On a new class of analytic functions associated with conic domain

The aim of this paper is to generalize the conic domain defined by Kanas and Wisniowska, and define the class of functions which map the open unit disk E onto this generalized conic domain. A brief comparison between these conic domains is the main motivation of this paper. A correction is made in selecting the range interval of order of conic domain.     

Translating the impact of knowledge management into knowledge‐based innovation: The neglected and mediating role of knowledge‐worker satisfaction

The purpose of knowledge management is innovation. However, this study proposes that the existence of knowledge management in the knowledge‐worker's work environment can nurture the overall satisfaction of knowledge worker. As the ultimate purpose of knowledge management is innovation performance, therefore, the satisfaction of knowledge worker as an outcome of knowledge management should be greater innovation performance. Consequently, the purpose of this study is to test the mediating role of satisfaction of knowledge worker between knowledge management and innovation. The study collected the data from 306 knowledge workers (engineers and managers) of software houses from Pakistan. The SmartPLS 3 Version 2.7 software that uses the PLS‐SEM (Partial Least Square‐Structural Equation Modelling) technique was used. The results indicate that satisfaction of knowledge worker mediates between two knowledge management processes (knowledge creation and knowledge sharing) and innovation significantly. However, it does not mediate between knowledge utilization and innovation significantly.     

Hero Wavelength Spectral Sampling

We present a spectral rendering technique that offers a compelling set of advantages over existing approaches. The key idea is to propagate energy along paths for a small, constant number of changing wavelengths. The first of these, the hero wavelength, is randomly sampled for each path, and all directional sampling is solely based on it. The additional wavelengths are placed at equal distances from the hero wavelength, so that all path wavelengths together always evenly cover the visible range. A related technique, spectral multiple importance sampling, was already introduced a few years ago. We propose a simplified and optimised version of this approach which is easier to implement, has good performance characteristics, and is actually more powerful than the original method. Our proposed method is also superior to techniques which use a static spectral representation, as it does not suffer from any inherent representation bias. We demonstrate the performance of our method in several application areas that are of critical importance for production work, such as fidelity of colour reproduction, sub‐surface scattering, dispersion and volumetric effects. We also discuss how to couple our proposed approach with several technologies that are important in current production systems, such as photon maps, bidirectional path tracing, environment maps, and participating media.     

Using artificial intelligence techniques for COVID-19 genome analysis

Applied Intelligence - The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province,...     

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.     

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.     

Esophageal atresia and tracheoesophageal fistula: success and failure rates in the United Arab Emirates

Randomized, prospective trials examining the effectiveness of carotid endarterectomy have given distinct information into the mechanisms and prevention of embolic strokes. The North American Symptomatic Carotid Endarterectomy Trial (NASCET) and the Asymptomatic Carotid Atherosclerosis Study (ACAS) have demonstrated the benefits of carotid endarterectomy for patients who meet the precise selection criteria and are treated according to established study protocols. The existing challenge to clinicians lies in the application of trial results to the individual patient.