Structural Control of NiO–YSZ/LSCF–YSZ Dual‐Layer Hollow Fiber Membrane for Potential Syngas Production

The objective of this study was to fabricate dual‐layer hollow fiber as a microreactor for potential syngas production via phase inversion‐based co‐extrusion/cosintering process. As the main challenge of phase inversion is the difficulty to obtain defect‐free fiber, this work focuses on the effect of the fabrication parameters, that is, nonsolvent content, sintering temperature and outer‐layer extrusion rate, on the macrostructure of the produced hollow fiber. SEM images confirm that the addition of nonsolvent has successfully minimized the finger‐like formation. At high sintering temperature, more dense hollow fiber was formed while outer‐layer extrusion rate affects the outer layer thickness.     

Improving Rainfall Forecasting Efficiency Using Modified Adaptive Neuro-Fuzzy Inference System (MANFIS)

Rainfall is one of the most complicated effective hydrologic processes in runoff prediction and water management. The adaptive neuro-fuzzy inference system (ANFIS) has been widely used for modeling different kinds of nonlinear systems including rainfall forecasting. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different kinds of problems, especially efficient in rainfall prediction. This paper after reconsidering conventional ANFIS architecture brings up a modified ANFlS (MANFlS) structure developed with attention to making ANFIS technique more efficient regarding to Root Mean Square Error (RMSE), Correlation Coefficient (R ²), Root Mean Absolute Error (RMAE), Signal to Noise Ratio (SNR) and computing epoch. The modified ANFIS (MANFIS) architecture is simpler than conventional ANFIS with nearly the same performance for modeling nonlinear systems. In this study, two scenarios were introduced; in the first scenario, monthly rainfall was used solely as an input in different time delays from the time (t) to the time (t-4) to conventional ANFIS, second scenario used the modified ANFIS to improve the rainfall forecasting efficiency. The result showed that the model based Modified ANFIS performed higher rainfall forecasting accuracy; low errors and lower computational complexity (total number of fitting parameters and convergence epochs) compared with the conventional ANFIS model.     

Crop yield prediction from remotely sensed vegetation indices and primary productivity in arid and semi-arid lands

Global demands for biomass and arable lands are expected to double in the next 35 years. Scarcity of water resources in arid and semi-arid areas poses a serious threat to their agricultural productivity and hence their food security. In this study, we examine whether crop yields can be predicted from remotely sensed vegetation indices and remotely sensed estimates of primary productivity. Spatial relationships between remotely sensed enhanced vegetation index (EVI), net photosynthesis (P_Net), and gross and net primary production (GPP and NPP, respectively) in irrigated semi-arid and arid agro-ecosystems since the beginning of the century are analysed. The conflict-affected country of Syria is selected as the case study. Relationships between EVI and crop yield are investigated in an effort to enhance food production estimates in affected areas outside governmental jurisdictions. Estimates of NPP derived from reported irrigated agriculture crop data in a semi-arid and an arid zone are compared to remotely sensed NPP in a geospatial environment. Results show that winter crop yields are correlated with spring GPP in semi-arid zones of the study area (R² = 0.85). Summer crop yield can be predicted from either cumulative summer EVI (R² = 0.77) or P_Net in most zones. Where fully irrigated fields are surrounded by hyper-arid landscape, summer P_Net was negative in all instances and EVI was inversely correlated with yield. NPP from crops was much higher (290 gC m^?2 year^?1) in those regions than MOD17 NPP (70 gC m^–2), where 1.0 g of carbon is equivalent to 2.2 g of oven-dry organic matter (= 45% carbon by weight). The gap was less in semi-arid zones (2–39% difference). Overall crop-derived NPP for the period 2000–2013 was 322 versus 300 gC m^–2 for that remotely sensed within the cropped zones of the political units. The results of this study are crucial to derive accurate estimates of irrigated agriculture productivity and to study the effect of the latter on net ecosystem carbon storage.     

Prussian Blue electrodeposition within an oriented mesoporous silica film: preliminary observations

Prussian Blue (PB) has been electrochemically grown through thin mesoporous silica films of novel generation, exhibiting regular arrays of hexagonally packed mesopores channels (3 nm in diameter) oriented perpendicular to the underlying electrode surface. Due to confinement effects, special care has to be taken to ensure effective permeability of PB precursors through the hard silica template, which was best achieved by pulsed electrodeposition from a high ionic strength medium (2 M KCl). Energy dispersive X-ray spectroscopy associated to electron microscopy was used to evidence the presence of PB in the vertically-aligned mesopores, which was also assessed by their electrocatalytic behavior towards H₂O₂ reduction.     

The catalytic deacidification of acidic crude oil using Cu-doped alkaline earth metal oxide catalysts

Naphthenic acids (NAs) tend to cause operational problems that can lead to the deactivation of catalysts. To overcome the problem, catalytic deacidification was introduced utilizing an ammonia solution in ethylene glycol with the aids of alkaline earth metal catalyst with alumina as a support. The initial total acid number observed for NAs in n-dodecane was 4.21 mg KOH/g. In total, 1,000 mg/L of 0.4% NH₃-EG were used as the acid removal agent. Calcium, barium, and magnesium catalysts were tested in this study. The results showed Cu/Ca/Al₂O₃ was found to be the best catalyst that could be used to enhance the reaction.     

Magnetic Force Microscopy in Liquids

In this work, the use of magnetic force microscopy (MFM) to acquire images of magnetic nanostructures in liquid environments is presented. Optimization of the MFM signal acquisition in liquid media is performed and it is applied to characterize the magnetic signal of magnetite nanoparticles. The ability for detecting magnetic nanostructures along with the well‐known capabilities of atomic force microscopy in liquids suggests potential applications in fields such as nanomedicine, nanobiotechnology, or nanocatalysis.     

Evaluating Efficiency of Nondestructive Detection of Flaws in Concrete

Tests have been performed to assess feasibility of using the impact-echo method (IEM) and ultrasonic pulse velocity method (UPVM) in detecting flaws and evaluating their depths on the early stage of a concrete structure's service life. Five reinforced concrete (RC) slabs of grades 25, 30, 40, and 50 with fabricated voids at known locations were studied on the 3rd, 7th, 14th, and 28th day. The results were compared to estimate the accuracy of both methods and their efficiency. Both methods detected flaws in the tested samples on the early stages of their operation. Depth measurements of flaws by IEM, however, are more accurate. Results of our tests indicate that both methods can be used in determining depths of flaws in concrete and in performing on-site tests. The accuracy of both methods proved to be better in tests of stronger concretes, and the detection probability ranged from 51.81 to 99.8% after aging during 3 to 28 days under full load, respectively.     

Simulation of Image Fusion of Dual Modality (Electrical Capacitance and Optical Tomography) in Solid/Gas Flow

This paper presents a novel method of combining dual modality electrical capacitance and optical tomography for applications in monitoring and investigating solid/gas flow. The objective of this method is to obtain a good quality image of the full-scale concentration distribution of solid/gas flow. A new image reconstruction algorithm fused the dual modality images is developed and evaluated.     

Enabling novel premium service classes in DiffServ over MPLS‐enabled network

Network resources dimensioning and traffic engineering influence the quality in provisioned services required by the Expedited Forwarding (EF) traffic in production networks established through DiffServ over MPLS‐enabled network. By modeling EF traffic flows and the excess of network resources reserved for it, we derive the range of delay values which are required to support these flows at DiffServ nodes. This enables us to develop an end‐to‐end (e2e) delay budget‐partitioning mechanism and traffic‐engineering techniques within a framework for supporting new premium QoS levels, which are differentiated based on e2e delay, jitter and loss. This framework enables ingress routers to control EF traffic flow admission and select appropriate routing paths, with the goal of EF traffic balancing, avoiding traffic congestion and getting the most use out of the available network resources through traffic engineering. As a result, this framework should enable Internet service providers to provide three performance levels of EF service class to their customers provided that their network is DiffServ MPLS TE aware. Copyright © 2008 John Wiley & Sons, Ltd.