Estimation of the Substrate Concentration Profiles in Petroleum Pipelines Containing Biofilms

In industrial systems, the formation of biofilms can cause many problems, such as an increase in the flow resistance of pipelines, energy losses in fluid transport and heat exchangers, product contamination, materials deterioration, and biocorrosion. As a result, biofilms contribute substantially to economic losses in the industry. Corrosion is particularly an issue in the petroleum industry and its implications range from down-hole completion through petroleum processing units. Much of this corrosion is attributed to microbial activities. This paper proposes a mathematical model for predicting substrate concentration for such microbial growth. Substrate concentrations in the system and near the biofilm surface are one of the parameters that has a great effect in determining the extent of the problems associated with biofilms. In this study, a convective-diffusion model under various flow conditions (stagnant, laminar, and turbulent) has been solved using the finite difference technique, employing the alternating direction implicit (ADI) method. The model assumes that a liquid containing substrate and bacteria is flowing in a pipeline with known concentrations at the inlet and then predicts the variation of the transient (as a function of time) substrate concentration along the pipeline and as a function of the pipe radius. The model is then used to predict and estimate the substrate concentration profiles on the biofilm surface under different environmental conditions. A parametric study was also conducted to study the effect of the different parameters influencing the substrate concentration profiles in the system and on the biofilm surface.     

Lipid peroxidation and lysosomal integrity in different inflammatory models in rats: the effects of indomethacin and naftazone

The authors tried to establish quantitative and qualitative acoustic parameters of a good voice, suitable for future voice professionals. In their work they used long-time average spectrum analysis (LTAS) and three-dimensional analysis of periodicity (3D-PAN). They consider the regression straight line of formant regions and the parameters offered by 3D-PAN--jitter first of all--as the main acoustic parameters for the evaluation of voice quality and draw attention to the fact that acoustic parameters represent only one part of the evaluation of voice quality.     

Generation of sodium hypochlorite (NaOCl) from sodium chloride solution using C/PbO_2 and Pb/PbO_2 electrodes

Two modified electrodes (Pb/PbO2 and C/PbO2) were prepared by electrodepositing a lead oxide layer on lead and carbon sub-strates. These modified electrodes were used as anodes for the generation of sodium hypochlorite (NaOCl) from sodium chloride solution. Different operating conditions and factors affecting the treatment process of NaOCl generation, including current density, pH values, con-ductive electrolytes, and electrolysis time, were studied and optimized. By comparison the C/PbO2 electrode shows a higher efficiency than the Pb/PbO2 electrode for the generation of NaOCl.     

Design methodology for a salt gradient solar pond coupled with an evaporation pond

This paper presents the results of a simple mathematical model for predicting the ratio of the evaporation pond (EP) area to that of a Salt Gradient Solar Pond (SGSP) area. The EP idea provides a very attractive method of salt recycling by evaporation, especially in areas of high rates of evaporation and low rates of rain as it is the case for North Africa. The model is applied for two types of surface water flushing (fresh water and seawater) under the prevailing conditions of Tripoli-Libya (Lat.=32.68°N) and for measured evaporation rates. Under the summer conditions and for the case of surface flushing by fresh water, the area ratio was estimated at about 0.17. While for the case of using seawater this ratio increases enormously to about 14.4. The time required for the salt concentration to increase from seawater concentration to a high concentrated brine, which can be injected at the bottom of the solar pond, is also presented. It was estimated that the time required to increase the salt concentration from 3.5 to 35% is about 120 to 250 days during the summer months and about 200 to 220 days during the winter months.     

Selection of optimal denoising-based regularization hyper-parameters for performance improvement in a sensor validation model

Multilayered auto-associative neural architectures have widely been used in empirical sensor modeling. Typically, such empirical sensor models are used in sensor calibration and fault monitoring systems. However, simultaneous optimization of related performance metrics, i.e., auto-sensitivity, cross-sensitivity, and fault-detectability, is not a trivial task. Learning procedures for parametric and other relevant non-parametric empirical models are sensitive to optimization and regularization methods. Therefore, there is a need for active learning strategies that can better exploit the underlying statistical structure among input sensors and are simple to regularize and fine-tune. To this end, we investigated the greedy layer-wise learning strategy and denoising-based regularization procedure for sensor model optimization. We further explored the effects of denoising-based regularization hyper-parameters such as noise-type and noise-level on sensor model performance and suggested optimal settings through rigorous experimentation. A visualization procedure was introduced to obtain insight into the internal semantics of the learned model. These visualizations allowed us to suggest an implicit noise-generating process for efficient regularization in higher-order layers. We found that the greedy-learning procedure improved the overall robustness of the sensor model. To keep experimentation unbiased and immune to noise-related artifacts in real sensors, the sensor data were sampled from simulators of a nuclear steam supply system of a pressurized water reactor and a Tennessee Eastman chemical process. Finally, we compared the performance of an optimally regularized sensor model with auto-associative neural network, auto-associative kernel regression, and fuzzy similarity-based sensor models.     

Cysts of median raphe

Middle raphe cysts are very uncommon lesions in the daily clinical practice and can go unnoticed based on their size. We contribute two cases where a quick growth is seen over the last few months. Their histological origin is discussed using specific staining and immunohistochemical techniques.     

Motion estimation with low resolution distortion metric

Considering all possible candidate motion vectors in a given search area and calculating a distortion measure at every search position, as with the full-search block-matching motion estimation algorithm (FSBME), places a prohibitively high computational burden on the video encoder, making it unsuitable for real-time/portable video applications. To reduce computational complexity while maintaining accuracy, a new version of the reduced-bit, sum of absolute difference (RBSAD) algorithm is presented, which allows for optional correction to full resolution (FSBME) when appropriate. Analysis of a number of video sequence shows that this correction is required for blocks for which there is little motion between frames.     

Optimized Intelligent Auto-Regressive Neural Network Model (ARNN) for Prediction of Non-Linear Exogenous Signals

Wireless Personal Communications - The paper presents a prediction of non-linear exogenous signal by optimized intelligent auto-regressive neural network model (ARNN). A signal comprises of two...     

Perception‐aware autonomous mast motion planning for planetary exploration rovers

Highly accurate real‐time localization is of fundamental importance for the safety and efficiency of planetary rovers exploring the surface of Mars. Mars rover operations rely on vision‐based systems to avoid hazards as well as plan safe routes. However, vision‐based systems operate on the assumption that sufficient visual texture is visible in the scene. This poses a challenge for vision‐based navigation on Mars where regions lacking visual texture are prevalent. To overcome this, we make use of the ability of the rover to actively steer the visual sensor to improve fault tolerance and maximize the perception performance. This paper answers the question of where and when to look by presenting a method for predicting the sensor trajectory that maximizes the localization performance of the rover. This is accomplished by an online assessment of possible trajectories using synthetic, future camera views created from previous observations of the scene. The proposed trajectories are quantified and chosen based on the expected localization performance. In this study, we validate the proposed method in field experiments at the Jet Propulsion Laboratory (JPL) Mars Yard. Furthermore, multiple performance metrics are identified and evaluated for reducing the overall runtime of the algorithm. We show how actively steering the perception system increases the localization accuracy compared with traditional fixed‐sensor configurations.     

SOLVENT EXTRACTION OF PAKISTAN COALS (III) EXTRACTION AND CHARACTERIZATION OF THE EXTRACTS BY SPECTROSCOPIC TECHNIQUES

The Makerwal coal of Pakistan gives higher extractibi1ity with Tetrahydrofuran ( THF ) as compared to Sharigh, Sor-Range and Lakhra coals. The structural characterization of Pakistan coal extracts by FT-IR spectroscopy indicates that all the extracts contained less condensed aromatic rings in comparision to their original coal.