Identification of the flow structures and regime transition in gas–solid fluidized beds through moment analysis

Using statistic parameters of solids holdup signals, a moment consistency data processing method (MCDPM) was proposed. Experiments were carried out using FCC particles of 76 μm under different operating conditions, and MCDPM was used to successfully obtain solids holdups of the dense and dilute phases and the phase fractions over five fluidization regimes, bubbling (BFB), turbulent (TFB), circulating turbulent (CTFB), high‐density circulating (HDCFB), and circulating (CFB) fluidized bed systems. In BFB, TFB, and CTFB regimes, only dense phase fraction decreased with increasing air velocity, while the transition from HDCFB to CFB experienced appreciable change in the solids holdup of the dense phase. From the low‐velocity to the high‐velocity regimes, both the solids holdup and the fraction of the dense phase experienced a drastic decrease, suggesting that this transition corresponded to a profound change in flow structure and further suggesting that CTFB is in reality still a TFB. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1479–1490, 2013     

Effects of Rheological Behavior of Viscoelastic Surfactants on Formation Damage in Carbonate Rocks

Viscoelastic surfactants (VES) are used in various oilfield applications such as matrix stimulation and enhanced oil recovery. The loss of surfactants during the propagation of VES could result in a significant reduction in the permeability of the rock (formation damage). The objective of the current work was to identify the effect of rheological behavior of the VES on the formation damage using core‐flooding experiments, nuclear magnetic resonance (NMR), and scanning electron microscopy (SEM) analysis. A combination of core‐flooding, NMR, and SEM techniques was used to quantify and identify the location of formation damage in carbonate core samples. The viscosity and storage modulus strongly depend on the nature and concentration of salts. The viscosity increased by increasing the salt concentration up to a specific point (15 wt% CaCl₂) and then starts decreasing. The VES formulations that displayed the maximum and minimum viscosities were used to identify the impact of rheological behavior on formation damage. Core‐flooding experiments were performed to assess the formation damage due to high‐viscosity and low‐viscosity VES formulations. The reduction in the permeability of carbonate rocks reaches more than 90% of the initial permeability. It was found that low‐viscosity VES caused more damage compared with high‐viscosity VES when they were used at constant concentrations. NMR and core‐flooding results revealed that the damage took place both in pore body and pore throat. However, most of the surfactant was retained at the pore throat.     

Influence of surface treatments on fatigue life of Al 7010 alloy

The objective of the present work is to show the influence of machining and anodizing processes on fatigue life of alloy 7010-T7451. Two different cutting conditions were employed to obtain two different initial surface roughnesses. Degreasing, pickling and anodizing were then carried out. In the as machined condition, surface roughness is clearly effective in reducing fatigue life in this material. As the surface roughness increases fatigue life decreases and this effect is found to be more pronounced in high cycle fatigue where major portion of fatigue life is consumed in nucleating the cracks. Effects of pre-treatments, like degreasing and pickling employed prior to anodizing, on fatigue life of the given alloy were also studied. Fatigue curves showed that pickling had negative impact on fatigue life of specimens while degreasing showed no change in fatigue life. The small decrease in fatigue life of anodized specimens as compare to pickled specimens is attributed to brittle nature and micro-cracking of the coating. Scanning electron microscopic (SEM) examination revealed multi-site crack initiation for the pickled and anodized specimens. SEM examination also showed that pickling process attacked the grain boundaries and the inclusions present on the surface resulting in pits formation. These pits are of primary concern with respect to accelerated fatigue crack nucleation and subsequent anodized coating formation.     

Contrast behavior of a field lens with a wire‐grid PBS

Abstract— In an LCOS projection system, a wire‐grid PBS may be used to improve contrast and a field lens may be employed to simplify the projection lens. However, the combination of the two leads to a reduced contrast, which decreases with increasing field angle in one direction. In a representative arrangement, measured contrast with a mirror varied from over 10,000 in the center to 450 at the left and right edges. Contrast loss can be improved with a weaker field lens. The problem arises due to a phase delay between polarization states introduced by the wire grid. Polarization measurements show a +53‐nm phase delay with an axis parallel to the wire‐grid surface normal. Various compensation schemes are proposed in order to reduce the aforementioned loss of contrast.     

Image-driven virtual simulation of arthroscopy

In recent years, minimally invasive arthroscopic surgery has replaced a number of conventional open orthopedic surgery procedures on joints. While this achieves a number of advantages for the patient, the surgeons have to learn very different skills, since the surgery is performed with special miniature pencil-like tools and cameras inserted through little incisions while observing the surgical field on video monitor. Therefore, virtual reality simulation becomes an alternative to traditional surgical training based on hundreds years old apprentice–master model that involves either real patients or increasingly difficult to procure cadavers. Normally, 3D simulation of the virtual surgical field requires significant efforts from the software developers but yet remains not always photorealistic. In contrast to this, for photorealistic visualization and haptic interaction with the surgical field we propose to use real arthroscopic images augmented with 3D object models. The proposed technique allows for feeling the joint cavity displayed on video monitor as real 3D objects rather than their images while various surgical procedures, such as menisectomy, are simulated in real time. In the preprocessing stage of the proposed approach, the arthroscopic images are stitched into panoramas and augmented with implicitly defined object models representing deformable menisci. In the simulation loop, depth information from the mixed scene is used for haptic rendering. The scene depth map and visual display are reevaluated only when the scene is modified.     

A comparative study between gravel and rubber drainage columns for mitigation of liquefaction hazards

Liquefaction is one of the most destructive natural hazards that cause damage to engineering structures during an earthquake. This study aims to examine the effect of rubber and gravel drainage columns on the reduction of liquefaction potential of saturated sandy soils using a shaking table. Experiments were carried out in various conditions such as construction materials, different arrangements and diameters of drainage columns. Effects of the relative density and the input motion on the base test were investigated as well. The results demonstrate that rubber drainage columns have slightly better performance compared to gravel drainage columns at high relative density and high input acceleration. Soil improvement using gravel drainage columns, which leads to reduction in liquefaction effects at moderate input acceleration and low relative density, is a more effective method than that using rubber drainage columns. By increasing the number and diameter of gravel and rubber drainage columns, deformations due to liquefaction are reduced. The drainage rate of gravel drains is higher than that of rubber drains after shaking. Totally, the outcomes indicate that densification is the most important factor controlling liquefaction.     

Fatigue properties of hemp and glass fiber composites

The fatigue properties of nonwoven randomly oriented short hemp fiber mat and chopped strand mat (CSM) glass fiber reinforced polyester composites have been studied, mainly in tension–tension mode. Despite having poorer absolute fatigue strength, the hemp fiber composites exhibited less fatigue sensitivity as compared with the CSM glass fiber composites in tension–tension fatigue. This could be correlated with the lower stiffness degradation observed during fatigue of the hemp fiber composites as compared with the glass fiber composites at the same normalized peak stress levels. Also, images recorded during fatigue loading showed that the hemp fiber composites were better at resisting crack formation and growth than the glass fiber composites. These results suggest that hemp fiber composites have the potential to replace glass fiber composites in applications where components are subjected to fatigue loads but the stress levels are of moderate value. POLYM. COMPOS., 35:1926–1934, 2014. © 2014 Society of Plastics Engineers     

Detection of microscopic glaucoma through fundus images using deep transfer learning approach

Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter. However, advances in artificial intelligence approaches have permitted machine learning techniques to diagnose at an early stage. Numerous methods have been proposed using Machine Learning to diagnose glaucoma with different data sets and techniques but these are complex methods. Although, medical imaging instruments are used as glaucoma screening methods, fundus imaging specifically is the most used screening technique for glaucoma detection. This study presents a novel DenseNet and DarkNet combination to classify normal and glaucoma affected fundus image. These frameworks have been trained and tested on three data sets of high-resolution fundus (HRF), RIM 1, and ACRIMA. A total of 658 images have been used for healthy eyes and 612 images for glaucoma-affected eyes classification. It has also been observed that the fusion of DenseNet and DarkNet outperforms the two CNN networks and achieved 99.7% accuracy, 98.9% sensitivity, 100% specificity for the HRF database. In contrast, for the RIM1 database, 89.3% accuracy, 93.3% sensitivity, 88.46% specificity has been attained. Moreover, for the ACRIMA database, 99% accuracy, 100% sensitivity, 99% specificity has been achieved. Therefore, the proposed method is robust and efficient with less computational time and complexity compared to the literature available.     

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.