Volume filtered FEM-DEM framework for simulating particle-laden flows in complex geometries

We present a computational framework for modeling large-scale particle-laden flows in complex domains with the goal of enabling simulations in medical-image derived patient specific geometries. The framework is based on a volume-filtered Eulerian-Lagrangian method that uses a finite element method (FEM) to solve for the fluid phase coupled with a discrete element method (DEM) for the particle phase, with varying levels of coupling between the phases. The fluid phase is solved on a three-dimensional unstructured grid using a stabilized FEM. The particle phase is modeled as rigid spheres and their motion is calculated according to Newton's second law for translation and rotation. We propose an efficient and conservative particle-fluid coupling scheme compatible with the FEM basis that enables convergence under grid refinement of the two-way coupling terms. Efficient algorithms for neighbor detection for particle-particle collision and particle-wall collisions are adopted. The method is applied to a few different test cases and the results are analyzed qualitatively. The results demonstrate the capabilities of the implementation and the potential of the method for simulating large-scale particle-laden flows in complex geometries.Comment: 15 pages, 11 figure

Correlation and prediction of dynamic human isolated joint strength from lean body mass

A relationship between a person's lean body mass and the amount of maximum torque that can be produced with each isolated joint of the upper extremity was investigated. The maximum dynamic isolated joint torque (upper extremity) on 14 subjects was collected using a dynamometer multi-joint testing unit. These data were reduced to a table of coefficients of second degree polynomials, computed using a least squares regression method. All the coefficients were then organized into look-up tables, a compact and convenient storage/retrieval mechanism for the data set. Data from each joint, direction and velocity, were normalized with respect to that joint's average and merged into files (one for each curve for a particular joint). Regression was performed on each one of these files to derive a table of normalized population curve coefficients for each joint axis, direction, and velocity. In addition, a regression table which included all upper extremity joints was built which related average torque to lean body mass for an individual. These two tables are the basis of the regression model which allows the prediction of dynamic isolated joint torques from an individual's lean body mass

Artificial Intelligence approaches in Cyber Security

As we all know that, the data that is been generated every second is increasing exponentially, as this information stored or received in any form is directly or indirectly is through Internet that means the data has to be travelled over a network for its completion of task, due to this the security for proper transmission of data plays a vital role in Cyber Security. The speed of processes and the amount of data to be used in defending the cyber space is cannot be handled by humans without considerable automations. However, it is difficult to develop software with conventional fixed algorithms for effectively defending against the dynamically evolving malicious attacks over the network. This situation can be handled by applying method of Artificial Intelligence that provides flexibility and learning capabilities of a network which later helps us in defending the attacks and as well as tracing down the culprits residing behind the terminology. This topic mainly emphasis on how well a packet is transferred from source to destination with proper security so that the end-user acquires the correct data as per his requirements

App Parameter Energy Profiling: Optimizing App Energy Drain by Finding Tunable App Parameters

In this paper, we observe that modern mobile apps come with a large number of parameters that control the app behavior which indirectly affect the app energy drain, and using incorrect or non-optimal values for such app parameters can lead to app energy drain deficiency or even energy bugs. We argue conventional app energy optimization using an energy profiler which pinpoints energy hotspot code segments in the app source code may be ineffective in detecting such parameter-induced app energy deficiency. We propose app parameter energy profiling which identifies tunable app parameters that can reduce app energy drain without affecting app functions as a potentially more effective solution for debugging such app energy deficiency. We present the design and implementation of Medusa, an app parameter energy profiling framework. Medusa overcomes three key design challenges: how to filter out and narrow down candidate parameters, how to pick alternative parameter values, and how to perform reliable energy drain testing of app versions with mutated parameter values. We demonstrate the effectiveness of Medusa by applying it to a set of Android apps which successfully identifies tunable energy-reducing parameters

An Empirical Study on the Impact of Deep Parameters on Mobile App Energy Usage

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these deep parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. In this paper we empirically investigate this topic, combining a developer survey with systematic energy measurements. Our motivational survey of 25 Android developers suggests that developers do not understand, and largely ignore, the energy impact of deep parameters. To assess the potential implications of this practice, we propose a deep parameter energy profiling framework that can analyze the energy impact of deep parameters in an app. Our framework identifies deep parameters, mutates them based on our parameter value selection scheme, and performs reliable energy impact analysis. Applying the framework to 16 popular Android apps, we discovered that deep parameter-induced energy inefficiency is rare. We found only 2 out of 1644 deep parameters for which a different value would significantly improve its app\u27s energy efficiency. A detailed analysis found that most deep parameters have either no energy impact, limited energy impact, or an energy impact only under extreme values. Our study suggests that it is generally safe for developers to ignore the energy impact when choosing deep parameter values in mobile apps

Scripting human animations in a virtual environment

The current deficiencies of virtual environment (VE) are well known: annoying lag time in drawing the current view, drastically simplified environments to reduce that time lag, low resolution and narrow field of view. Animation scripting is an application of VE technology which can be carried out successfully despite these deficiencies. The final product is a smoothly moving high resolution animation displaying detailed models. In this system, the user is represented by a human computer model with the same body proportions. Using magnetic tracking, the motions of the model's upper torso, head and arms are controlled by the user's movements (18 degrees of freedom). The model's lower torso and global position and orientation are controlled by a spaceball and keypad (12 degrees of freedom). Using this system human motion scripts can be extracted from the user's movements while immersed in a simplified virtual environment. Recorded data is used to define key frames; motion is interpolated between them and post processing adds a more detailed environment. The result is a considerable savings in time and a much more natural-looking movement of a human figure in a smooth and seamless animation

Distribution of luminescent Vibrio harveyi and their bacteriophages in a commercial shrimp hatchery in South India

Luminescent Vibrio harveyi is a natural microflora of marine and coastal water bodies and is associated with mortality of larval shrimp in penaeid shrimp hatcheries. It is also known that the bacteriophages occur virtually in all places where their hosts exist. In this study, distribution of luminescent V. harveyi and the bacteriophages affecting these hosts was examined in a commercial Penaeus monodon hatchery during three shrimp larval production cycles, including a cycle affected by luminescent bacterial (LB) disease outbreak

Copper biodissolution from a low grade chalcopyrite ore by unadapted/adapted acidithiobacillus ferrooxidans

The depletion of high-grade deposit of copper around the world has drawn attention for the utilisation of low-grade reserves . Malanjkhand Copper Project (MCP) in India is a low-grade ore containing -0.3% Cu in which copper metal is found to be present as chalcopyrite associated with pyrite in quartz veins and granitic rocks. In order to extract copper from this material , an alternate processing option such as bioleaching has been followed. Bench scale bioleaching experiments were carried out using Acidithiobacillus ferrooxidans (Ac. Ti) isolated from mine water. On using unadapted Ac. Tf isolate directly at pH 2.0 and 35°C, the optimum leaching conditions in shake flask were found to be 5% pulp density (PD), 2.OpH , 35°C temperature for <50p .m particles , yielding 72% Cu biorecovery in 35days. The Tf isolate when adapted to the ore and employed for the bioleaching of the ore at 5% PD (w/v), 2.OpH and 25 °C with three particle sizes viz.150 -76μm, 76-5011m and <50μm, resulted in recovery of 38 .31%, 29.68% and 47.5% Cu respectively with a rise in Eh from 530 to 654 mV in 35 days. Under similar conditions , the unadapted strain gave maximum recovery of 44.0 % for <50pm ore size with rise in Eh from 525 to 650mV . Copper biorecovery increased to 75.3% with the adapted isolates at 35°C for the finer particles of <50gm at 2.OpH with a rise in cell count from lx l 07 cells/mL to 1.13x109 cells/mL in 35 days. The biodissolution of copper from chalcopyrite with the involvement of adapted Ac. Tf species resulted in the improvement of iron oxidation rate (Fe2+ to Fe'`) and consequently higher redox potential

Sustainability of crop production from polluted lands

Sustainable food production for a rapidly growing global population is a major challenge of this century. In order to meet the demand for food production, an additional land area of 2.7 to 4.9 Mha year -1 will be required for agriculture. However, one third of arable lands are already contaminated, therefore the use of polluted lands will have to feature highly in modern agriculture. The use of such lands comes however with additional challenges and suitable agrotechnological interventions are essential for ensuring the safety and sustainability of relevant production system. There are also other issues to consider such as, cost benefit analysis, the possible entry of pollutants into to the phytoproducts, certification and marketing of such products, in order to achieve a the large scale exploitation of polluted land

Comparison of Extravehicular Mobility Unit (EMU) suited and unsuited isolated joint strength measurements

In this study the strength of subjects suited in extravehicular mobility units (EMU's) - or Space Shuttle suits - was compared to the strength of unsuited subjects. The authors devised a systematic and complete data set that characterizes isolated joint torques for all major joints of EMU-suited subjects. Six joint motions were included in the data set. The joint conditions of six subjects were compared to increase our understanding of the strength capabilities of suited subjects. Data were gathered on suited and unsuited subjects. Suited subjects wore Class 3 or Class 1 suits, with and without thermal micrometeoroid garments (TMG's). Suited and unsuited conditions for each joint motion were compared. From this the authors found, for example, that shoulder abduction suited conditions differ from each other and from the unsuited condition. A second-order polynomial regression model was also provided. This model, which allows the prediction of suited strength when given unsuited strength information, relates the torques of unsuited conditions to the torques of all suited conditions. Data obtained will enable computer modeling of EMU strength, conversion from unsuited to suited data, and isolated joint strength comparisons between suited and unsuited conditions at any measured angle. From these data mission planners and human factors engineers may gain a better understanding of crew posture, and mobility and strength capabilities. This study also may help suit designers optimize suit strength, and provide a foundation for EMU strength modeling systems