Mechanism of Water Droplet Breakup Near the Leading Edge of an Airfoil

This work presents results of an experimental study on droplet deformation and breakup near the leading edge of an airfoil. The experiment was conducted in the rotating rig test cell at the Instituto Nacional de Tecnica Aeroespacial (INTA) in Madrid, Spain. The airfoil model was placed at the end of the rotating arm and a monosize droplet generator produced droplets that fell from above, perpendicular to the path of the airfoil. The interaction between the droplets and the airfoil was captured with high speed imaging and allowed observation of droplet deformation and breakup as the droplet approached the airfoil near the stagnation line. Image processing software was used to measure the position of the droplet centroid, equivalent diameter, perimeter, area, and the major and minor axes of an ellipse superimposed over the deforming droplet. The horizontal and vertical displacement of each droplet against time was also measured, and the velocity, acceleration, Weber number, Bond number, Reynolds number, and the drag coefficients were calculated along the path of the droplet to the beginning of breakup. Droplet deformation is defined and studied against main parameters. The high speed imaging allowed observation of the actual mechanism of breakup and identification of the sequence of configurations from the initiation of the breakup to the disintegration of the droplet. Results and comparisons are presented for droplets of diameters in the range of 500 to 1800 microns, and airfoil velocities of 70 and 90 m/sec

Drag Coefficient of Water Droplets Approaching the Leading Edge of an Airfoil

This work presents results of an experimental study on droplet deformation and breakup near the leading edge of an airfoil. The experiment was conducted in the rotating rig test cell at the Instituto Nacional de Tecnica Aeroespacial (INTA) in Madrid, Spain. An airfoil model was placed at the end of the rotating arm and a monosize droplet generator produced droplets that fell from above, perpendicular to the path of the airfoil. The interaction between the droplets and the airfoil was captured with high speed imaging and allowed observation of droplet deformation and breakup as the droplet approached the airfoil near the stagnation line. Image processing software was used to measure the position of the droplet centroid, equivalent diameter, perimeter, area, and the major and minor axes of an ellipse superimposed over the deforming droplet. The horizontal and vertical displacement of each droplet against time was also measured, and the velocity, acceleration, Weber number, Bond number, Reynolds number, and the drag coefficients were calculated along the path of the droplet to the beginning of breakup. Results are presented and discussed for drag coefficients of droplets with diameters in the range of 300 to 1800 micrometers, and airfoil velocities of 50, 70 and 90 meters/second. The effect of droplet oscillation on the drag coefficient is discussed

Smart Cupboard for Assessing Memory in Home Environment

Sensor systems for the Internet of Things (IoT) make it possible to continuously monitor people, gathering information without any extra effort from them. Thus, the IoT can be very helpful in the context of early disease detection, which can improve peoples'' quality of life by applying the right treatment and measures at an early stage. This paper presents a new use of IoT sensor systemswe present a novel three-door smart cupboard that can measure the memory of a user, aiming at detecting potential memory losses. The smart cupboard has three sensors connected to a Raspberry Pi, whose aim is to detect which doors are opened. Inside of the Raspberry Pi, a Python script detects the openings of the doors, and classifies the events between attempts of finding something without success and the events of actually finding it, in order to measure the user''s memory concerning the objects'' locations (among the three compartments of the smart cupboard). The smart cupboard was assessed with 23 different users in a controlled environment. This smart cupboard was powered by an external battery. The memory assessments of the smart cupboard were compared with a validated test of memory assessment about face-name associations and a self-reported test about self-perceived memory. We found a significant correlation between the smart cupboard results and both memory measurement methods. Thus, we conclude that the proposed novel smart cupboard successfully measured memory

Birthmark based identification of software piracy using Haar wavelet

Piracy of software is an increasing problem of modern day software industry. Piracy of software is the unlawful use of software or part of it without proper permission as described in license agreement. Software piracy is a serious crime but not taken seriously by most people. Preventing software piracy is very important for the growing software industry. Efforts are being made to prevent and detect software piracy. Several techniques have been developed most important of which is software birthmark. The birthmark of a software is the intrinsic properties of software. A recent research shows that a features based software birthmark can be used as a strong mechanism to detect piracy of a software and how much piracy performed has been performed on it. An objective measure is needed to overcome this problem and to compare features based birthmark of a software which efficiently and precisely detect piracy in reproduction of software. The proposed study presents Haar wavelet collocation method for software features (birthmark) to detect piracy. The proposed method gives an exclusive solution for the features based birthmark of software and is then further used for comparisons of birthmark. The results of the proposed study show the effectiveness in terms of accuracy and efficiency to compare the features based software

A comprehensive analysis of healthcare big data management, analytics and scientific programming

Healthcare systems are transformed digitally with the help of medical technology, information systems, electronic medical records, wearable and smart devices, and handheld devices. The advancement in the medical big data, along with the availability of new computational models in the field of healthcare, has enabled the caretakers and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. The role of medical big data becomes a challenging task in the form of storage, required information retrieval within a limited time, cost efficient solutions in terms care, and many others. Early decision making based healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Scientific programming play a significant role to overcome the existing issues and future problems involved in the management of large scale data in healthcare, such as by assisting in the processing of huge data volumes, complex system modelling, and sourcing derivations from healthcare data and simulations. Therefore, to address this problem efficiently a detailed study and analysis of the available literature work is required to facilitate the doctors and practitioners for making the decisions in identifying the disease and suggest treatment accordingly. The peer reviewed reputed journals are selected for the accumulated of published research work during the period ranges from 2015 - 2019 (a portion of 2020 is also included). A total of 127 relevant articles (conference papers, journal papers, book section, and survey papers) are selected for the assessment and analysis purposes. The proposed research work organizes and summarizes the existing published research work based on the research questions defined and keywords identified for the search process. This analysis on the existence research work will help the doctors and practitioners to make more authentic decisions, which ultimately will help to use the study as evidence for treating patients and suggest medicines accordingly

Entangled Quantum States of Magnetic Dipoles

Free magnetic moments usually manifest themselves in Curie Laws, where weak external magnetic fields produce magnetizations diverging as the reciprocal 1/T of the temperature. for a variety of materials that do not disply static magnetism, including doped semiconductors and certain rare earth intermetallics, the 1/T law is changed to a power law T^-a with a<1. We report here that a considerably simpler material, namely an insulating magneticsalt can also display such a power law, and show via comparison to specific heat data and numerical simulations that quantum mechanics is crucial for its formation. Two quantum mechanical phenomena are needed, namely level splitting - which affects the spectrum of excited states - and entanglement - where the wavefunction of a system with several degrees of freedom cannot be written as a product of wavefunctions for each degree of freedom. Entanglement effects become visible for remarkably small tunnelling terms, and are turned on well before tunnelling has visible effects on the spectrum. Our work is significant because it illustrates that entanglement is at the very heart of a very simple experimental observation for an insulating quantum spin system.Comment: 17 pages, 4 figure

Multivariate statistical approaches for wine classification based on low molecular weight phenolic compounds

Background and Aims: Phenolic compounds influence the colour, flavour and astringency of wines. These compounds are extracted into the wine during grape fermentation and maceration and thus the winemaking process is the main factor affecting the phenolic content of wines, besides the varietal factor. In this work, we aimed to apply self organizing maps to investigate the relationships between the profile of phenolic compounds and grape variety of wines, as well as the changes in the phenolic profile resulting from the malolactic fermentation. The results are compared with principal component analysis, and variation partitioning. Methods and Results: A reversed phase liquid chromatography/DAD method was used for the analysis of major non-flavonoid phenolic compounds in wines. The method employed allowed to evaluate the impact of malolactic fermentation in low molecular phenolic compounds in different wine varieties: Trincadeira, Aragonez, Cabernet Sauvignon, Alfrocheiro, Castelão and Touriga Nacional. The malolactic fermentation process was also study in Trincadeira variety using indigenous bacteria and two different commercial lactic bacteria. The impact of malolactic fermentation and grape varieties on the phenolic profile was evaluated by different multivariate statistical approaches: principal component analysis, variation partitioning analysis and artificial neural network. Conclusions: Principal component analysis allowed to explain 86.5% of the total variance among samples, without any additional information. Artificial neural network showed a significant clustering of samples according to grape variety, and confirmed that malolactic fermentation has a minor effect on wines phenolic profile. Variance partitioning enable to extract more information about the data since it allow to identify explanatory variables responsible for variability among samples. In this study, it was possible to identify grape variety as the main responsible factor for explaining total variability (63.6%) being malolactic fermentation responsible only for 4.0% Significance of Study: The results obtained from each of the three multivariate statistical approaches showed clearly ways of analyzing and handling large chemistry experimental data sets. When explained variables are available in the data set, the variance partitioning method could be considered as a step forward in the data analysis, providing a more solid and complete information concerning the variability on the sample system allowing a more objective result not possible by PCA and neural networks alone