Bubble Formation through Reaction at Liquid‐Liquid Interfaces

Slag foaming is an important phenomenon in steelmaking processes with both beneficial as well as negative effects. The present work is part of the wider project on the modelling of slag foaming, with special reference to dynamic conditions. Since bubble formation is the first step to foam formation, the present work was carried out in an attempt to simulate the bubble formation in slag/metal reactions in steelmaking processes by water‐modelling experiments. The bubble formation due to the gas produced through chemical reaction at the interface between oleic acid and sodium bicarbonate solution was systematically monitored. The chemical reaction rate was varied by varying the concentration of sodium bicarbonate. The bubbles were observed to be generated in the heavier aqueous phase just below the water‐oil interface. The bubbles penetrated the interface and escaped through the oil phase. The rate of the reaction was estimated from the volume of the gas that passed the water/oil interface. It was observed that the bubble formation and bubble growth mechanism were influenced by the reaction rate while the bubble size seemed to be unaffected by the reaction rate.     

Statistical motion model based on the change of feature relationships: human gait-based recognition

We offer a novel representation scheme for view-based motion analysis using just the change in the relational statistics among the detected image features, without the need for object models, perfect segmentation, or part-level tracking. We model the relational statistics using the probability that a random group of features in an image would exhibit a particular relation. To reduce the representational combinatorics of these relational distributions, we represent them in a Space of Probability Functions (SoPF), where the Euclidean distance is related to the Bhattacharya distance between probability functions. Different motion types sweep out different traces in this space. We demonstrate and evaluate the effectiveness of this representation in the context of recognizing persons from gait. In particular, on outdoor sequences: (1) we demonstrate the possibility of recognizing persons from not only walking gait, but running and jogging gaits as well; (2) we study recognition robustness with respect to view-point variation; and (3) we benchmark the recognition performance on a database of 71 subjects walking on soft grass surface, where we achieve around 90 percent recognition rates in the presence of viewpoint variation.     

Classification of heart rate data using artificial neural network and fuzzy equivalence relation

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.     

Determination of various impurities in synthetic diamond powder

Impurities in industrial synthetic diamond powder samples were analyzed by X-ray diffraction (XRD) and Flame Atomic Absorption Spectrometry (FAAS). Specimen for FAAS is required in solution form. Diamonds are chemically inert to most acids and alkalies. Carbon was removed as CO₂ on heating and estimated gravimetrically. The remaining residue was fused with di-lithium tetraborate and dissolved in nitric acid. Impurities such as Si, Al, Fe, Ca, Mg, W, Na, Co and Ni were then determined by FAAS. Crystalline phases of major impurities were identified by XRD.     

TMAO Suppresses Megalin Expression and Albumin Uptake in Human Proximal Tubular Cells Via PI3K and ERK Signaling

Trimethylamine-N-oxide (TMAO) is a uremic toxin, which has been associated with chronic kidney disease (CKD). Renal tubular epithelial cells play a central role in the pathophysiology of CKD. Megalin is an albumin-binding surface receptor on tubular epithelial cells, which is indispensable for urine protein reabsorption. To date, no studies have investigated the effect of TMAO on megalin expression and the functional properties of human tubular epithelial cells. The aim of this study was first to identify the functional effect of TMAO on human renal proximal tubular cells and second, to unravel the effects of TMAO on megalin-cubilin receptor expression. We found through global gene expression analysis that TMAO was associated with kidney disease. The microarray analysis also showed that megalin expression was suppressed by TMAO, which was also validated at the gene and protein level. High glucose and TMAO was shown to downregulate megalin expression and albumin uptake similarly. We also found that TMAO suppressed megalin expression via PI3K and ERK signaling. Furthermore, we showed that candesartan, dapagliflozin and enalaprilat counteracted the suppressive effect of TMAO on megalin expression. Our results may further help us unravel the role of TMAO in CKD development and to identify new therapeutic targets to counteract TMAOs effects.     

Improved indoor environment through optimised ventilator and furniture positioning: A case of slum rehabilitation housing,Mumbai, India

This study optimized the ventilator and furniture location of a tenement unit in a low-income urban habitat to obtain maximum experiential indoor environmental quality (e-IEQ) over the breathing zone. Hypothetical interior layouts using a combination of the two design parameters of ventilator location and bed position were generated for optimizing the design layout. This layout could promote maximum indoor airflow and minimum indoor air temperature and contaminant concentration. In this study, an improved indoor environment is hypothesized to be attainable through improved natural ventilation and thermal performance in the occupied zones. A sequential methodology involving “parametric design modeling–computational simulation–multiobjective optimization–multicriteria decision making”-based framework was selected. Results exhibited that the currently designed tenement unit had a poor indoor environment, whereas the hypothesized iterated layout “optimized design layout, scenario 3 (ODL 3)” derived from the optimization and decision-making algorithm performed effectively in providing e-IEQ. An increase in experiential indoor air velocity by 0.2 m/s and a decrease in temperature by 2 °C were observed over the monitoring point in the ODL 3 considering the existing scenario. Therefore, this study can find a way toward the development of sustainable habitat design guidelines under upcoming slum redevelopment policies across the nation.     

Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction

Tenderness is a primary determinant of consumer satisfaction of beef steaks. The objective of this study was to implement and test near-infrared (NIR) hyperspectral imaging to forecast 14-day aged, cooked beef tenderness from the hyperspectral images of fresh ribeye steaks (n = 319) acquired at 3–5 day post-mortem. A pushbroom hyperspectral imaging system (wavelength range: 900–1700 nm) with a diffuse-flood lighting system was developed. After imaging, steaks were vacuum-packaged and aged until 14 days postmortem. After aging, the samples were cooked and slice shear force (SSF) values were collected as a tenderness reference. After reflectance calibration, a Region-of-Interest (ROI) of 150 × 300 pixels at the center of longissimus muscle was selected. Partial least squares regression (PLSR) was carried out on each ROI image to reduce the dimension along the spectral axis. Gray-level textural co-occurrence matrix analysis with two quantization levels (64 and 256) was conducted on the PLSR bands to extract second-order statistical textural features. These features were then used in a canonical discriminant model to predict three beef tenderness categories, namely tender (SSF ≤ 205.80 N), intermediate (205.80 N < SSF < 254.80 N), and tough (SSF ≥ 254.80 N). The model with a quantization level of 256 performed better than the one with a quantization level of 64. This model correctly classified 242 out of 314 samples with an overall accuracy of 77.0%. Fat, protein, and water absorption bands were identified between 900 and 1700 nm. Our results show that NIR hyperspectral imaging holds promise as an instrument for forecasting beef tenderness.