The late-time development of the Richtmyer–Meshkov instability

Measurements have been made of the growth by the Richtmyer–Meshkov instability of nominally single-scale perturbations on an air/sulfur hexafluoride (SF6) interface in a large shock tube. An approximately sinusoidal shape is given to the interface by a wire mesh which supports a polymeric membrane separating the air from the SF6. A single shock wave incident on the interface induces motion by the baroclinic mechanism of vorticity generation. The visual thickness delta of the interface is measured from schlieren photographs obtained singly in each run and in high-speed motion pictures. Data are presented for delta at times considerably larger than previously reported, and they are tested for self-similarity including independence of initial conditions. Four different initial amplitude/wavelength combinations at one incident shock strength are used to determine the scaling of the data. It is found that the growth rate decreases rapidly with time, ddelta/dt[proportional]t–p (i.e., delta[proportional]t1–p), where 0.67<~p<~0.74 and that a small dependence on the initial wavelength lambda0 persists to large time. The larger value of the power law exponent agrees with the result of the late-time-decay similarity law of Huang and Leonard [Phys. Fluids 6, 3765–3775 (1994)]. The influence of the wire mesh and membrane on the mixing process is assessed

Extracting temporal and spectral parameters from surface electromyography signals during a fatigue contraction

This paper presents findings from a study of five healthy subjects performing 50% maximum voluntary contraction until complete fatigue of the muscle. An overlapping window technique was used to find the values for mean frequency (MNF), median frequency (MDF) of the power spectrum, root mean square (RMS) and muscle fibre conduction velocity (MFCV). The surface electromyography signal (sEMG) was collected from the vastus lateralis muscle using a three channel Laplacian electrode. The results show the MNF and MDF values showed a consistent trend with each other where they remained at steady values between 20-30% and 75-80% of the signal after which they fell 15-30% of this value. The RMS showed a linear increase in value. The MFCV showed a similar trend to that found in the MNF and MDF values

CNN Ensemble Approach for Early detection of Sugarcane Diseases – A Comparison

This paper mainly concentrates and discusses on sugarcane crop, the variety of cane seeds available for sowing; various cane diseases and its early detection using different approaches. Machine Learning (ML) and Deep Learning (DL) techniques are used to analyze agricultural data like temperature, soil quality, yield prediction, selling price forecasts, etc. and avoid crop damage from a variety of sources, including diseases. In the proposed work, with particular reference to eight specific sugarcane crop diseases and including healthy crop database, the neural network algorithms are tested and verified in terms quality metrics like accuracy, F1 score, recall and precision

Visualisation of an entangled channel spin-1 system

Co-variance matrix formalism gives powerful entanglement criteria for continuous as well as finite dimensional systems. We use this formalism to study a mixed channel spin-1 system which is well known in nuclear reactions. A spin-j state can be visualized as being made up of 2j spinors which are represented by a constellation of 2j points on a Bloch sphere using Majorana construction. We extend this formalism to visualize an entangled mixed spin-1 system.Comment: 4 pages,4 figure

Remote surface inspection system

This paper reports on an on-going research and development effort in remote surface inspection of space platforms such as the Space Station Freedom (SSF). It describes the space environment and identifies the types of damage for which to search. This paper provides an overview of the Remote Surface Inspection System that was developed to conduct proof-of-concept demonstrations and to perform experiments in a laboratory environment. Specifically, the paper describes three technology areas: (1) manipulator control for sensor placement; (2) automated non-contact inspection to detect and classify flaws; and (3) an operator interface to command the system interactively and receive raw or processed sensor data. Initial findings for the automated and human visual inspection tests are reported

Scale Up Sediment Microbial Fuel Cell for Powering Led Lighting

Sediment microbial fuel cells (SMFCs) are expected to be utilized as a sustainable power source for remote environmental observing 30 day's investigations of experiment to understand the long-term performance of SMFCs. The point of this investigation is to increase power generation, 8 individual sediment microbial fuel cells is stacked together either in series or in hybrid connection. Two combinations, of the hybrid connection, are proving to be the more effective one, step-up both the voltage and current of the framework, mutually. Polarization curve tests are done for series and hybrid connected sediment microbial fuel cell. The maximum study state voltage and current are obtained 8.150V and 435.25µA from series and 4.078V and 870.75µA hybrid connected SMFC. This study suggests that power of SMFC scale-up by connecting series and hybrid for practical use of the device.Article History: Received : September 26th 2017; Received: December 24th 2017; Accepted: January 4th 2018; Available onlineHow to Cite This Article: Prasad, J and Tripathi, R.K. (2018) Scale Up Sediment Microbial Fuel Cell For Powering Led Lighting. International Journal of Renewable Energy Development, 7(1), 53-58.https://doi.org/10.14710/ijred.7.1.53-5

A model based on clinical parameters to identify myocardial late gadolinium enhancement by magnetic resonance in patients with aortic stenosis: An observational study

Objective With increasing age, the prevalence of aortic stenosis grows exponentially, increasing left heart pressures and potentially leading to myocardial hypertrophy, myocardial fibrosis and adverse outcomes. To identify patients who are at greatest risk, an outpatient model for risk stratification would be of value to better direct patient imaging, frequency of monitoring and expeditious management of aortic stenosis with possible earlier surgical intervention. In this study, a relatively simple model is proposed to identify myocardial fibrosis in patients with a diagnosis of moderate or severe aortic stenosis. Design Patients with moderate to severe aortic stenosis were enrolled into the study; patient characteristics, blood work, medications as well as transthoracic echocardiography and cardiovascular magnetic resonance were used to determine potential identifiers of myocardial fibrosis. Setting The Royal Brompton Hospital, London, UK Participants One hundred and thirteen patients in derivation cohort and 26 patients in validation cohort. Main outcome measures Identification of myocardial fibrosis. Results Three blood biomarkers (serum platelets, serum urea, N-terminal pro-B-type natriuretic peptide) and left ventricular ejection fraction were shown to be capable of identifying myocardial fibrosis. The model was validated in a separate cohort of 26 patients. Conclusions Although further external validation of the model is necessary prior to its use in clinical practice, the proposed clinical model may direct patient care with respect to earlier magnetic resonance imagining, frequency of monitoring and may help in risk stratification for surgical intervention for myocardial fibrosis in patients with aortic stenosis