear norm regularization, and ℓ 2 , 1 -norm regularization. Our experiments have showed that the proposed parameter-reducing formulations and consecutive-hour-related regularizations achieve better performance than existing standard regression models and existing regularizations