Single-Shot Characterization of Enzymatic Reaction Constants K(m) and k(cat) by an Acoustic-Driven, Bubble-Based Fast Micromixer

In this work we present an acoustofluidic approach for rapid, single-shot characterization of enzymatic reaction constants K(m) and k(cat). The acoustofluidic design involves a bubble anchored in a horseshoe structure which can be stimulated by a piezoelectric transducer to generate vortices in the fluid. The enzyme and substrate can thus be mixed rapidly, within 100 ms, by the vortices to yield the product. Enzymatic reaction constants K(m) and k(cat) can then be obtained from the reaction rate curves for different concentrations of substrate while holding the enzyme concentration constant. We studied the enzymatic reaction for β-galactosidase and its substrate (resorufin-β-D-galactopyranoside) and found K(m) and k(cat) to be 333 ± 130 μM and 64 ± 8 s(-1), respectively, which are in agreement with published data. Our approach is valuable for studying the kinetics of high-speed enzymatic reactions and other chemical reactions.     

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

Scientometrics - In-text citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata,...     

Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of CNN. Therefore, an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance. In this process, a hierarchical CNN model is designed, in which the former model is non-regularized (due to dense architecture) and the later one is regularized, specifically designed for small histopathology images. Moreover, the regularized model is integrated with CNN’s basic architecture to reduce overfitting. Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training. The training and testing accuracies of the non-regularized CNN model are 98% & 78% with an imbalanced dataset and 96% & 81% with a balanced dataset, respectively. The regularized CNN model training and testing accuracies are 84% & 75% for an imbalanced dataset and 87% & 86% for a balanced dataset.     

A new nonparametric double exponentially weighted moving average control chart

There are many practical situations where the underlying distribution of the quality characteristic either deviates from normality or it is unknown. In such cases, practitioners often make use of the nonparametric control charts. In this paper, a new nonparametric double exponentially weighted moving average control chart on the basis of the signed-rank statistic is proposed for monitoring the process location. Monte Carlo simulations are carried out to obtain the run length characteristics of the proposed chart. The performance comparison of the proposed chart with the existing parametric and nonparametric control charts is made by using various performance metrics of the run length distribution. The comparison showed the superiority of the suggested chart over its existing parametric and nonparametric counterparts. An illustrative example for the practical implementation of the proposed chart is also provided by using the industrial data set.     

Evaluation of peanut husk as a novel,low cost biosorbent for the removal of Indosol Orange RSN dye from aqueous solutions: batch and fixed bed studies

The feasibility of using peanut husk biomass for the removal of Indosol Orange RSN dye was explored during this study. Batch experiments were conducted with native, polyethyleneimine (PEI) treated and Na-alginate immobilized biomass. Different important process parameters like pH, contact time, biosorbent dose, initial dye concentration, and temperature were optimized during batch study. Low pH and low biosorbent dose were found to be the feasible conditions for the maximum biosorption of dye. PEI-treated biomass exhibited maximum biosorption capacity (79.5 mg g^?1) for Indosol Orange RSN dye. Pseudo-second-order equation generated the best agreement with experimental data. Different equilibrium isotherm models were applied to the experimental data. Langmuir adsorption isotherm model showed better fitness to the experimental results. Biosorption process was found to be exothermic in nature and thermodynamic study was carried out to check out the feasibility of process. Continuous mode study was performed with native peanut husk biomass to optimize the bed height, flow rate, and initial dye concentration for maximum dye removal. The results indicate that maximum dye removal (8.8 mg L^?1) was obtained with 3 cm bed height and 1.8 mL min^?1 flow rate by using 70 mg L^?1 initial dye concentration. Characterization of biosorbent was carried out by determination of point of zero charge, scanning electron microscopy, and Fourier transform infrared spectroscopy. The findings revealed that peanut husk biomass has a high biosorption potential, and it can be exploited for the treatment of dye containing waste water.     

Crystallization kinetics and structure of poly(trimethylene terepthalate)/monolayer nano-mica nanocomposites

In this work, the structure of poly(trimethylene terepthalate) (PTT)/monolayer nano-mica (MNM) nanocomposites are investigated by wide angle X-ray diffraction (WAXD), small angle X-ray scattering (SAXS) and scanning electron microscope (SEM). In the PTT nanocomposites, the crystallization induces the segregation of MNM in which three morphologies, including interlamellar, interfibrillar, and interspherulitic segregations, are observed with changing the MNM content. Avrami analysis of isothermal crystallization demonstrates that MNM enhances the bulk crystallization rate in the nanocomposites. Moreover, the non-integral values of Avrami exponent n between 2 and 4 with increasing crystallization temperature indicate the mixed growth and nucleation mechanisms. The analysis of secondary nucleation theory for neat PTT and the PTT nanocomposites exhibit the same regime transition of crystallization behaviour in which the classical transition temperatures of regime I to II and regime II to III take place at 488 K and 468 K, respectively. The growth rate of spherulites of the PTT nanocomposites is twofold larger than that of neat PTT in regime III, implying that MNM plays an effective role as a nucleating agent, since the addition of MNM enormously reduces the activation energy of nucleation, folding surface free energy and average work of chain folding for PTT nucleation. However, experimental results show that the MNM content below 1 wt% is the most effective for nucleation of PTT crystallization.     

An efficient double exponentially weighted moving average Benjamini‐Hochberg control chart to control false discovery rate

A control chart based on double exponentially weighted moving average and Benjamini‐Hochberg multiple testing procedure is proposed that controls the false discovery rate (FDR). The proposed control chart is based on probabilities (or P values) to accept or reject the null hypothesis of the underlying process is in control. To make a decision, instead of using only the current probability, previous “m” probabilities are considered. The performance of the control chart is evaluated in terms of average run length (ARL) using Monte Carlo simulations. Procedure for estimation of parameters used in the control chart is also discussed. The proposed control chart is compared with previous control charts and found to be more efficient in controlling the false discovery rate.     

Influence of absorber doping in a-SiC：H/a-Si：H/a-SiGe：H solar cells

This work deals with the design evaluation and influence of absorber doping for a-Si:H/a-SiC:H/a-SiGe:H based thin-film solar cells using a two-dimensional computer aided design (TCAD) tool. Various physical parameters of the layered structure, such as doping and thickness of the absorber layer, have been studied. For reliable device simulation with realistic predictability, the device performance is evaluated by implementing necessary models (e.g., surface recombinations, thermionic field emission tunneling model for carrier transport at the heterojunction, Schokley-Read Hall recombination model, Auger recombination model, bandgap narrowing effects, doping and temperature dependent mobility model and using Fermi-Dirac statistics). A single absorber with a graded design gives an efficiency of 10.1% for 800 nm thick multiband absorption. Similarly, a tandem design shows an efficiency of 10.4% with a total absorber of thickness of 800 nm at a bandgap of 1.75 eV and 1.0 eV for the top a-Si and bottom a-SiGe component cells. A moderate n-doping in the absorber helps to improve the efficiency while p doping in the absorber degrades efficiency due to a decrease in the V_OC (and fill factor) of the device.