The impact of variable fluid properties on hydromagnetic boundary layer and heat transfer flows over an exponentially stretching sheet

This paper put forward an analysis of variable fluid properties and their impact on hydromagnetic boundary and thermal layers in a quiescent fluid which is developed due to the exponentially stretching sheet. The viscous incompressible fluid has been set into motion due to aforementioned sheet. We assume that the viscosity and the thermal conductivity of the Newtonian fluid are temperature dependent. The governing boundary layer equations containing continuity, momentum and energy equations are coupled and nonlinear in nature, thereby, cannot be solvable easily by using analytical methods. Since the general boundary layer equations admits a similarity solutions, thus a generalized Howarth-Dorodnitsyn transformations have been exploited for the set-up of a coupled nonlinear ODEs. These transformed ODEs are solved numerically by a shooting method and is verified from MATLAB built-in collocation solver bvp4c for different parameters appearing in the work. We show results graphically and in a tabulated form for a constant and a variable fluid properties. We find that the temperature dependent variable viscosity and a thermal conductivity influence a velocity and a temperature profiles. We show that the thermal boundary layer decreases for constant variable fluid properties and increases for variable fluid propertiesThe impact of variable fluid properties on hydromagnetic boundary layer and heat transfer flows over an exponentially stretching sheetpublishedVersio

A Simplified Finite Difference Method (SFDM) Solution via Tridiagonal Matrix Algorithm for MHD Radiating Nanofluid Flow over a Slippery Sheet Submerged in a Permeable Medium

In this paper, we turn our attention to the mathematical model to simulate steady, hydromagnetic, and radiating nanofluid flow past an exponentially stretching sheet. A numerical modeling technique, simplified finite difference method (SFDM), has been applied to the flow model that is based on partial differential equations (PDEs) which is converted to nonlinear ordinary differential equations (ODEs) by using similarity variables. For the resultant algebraic system, the SFDM uses the tridiagonal matrix algorithm (TDMA) in computing the solution. The effectiveness of numerical scheme is verified by comparing it with solution from the literature. However, where reference solution is not available, one can compare its numerical results with the results of MATLAB built-in package bvp4c. The velocity, temperature, and concentration profiles are graphed for a variety of parameters, i.e., Prandtl number, Grashof number, thermal radiation parameter, Darcy number, Eckert number, Lewis number, and Brownian and thermophoresis parameters. The significant effects of the associated emerging thermophysical parameters, i.e., skin friction coefficient, local Nusselt number, and local Sherwood numbers are analyzed and discussed in detail. Numerical results are compared from the available literature and found a close agreement with each other. It is found that the Eckert number upsurges the velocity curve. However, the dimensionless temperature declines with the Grashof number. It is also shown that the SFDM gives good results when compared with the results obtained from bvp4c and results from the literature.publishedVersio

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

The recognition of activities of daily living (ADL) in smart environments is a well-known and an important research area, which presents the real-time state of humans in pervasive computing. The process of recognizing human activities generally involves deploying a set of obtrusive and unobtrusive sensors, pre-processing the raw data, and building classification models using machine learning (ML) algorithms. Integrating data from multiple sensors is a challenging task due to dynamic nature of data sources. This is further complicated due to semantic and syntactic differences in these data sources. These differences become even more complex if the data generated is imperfect, which ultimately has a direct impact on its usefulness in yielding an accurate classifier. In this study, we propose a semantic imputation framework to improve the quality of sensor data using ontology-based semantic similarity learning. This is achieved by identifying semantic correlations among sensor events through SPARQL queries, and by performing a time-series longitudinal imputation. Furthermore, we applied deep learning (DL) based artificial neural network (ANN) on public datasets to demonstrate the applicability and validity of the proposed approach. The results showed a higher accuracy with semantically imputed datasets using ANN. We also presented a detailed comparative analysis, comparing the results with the state-of-the-art from the literature. We found that our semantic imputed datasets improved the classification accuracy with 95.78% as a higher one thus proving the effectiveness and robustness of learned models

Industry-School Interface: Following Professional Education Model to Impart Pragmatic Business Edification

This intercourse aims to identify means by which the business education system can be aided to effectively meet the needs of the business-operating environment. Despite of the popularity of business and management education critics have been talking about business education to be less relevant to business needs. This study focuses on exploring the modes of exchange of knowledge between business schools and industry to facilitate practical learning. It mainly aims to get a holistic view of the extent to which an industry and school collaboration based system can ensure pragmatism in business education along with promises, issues and challenges such industry school interface can offer. The data collection for this hypothesized model will be collected from business executives, business school management and alumni. This intercourse will offer new insight into the business education system. The study results will be valuable towards bringing improvements to the existing business education system. Keywords: Business Education, Management, School - Industry Interface, Pragmatism, Industr

The Impact of Working Capital Management on Firms Financial Performance: Evidence from Pakistan

The purpose of this study was to empirically explore the impact of working capital management on firms performance of chosen manufacturing firms listed in Karachi Stock Exchange (KSE). The quantitative research methods, correlation matrix and multiple regressions, secondary data and purposive sampling have been worked out. A random sample of 50 listed non-financial companies on Pakistani Stock Market was selected for the period ranging from year 2005 to 2014. The working capital management has been used as an independent variable i.e. inventory turnover (ITO), cash conversion cycle (CCC), average collection period (ACP), and average payment period (APP). The firm performance (FP) has been used as dependent variable i.e. Return on Asset (ROA), Return on Equity (ROE) and Earning per Share (EPS). The results of multiple regression articulated that the APP, ITO and CCC have negative and significant impact on ROA but ACP has positive and significant impact on ROA. While APP has negative significant impact on ROE. The Inventory turnover (ITO) has negative significant impact on EPS while ACP has positive and statistically significant impact on. The study results advocated that the firm performance of selected firms is influenced by working capital management. By validating the findings with previous researchers, this endeavor will contribute to the literature. It will be beneficial to the academic, social and practical deportment. The study findings endowed with deeper insights into working capital management practices and present recommendations that in turn bring improvements in the firm performance of the targeted firms. Keywords: Working capital, working capital management, financial performance, Pakistan JEL Classifications: C10, C30, D73, E37, F6

Occult nodal metastasis in oral cavity cancers

Introduction: In squamous cell carcinoma (SCC) of the oral cavity, there is always a risk of occult metastasis to neck nodes in the clinically and radiologically negative neck (N0). Therefore, elective neck dissection (END) has ever been under discussion since the beginning of their routine use for the management of neck for oral carcinomas. The purpose of the current study is to identify the percentage of occult nodal metastasis to neck levels I-V in the cases of oral carcinoma who were treated for the N0 with END.Methods: Patients who were treated between June 2005 and May 2010 with END from neck levels I to V for the management of N0 with oral SCC had been identified from the database of Aga Khan University Hospital. Those who met the inclusion and exclusion criteria were included in the study. Data were analyzed using SPSS 16 software. Using descriptive statistics, the mean was computed for the quantitative variable (age). Frequencies and percentages were calculated for gender, site, tumor grade, and lymph node involvement for each neck level.Results: A total of 50 patients were included in the study. There were 38 males and 12 females. The mean age was 47 (range 25-72). The most common site of the tumor was buccal mucosa in 50% of the cases followed by tongue 20%, then floor of mouth 14%, dentoalveolar ridge 8%, retromolar area 4%, lip 2%, and hard palate 2%. Histopathological grading of tumors showed well-differentiated 28%, moderately differentiated 33%, and poorly differentiated 6%. Twenty-seven out of 50 patients were found positive for nodal metastasis on final postoperative histopathology. Neck node metastasis at level I was found in 22 patients, at level II in 16 patients, at level III in seven patients, and at level IV in two patients. The level V was found free of metastasis in all of the cases.Conclusion: The rate of occult metastatic disease to the neck nodes was similar to that found in the literature. Both early and advanced local disease is associated with a risk of occult metastasis. END for neck levels I-V is, therefore, recommended for the management of the N0 in all cases of oral SCCs. Spread to levels IV and V is rare and these levels should not be a part of routine END

Two-phase frictional pressure drop with pure refrigerants in vertical mini/micro-channels

Environmental concerns have urged a search for eco-friendly refrigerants in the refrigeration industry to overcome ozone depletion and global warming problems. Therefore, current research emphasizes frictional pressure drop during flow boiling of environment-friendly refrigerants (GWP\u3c150), isobutane, HFC-152a, HFO-1234yf were tested against commonly reported HFC-134a. The data presented here was collected under heat flux-controlled conditions; the test piece was a round tube (1.60 mm diameter). The data collection was performed at 27 and 32 °C with mass velocities in 50-500 kg/m2s range. Effects of critical controlling parameters, like heat flux, mass velocity, exit vapor quality, operating pressure and medium, were studied in detail. It was observed that pressure drop increases along with mass velocity increment in the test piece and increases with exit vapor quality increment. The same was noticed to decrease with saturation temperature increment. Parametric effects and prediction of assessment methods are reported

Laparoscopic sleeve gastrectomy - a prospective follow up of 30 patients

Background: Morbid obesity has become a surgically treatable problem. Laparoscopic sleeve gastrectomy is becoming a popular choice both for surgeons and patients due to effectiveness and low complication rates. Methods: It was a prospective case series spanning over 6 years. Patient enrolment started from January 2009 and data collection completed in January 2015.Patient with BMI (weight in kilogram/height in meter square) of more than 35 were included in the study. Follow up was at 2 weeks, 1 month, 6 months and 12 months. Success was defined as 25% of excess weight loss at 1 year. Paired t-test was used as a test of significance.Results:A total of 34 patients were included in the study over a 6-year period, 3 were lost to follow up and one patient died of cardiac arrest. Data of 30 patients is considered for final analysis. Mean age was 39.5±10 years, while mean BMI 45.8±6.3 (range 37.1–62.2). Average weight of the patients pre-operatively was 129.9±20.8 kg while mean excess weight was 70.3±20.8 kg. Average weight loss at two weeks was 8.9±2.9 kg, at one month 14.7±4.6 kg, at 6 months 25.0±7.6 kg and at twelve months was 31.4±6.8 kg. Mean percentage of excess weight loss after 2 weeks was 13.5±4.6%, at one month 22.0±6.1%, at six months 37.6±12.0% and at twelve months 47.3±10.1%. Conclusion:Laparoscopic sleeve gastrectomy is an effective weight loss surgery with minimal complications. On average weight loss of about 30 kg at one year was achieved which equals to almost half of excess body weight

A Simplified Finite Difference Method (SFDM) Solution via Tridiagonal Matrix Algorithm for MHD Radiating Nanofluid Flow over a Slippery Sheet Submerged in a Permeable Medium

In this paper, we turn our attention to the mathematical model to simulate steady, hydromagnetic, and radiating nanofluid flow past an exponentially stretching sheet. A numerical modeling technique, simplified finite difference method (SFDM), has been applied to the flow model that is based on partial differential equations (PDEs) which is converted to nonlinear ordinary differential equations (ODEs) by using similarity variables. For the resultant algebraic system, the SFDM uses the tridiagonal matrix algorithm (TDMA) in computing the solution. The effectiveness of numerical scheme is verified by comparing it with solution from the literature. However, where reference solution is not available, one can compare its numerical results with the results of MATLAB built-in package bvp4c. The velocity, temperature, and concentration profiles are graphed for a variety of parameters, i.e., Prandtl number, Grashof number, thermal radiation parameter, Darcy number, Eckert number, Lewis number, and Brownian and thermophoresis parameters. The significant effects of the associated emerging thermophysical parameters, i.e., skin friction coefficient, local Nusselt number, and local Sherwood numbers are analyzed and discussed in detail. Numerical results are compared from the available literature and found a close agreement with each other. It is found that the Eckert number upsurges the velocity curve. However, the dimensionless temperature declines with the Grashof number. It is also shown that the SFDM gives good results when compared with the results obtained from bvp4c and results from the literature

Multimodal Sensor Data Fusion for Activity Recognition Using Filtered Classifier

Activity recognition (AR) is a subtask in pervasive computing and context-aware systems, which presents the physical state of human in real-time. These systems offer a new dimension to the widely spread applications by fusing recognized activities obtained from the raw sensory data generated by the obtrusive as well as unobtrusive revolutionary digital technologies. In recent years, an exponential growth has been observed for AR technologies and much literature exists focusing on applying machine learning algorithms on obtrusive single modality sensor devices. However, University of Jaén Ambient Intelligence (UJAmI), a Smart Lab in Spain has initiated a 1st UCAmI Cup challenge by sharing aforementioned varieties of the sensory data in order to recognize the human activities in the smart environment. This paper presents the fusion, both at the feature level and decision level for multimodal sensors by preprocessing and predicting the activities within the context of training and test datasets. Though it achieves 94% accuracy for training data and 47% accuracy for test data. However, this study further evaluates post-confusion matrix also and draws a conclusion for various discrepancies such as imbalanced class distribution within the training and test dataset. Additionally, this study also highlights challenges associated with the datasets for which, could improve further analysis