Semi-supervised model adaptation for statistical machine translation

Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French–English EuroParl data set and on data from the NIST Chinese–English large-data track. We show a significant improvement in translation quality on both tasks.     

Optimizing the Runoff Estimation with HEC-HMS Model Using Spatial Evapotranspiration by the SEBS Model

Most of the commonly used hydrological models do not account for the actual evapotranspiration (ETa) as a key contributor to water loss in semi-arid/arid regions. In this study, the HEC-HMS (Hydrologic Engineering Center Hydrologic Modeling System) model was calibrated, modified, and its performance in simulating runoff resulting from short-duration rainfall events was evaluated. The model modifications included integrating spatially distributed ETa, calculated using the surface energy balance system (SEBS), into the model. Evaluating the model’s performance in simulating runoff showed that the default HEC-HMS model underestimated the runoff with root mean squared error (RMSE) of 0.14 m³/s (R²?=?0.92) while incorporating SEBS ETa into the model reduced RMSE to 0.01 m³/s (R²?=?0.99). The integration of HECHMS and SEBS resulted in smaller and more realistic latent heat flux estimates translated into a lower water loss rate and a higher magnitude of runoff simulated by the HECHMS model. The difference between runoff simulations using the default and modified model translated into an average of 95,000 m³ runoff per rainfall event (equal to seasonal water requirement of ten-hectare winter wheat) that could be planned and triggered for agricultural purposes, flood harvesting, and groundwater recharge in the region. The effect of ETa on the simulated runoff volume is expected to be more pronounced during high evaporative demand periods, longer rainfall events, and larger catchments. The outcome of this study signifies the importance of implementing accurate estimates of evapotranspiration into a hydrological model.

     

A model to predict the solubility and permeability of gaseous penetrant in the glassy polymeric membrane at high pressure

In this work, the transport properties of gaseous penetrant through several dense glassy polymeric membranes are studied. The nonequilibrium lattice fluid (NELF) in conjunction with the modified Fick's law and dual mode sorption model was used to simulate the gas transport in glassy polymeric membranes. The approach is based on the sorption, diffusion, in which solubility is calculated based on the NELF model, and diffusion coefficient is obtained from the product thermodynamic coefficient and molecular mobility. The governing equation is solved by the finite element method using COMSOL multi-physics software. The developed model for gas permeability of glassy polymeric membrane can be applied in a wide range of pressure and temperature. The comparison of the calculated permeability and solubility of gasses with the experimental data represented the ability of the developed model. Increasing feed gas temperature increases the gas permeability, while this variation leads to lower gas solubility in the glassy polymeric membranes. The effect of feed temperature and pressure on permeability and solubility is investigated, and the experimental data from literature are described by the developed model. A good prediction of the experimental data can be observed over the considered condition.     

Data-Driven Texture Rendering on an Electrostatic Tactile Display

In this article, we present a data-driven texture rendering method applied to a tactile display based on electrostatic attraction. The proposed method was examined in two steps. First, accelerations occurring due to sliding a tool on three different surfaces were measured, and then the collected data were replayed on an electrostatic tactile display. The proposed data-driven texture rendering method was evaluated against a conventional method in which a standard input such as a square wave was used for texture representation. Second, data from the Penn Haptic Texture Toolkit were used to generate virtual textures on the same tactile display. Psychophysical experiments were carried out for both steps, during which subjects rated similarities among the rendered virtual textures and the real samples. Confusion matrices were created, and multidimensional scaling (MDS) analysis was performed to create a perceptual space for further examination and to extract underlying dimensions of the textures. The results show that the virtual textures generated using the data-driven method were similar to the real textures. Roughness and stickiness were the primary dimensions of texture perception. Together with the supporting results from the MDS analysis, this study showed that the data-driven method is a viable solution for realistic texture rendering with electrostatic attraction.     

Evaluation of Antioxidant and Antimicrobial Activities of Essential Oils from Carum copticum Seed and Ferula assafoetida Latex

Carum copticum and Ferula assafoetida have several medicinal properties including antispasmodic, carminative, sedative, analgesic, and antiseptic. Reactive oxygen species (ROS), reactive nitrogen species (RNS), hydrogen peroxide (H₂O₂), and thiobarbituric acid reactive substances (TBARS) scavenging activities of Carum and Ferula oils along with their antibacterial and antifungal activities were examined. Thymol (40.25%), γ‐terpinene (38.7%) and p‐cymene (15.8%) were detected as the main components of Carum oil while, β‐pinene (47.1%), α‐pinene (21.36%), and 1, 2‐dithiolane (18.6%) were the main components of Ferula oil. Inhibitory concentrations (IC50) for total radical scavenging were between 40 and 60 and 130 and 160 μg/mL of Carum and Ferula oil, respectively. Minimal inhibitory concentration (MIC) for Salmonella typhi, Escherichia coli, Staphylococcus aureus, Bacillus subtilis, Aspergillus niger, and Candida albicans were 78 ± 8, 65 ± 7, 14 ± 3, 5 ± 2, 5.6 ± 1.3, and 8.8 ± 2.2 μg/mL of Carum oil, respectively. MIC for S. typhi, E. coli, S. aureus, B. subtilis, A. niger, and C. albicans were >200, >200, 125 ± 17, 80 ± 12, 85 ± 5, and 90 ± 11 μg/mL of Ferula oil, respectively. Accordingly, Carum and Ferula oils could be used as safe and effective natural antioxidants to improve the oxidative stability of fatty foods during storage and to preserve foods against food burn pathogens. Practical Application : This study clearly demonstrates the potential of Carum and Ferula oil especially Carum oil as natural antioxidant and antimicrobial agent. The chemical composition of essential oils was identified. Thus, identification of such compounds also helps to discover of new antioxidant, antibacterial and antifungal agents for potential applications in food safety and food preservation.     

Study of the performance of dry methane reforming in a microchannel reactor using sputtered Ni/Al2O3 coating on stainless steel

In this study, a microchannel reactor was designed, its catalytic performance in dry methane reforming (DRM) was assessed, and the results were compared with those observed in a conventional fixed bed reactor. The catalyst was prepared in two forms, including catalyst pellets and catalyst-coated plate. The microchannel reactor had thin films of Ni/Al₂O₃ coated on stainless steel substrate via radio frequency (RF) magnetron sputtering method in various sputtering times. The fall-off rate of the catalyst-coated plates can be neglected after putting the plates under the high-temperature DRM reaction, due to the formation of firm active catalyst coatings. The performance of the samples was evaluated at different temperatures from 700 to 800 °C, at P = 1 atm, with a CH₄:CO₂ ratio of 1. The results of XRD showed that with increasing the sputtering time, there was an increase in crystallinity. As observed in FESEM images, the sample prepared with 5 min of sputtering was dense and uniform. The results of EDX not only proved the dispersion of the samples observed in XRD and FESEM analysis, but also verified the presence of the utilized elements. The temperature of 800 °C and the sample with 5 min sputtering time were selected as the optimum condition that provided the best performance. Catalytic performance was investigated in fixed bed reactor at the same GHSV; based on the results there were no significant conversions in the fixed bed reactor. The results of the stability test in the microchannel reactor showed a good performance during 30 h on stream. Therefore, Ni/Al₂O₃ thin films had a satisfactory performance in the designed microchannel. Our study shows that this type of reactor has many advantages in terms of performance, compactness, and economic concerns.     

Mixed Biogeography-Based Optimization for GENCOs’ Maintenance Scheduling in Restructured Power Systems

Power industry restructuring has brought new challenges to the generation unit maintenance scheduling problem. Maintenance scheduling establishes the outage time scheduling of units in a particular time horizon. In the restructured power systems, the decision-making process is decentralized where each generating company (GENCO) tries to maximize its own benefit. Therefore, the principle to draw up the unit maintenance scheduling is different from the traditional centralized power systems. The objective function for GENCOs is to minimize his maintenance investment loss. Therefore, he hopes to put its maintenance on the weeks when the market-clearing price is lowest so that maintenance investment loss descends. This paper addresses the unit maintenance scheduling problem of GENCOs in restructured power systems. The problem is formulated as a mixed integer programming problem, and it is solved by using an optimization method known as biogeography-based optimization (BBO). BBO is simple to implement in practice and requires a reasonably small amount of computing time and a small amount of data communication. BBO has been tested by applying it to a GENCO with three generating units. This model consists of an objective function and related constraints, e.g., maintenance window, generation capacity, load and network flow. The simulation result of this method is compared with a classic method. The outcome is very encouraging and proves that BBO is powerful for minimizing GENCOs’ objective function.