Shock propagation in locally driven granular systems

We study shock propagation in a system of initially stationary hard-spheres that is driven by a continuous injection of particles at the origin. The disturbance created by the injection of energy spreads radially outwards through collision between particles. Using scaling arguments, we determine the exponent characterizing the power law growth of this disturbance in all dimensions. The scaling functions describing the various physical quantities are determined using large scale event driven simulations in two and three dimensions for both the elastic and the inelastic system. The results are shown to describe well the data from two different experiments on granular systems that are similarly driven.Comment: 8 pages, 9 figure

An Imperceptible Method to Monitor Human Activity by Using Sensor Data with CNN and Bi-directional LSTM

Deep learning (DL) algorithms have substantially increased research in recognizing day-to-day human activities All methods for recognizing human activities that are found through DL methods will only be useful if they work better in real-time applications.  Activities of elderly people need to be monitored to detect any abnormalities in their health and to suggest healthy life style based on their day to day activities. Most of the existing approaches used videos, static photographs for recognizing the activities. Those methods make the individual to feel anxious that they are being monitored. To address this limitation we utilized the cognitive outcomes of DL algorithms and used sensor data as input to the proposed model which is collected from smart home dataset for recognizing elderly people activity, without any interference in their privacy. At early stages human activities the input for human activity recognition by DL models are done using single sensor data which are static and lack in recognizing dynamic and multi sensor data. We propose a DL architecture based on the blend of deep Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) in this research which replaces human intervention by automatically extracting features from multifunctional sensing devices to reliably recognize the activities. During the entire investigation process we utilized Tulum, a benchmark dataset that contains the logs of sensor data. We exhibit that our methodology outperforms by marking its accuracy as 98.76% and F1 score as 0.98

A Fuzzy Set Theory Based Methodology for Analysis of Uncertainties in Stage-Discharge Measurements and Rating Curve

River stage and discharge records are essential for hydrological and hydraulic analyses. While stage is measured directly, discharge value is calculated from measurements of flow velocity, depth and channel cross-section dimensions. The measurements are affected by random and systematic measurement errors and other inaccuracies, such as approximation of velocity distribution and channel geometry with a finite number of measurements. Such errors lead to the uncertainty in both, the stage and the discharge values, which propagates into the rating curve established from the measurements. The relationship between stage and discharge is not strictly single valued, but takes a looped form due to unsteady flow in rivers. In the first part of this research, we use a fuzzy set theory based methodology for consideration of different sources of uncertainty in the stage and discharge measurements and their aggregation into a combined uncertainty. The uncertainty in individual measurements of stage and discharge is represented using triangular fuzzy numbers and their spread is determined according to the ISO – 748 guidelines. The extension principle based fuzzy arithmetic is used for the aggregation of various uncertainties into overall stage discharge measurement uncertainty. In the second part of the research we use fuzzy nonlinear regression for the analysis of the uncertainty in the single valued stage – discharge relationship. The methodology is based upon fuzzy extension principle. All input and output variables as well as the coefficients of the stage - discharge relationship are considered as fuzzy numbers. Two different criteria; the minimum spread and the least absolute deviation are used for the evaluation of output fuzziness. The results of the fuzzy regression analysis lead to a definition of lower and upper uncertainty bounds of the stage – discharge relationship and representation of discharge value as a fuzzy number. The third part of this research considers uncertainties in a looped rating curve with an application of the Jones formula. The Jones formula is based on approximate form of unsteady flow equation, which leads to an additional uncertainty. In order to take into account of the uncertainties due to the use of approximate formula and measurement of discharge values, the parameters of the Jones formula are considered fuzzy numbers. This leads to a fuzzified form of Jones formula. Its spread is determined by a multi-objective genetic algorithm. We used a criterion to minimize the spread of the fuzzified Jones formula so that the measurements points are bounded by the lower and upper bound curves. The study therefore considers individual sources of uncertainty from measurements to the single valued and looped rating curves. The study also shows that the fuzzy set theory provides an appropriate methodology for the analysis of the uncertainties in a nonprobabilistic framework.https://ir.lib.uwo.ca/wrrr/1023/thumbnail.jp

Comparison between simultaneous versus staged bilateral total knee arthroplasty: a prospective, randomized, controlled study

Background: Bilateral total knee arthroplasty (TKA) is a common procedure nowadays. Although, staging of surgery is a subject of debate. We conducted a study to compare safety and functional outcomes of simultaneous and staged bilateral TKA.Methods: Our study includes total 70 patients of symptomatic severe bilateral osteoarthritis, underwent simultaneous or staged bilateral TKA during 2015 to 2019. 35 patients were randomly allotted for each procedure. The postoperative evaluations were done according to Knee Society Score at one, three, six and 12 months and yearly thereafter for 2 years following a simultaneous bilateral TKA (group A) and the second procedure in the staged bilateral TKA (group B).  In the staged group, the patients were followed at monthly intervals until the second procedure. The categorical variables were statistically significant when p value <0.05.Results: As compared to staged procedure (group B), estimated blood loss was significantly less in simultaneous TKA (group A). Although, blood transfusion rate was significantly high in group A. The length of hospital stay was significantly shorter in group A. Overall complication rate (inpatient and/or 90 days readmission) was not significantly higher in group A. Knee infection rate was significantly lower in simultaneous TKA group. There was no revision of surgery and no mortality in any of our study group within 2 years of follow-up.Conclusions: Simultaneous bilateral TKA is safe and cost-effective procedure with acceptable complication rates for bilateral symptomatic end stage knee osteoarthritis