Studies on influence of hydrogen and carbon monoxide concentration on reduction progression behavior of molybdenum oxide catalyst

Temperature programmed reduction (TPR) analysis was applied to investigate the chemical reduction progression behavior of molybdenum oxide (MoO₃) catalyst. The composition and morphology of the reduced phases were characterized by X-ray diffraction spectroscopy (XRD), X-ray photoelectron spectroscopy (XPS), and field emission scanning electron microscopy (FE-SEM). The reduction progression of MoO₃ catalyst was attained with different reductant types and concentration (10% H₂/N₂, 10% and 20% CO/N₂ (%, v/v)). Two different modes of reduction process were applied. The first approach of reduction involved non-isothermal mode reduction up to 700 °C, while the second approach of reduction involved the isothermal mode reduction for 60 min at 700 °C. Hydrogen temperature programmed reduction (H₂-TPR) results showed the reduction progression of three-stage reduction of MoO₃ (Mo⁶⁺ → Mo⁵⁺ → Mo⁴⁺ → Mo⁰) with Mo⁵⁺ and Mo⁴⁺. XRD analysis confirmed the formation of Mo₄O₁₁ phase as an intermediate phase followed by MoO₂ phase. After 60 min of isothermal reduction, peaks of metallic molybdenum (Mo) appeared. Whereas, FESEM analysis showed porous crater-like structure on the surface cracks of MoO₂ layer which led to the growth of Mo phase. Meanwhile, the reduction of MoO₃ catalyst in 10% carbon monoxide (CO) showed the formation of unstable intermediate phase of Mo₉O₂₆ at the early stage of reduction. Furthermore, by increasing 20% CO led to the carburization of MoO₂ phase, resulted in the formation of Mo₂C rather than the formation of metallic Mo, as confirmed by XPS analysis. Therefore, the presented study shows that hydrogen gave better reducibility due to smaller molecular size, which contributed to high diffusion rate and achieved deeper penetration into the MoO₃ catalyst compared to carbon monoxide reductant. Hence, the reduction of MoO₃ in carbon monoxide atmosphere promoted the formation of Mo₂C which was in agreement with the thermodynamic assessment.     

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.     

Influence of receptor number on the stimulation by salmeterol of gene transcription in CHO-K1 cells transfected with the human beta2-adrenoceptor

We investigated the behavior of the antioxidant enzymes, superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APx), in potato tubers during storage at low temperature. SOD activity increased temporarily within 3 weeks and was higher at 1 degree C than at 20 degrees C. APx activity also increased more at low (1 degree C) than at higher temperatures (5 and 20 degrees C). The contents of ascorbic acid (AsA), which is the substrate of APx, decreased immediately within 3 weeks and then gradually decreased until 15 weeks. The activity of CAT, the other enzyme which can scavenge hydrogen peroxide, decreased once in the first six weeks and thereafter increased to 15 weeks. Thus, the enhancement of the active oxygen-scavenging system that was induced by low temperature in potato tubers could result not only in a decrease of AsA but also in combined increases in APx and CAT activity whose manners were different.     

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.

     

Adaptive Periodic Noise Reduction in Digital Images Using Fuzzy Transform

Journal of Mathematical Imaging and Vision - Periodic noise degrades the image quality by overlaying similar patterns. This noise appears as peaks in the image spectrum. In this research, a method...     

Autonomous mapping of HL7 RIM and relational database schema

Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients. For this purpose, they take advantage of the full potential of current state of the art in healthcare standards providing interoperable solutions. HL7 V3 specification is an international message exchange and interoperability standard. HL7 V3 messages exchanged between healthcare applications are ultimately recorded into local healthcare databases, mostly in relational databases. In order to bring these relational databases in compliance with HL7, mappings between HL7 RIM (Reference Information Model) and relational database schema are required. Currently, RIM and database mapping is largely performed manually, therefore it is tedious, time consuming, error prone and expensive process. It is a challenging task to determine all correspondences between RIM and schema automatically because of extreme heterogeneity issues in healthcare databases. To reduce the amount of manual efforts as much as possible, autonomous mapping approaches are required. This paper proposes a technique that addresses the aforementioned mapping issue and aligns healthcare databases to HL7 V3 RIM specifications. Furthermore, the proposed technique has been implemented as a working application and tested on real world healthcare systems. The application loads the target healthcare schema and then identifies the most appropriate match for tables and the associated fields in the schema by using domain knowledge and the matching rules defined in the Mapping Knowledge Repository. These rules are designed to handle the complexity of semantics found in healthcare databases. The GUI allows users to view and edit/re-map the correspondences. Once all the mappings are defined, the application generates Mapping Specification, which contains all the mapping information i.e. database tables and fields with associated RIM classes and attributes. In order to enable the transactions, the application is facilitated with the autonomous code generation from the Mapping Specification. The Code Generator component focuses primarily on generating custom classes and hibernate mapping files against the runtime system to retrieve and parse the data from the data source—thus allows bi-directional HL7 to database communication, with minimum programming required. Our experimental results show 35–65% accuracy on real laboratory systems, thus demonstrating the promise of the approach. The proposed scheme is an effective step in bringing the clinical databases in compliance with RIM, providing ease and flexibility.     

Application of ANFIS for modeling of layer thickness of chromium carbonitride coating

In the present work, layer thickness of duplex coating made from thermo-reactive deposition and diffusion has been predicted by Adaptive network-based fuzzy inference systems (ANFIS). A duplex surface treatment on five steels has been developed involving nitrocarburizing and followed by chromium thermo-reactive deposition (TRD) techniques. The TRD process was performed in molten salt bath at 550, 625 and 700 °C for 1–30 h. The process formed a thickness up to 9.5 μm of chromium carbonitride coatings on a hardened diffusion zone. A model based on ANFIS for predicting the layer thickness of duplex coating of the specimens has been presented. To build the model, training and testing using experimental results from 84 specimens were conducted. The data used as inputs in ANFIS models are arranged in a format of twelve parameters that cover the chemical composition (C, Mn, Si, Cr, Mo, V, W), the pre-nitriding time, ferro-chromium particle size, ferro-chromium weight percent, salt bath temperature and coating time. According to these input parameters, in the Adaptive network-based fuzzy inference system models, the layer thickness of duplex coating of each specimen was predicted. The training and testing results in ANFIS models have shown a strong potential for predicting the layer thickness of duplex coating.     

In‐stream construction‐induced suspended sediment in riverine ecosystems

Construction activities within the wetted‐perimeter of rivers, referred to as in‐stream construction, are prevalent economically and environmentally motivated activities having direct interactions with sensitive lotic environments. Currently, there is a paucity of research related to in‐stream construction activities and their effects on aquatic ecosystems. In‐stream construction‐induced suspended sediment may result in harmful effects to aquatic flora and fauna. Regulatory frameworks worldwide focus primarily on concentration, with limited consideration for duration and no consideration for spatial extents of suspended sediment exposures. This research develops theoretical concentration, duration, and spatial extent exposure risk relationships for riverine ecosystems to demonstrate the influence of each mechanism during typical in‐stream construction activities. To reduce exposure risk, concentration and duration may be considered pragmatically, based on anticipated activity characteristics and site conditions. Spatial exposure characteristics are important to consider, as illustrated by our finding that activities located near the channel centerline may result in greater exposure risk than similar activities conducted near the streambank. Current regulatory frameworks worldwide do not sufficiently consider all exposure risk mechanisms present during in‐stream construction‐induced suspended sediment releases, possibly inhibiting efforts to reduce adverse environmental effects. This research improves our understanding of suspended sediment in lotic environments and may help environmental managers better evaluate and manage in‐stream construction activities.     

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.