Predicting temporal centrality in Opportunistic Mobile Social Networks based on social behavior of people

Predicting the centrality of nodes is a significant problem for different applications in Opportunistic Mobile Social Networks (OMSNs). However, when calculating such metrics, current studies focused on analyzing static networks that do not change over time or using aggregated contact information over a period of time. Furthermore, the centrality measured in the past is not verified whether it is useful as a predictor for the future. In this paper, in order to capture the dynamic behavior of people, we focus on predicting nodes’ future centrality (importance) from the temporal perspective using real mobility traces in OMSNs. Three important centrality metrics, namely betweenness, closeness, and degree centrality, are considered. Through real trace-driven simulations, we find that nodes’ future centrality is highly predictable due to natural social behavior of people. Then, based on the observations in the simulation, we design several reasonable prediction methods to predict nodes’ future temporal centrality. Finally, extensive real trace-driven simulations are conducted to evaluate the performance of our proposed methods. The results show that the Recent Weighted Average Method performs best in the MIT Reality trace, and the recent Uniform Average Method performs best in the Infocom 06 trace. Furthermore, we also evaluate the impact of parameters m and w on the performance of the proposed methods and find proper values of different parameters for each proposed method at the same time.     

SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors

Getting enough quality sleep is a key part of a healthy lifestyle. Many people are tracking their sleep through mobile and wearable technology, together with contextual information that may influence sleep quality, like exercise, diet, and stress. However, there is limited support to help people make sense of this wealth of data, i.e., to explore the relationship between sleep data and contextual data. We strive to bridge this gap between sleep-tracking and sense-making through the design of SleepExplorer, a web-based tool that helps individuals understand sleep quality through multi-dimensional sleep structure and explore correlations between sleep data and contextual information. Based on a two-week field study with 12 participants, this paper offers a rich understanding on how technology can support sense-making on personal sleep data: SleepExplorer organizes a flux of sleep data into sleep structure, guides sleep-tracking activities, highlights connections between sleep and contributing factors, and supports individuals in taking actions. We discuss challenges and opportunities to inform the work of researchers and designers creating data-driven health and well-being applications.     

Direction prediction for avoiding occlusion in visual surveillance

Occurrence of occlusion while providing visual surveillance leads to anarchy as the track of the subject under motion may be lost. This often results into the failure of the surveillance system. The approach of predicting motion of moving subjects and hence the chances of their mutual occlusion gives an upper hand to surveillance system to take in-time necessary action towards mitigation of loss of track during dynamic occlusion. Direction of motion of a moving subject plays a major role while studying its motion. Direction along with the velocity of a subject in a 3D plane completely describes the motion of any subject. This article proposes a model‘-based approach for direction prediction of a moving subject in a 3D global plane as acquired in a 2D camera plane. The proposed approach uses the eight discrete directions of motion as proposed in and models different directions. The proposed direction prediction method is experimentally verified with six different classifiers, i.e. regression analysis, simple logistic regression, MLP, k-NN, SVM and Bays classifier over existing as well as self-acquired databases. The initial simulation results are motivating as the overall accuracies achieved through different classifiers are of the range of 87–94 \(\%\), which advocates the suitability of the said approach.     

Viscoelastic flow in an obstructed microchannel at high Weissenberg number

Detailed measurements of the flow instability of dilute shear-thinning viscoelastic aqueous solutions, with relatively low zero-shear viscosities, in an obstructed microchannel flow are reported. We examine the flow behaviour resulting from a 100 μm post placed in the channel centreline over a range of Reynolds numbers (\(5<Re<300\)) and Weissenberg numbers (\(20<Wi<10^3\)). Micro-particle image velocimetry measurements show the onset of an upstream instability within a Reynolds number range and at a critical elasticity number corresponding to polymer concentrations above 25 ppm of long-chain polyacrylamide. The instability results in significant local fluctuations in the flow field approaching 30 % of the mean velocity. The magnitude of the local viscosity ratio in the region upstream of the post is proposed as a driving mechanism for the instability which resembles a buckling flow. Additionally, the classical instability owing to separation and vortex formation downstream of the post in Newtonian flow is suppressed and a very long stable wake is observed extending over 10 post-diameters downstream.     

Fault diagnosis and fault tolerant control for non-Gaussian time-delayed singular stochastic distribution systems

Stochastic distribution control (SDC) is a new branch of stochastic system control that the system output is the probability density function (PDF) of the output. In practice, some algebraic relations exist between the input and the weights of SDC systems, leading to a singular state space model between the weights and the control input which increases the complexity of the system. The ignorance of time delay in practical systems will make the effectiveness of the fault diagnosis (FD) and fault tolerant control (FTC) be reduced. In this paper, the linear B-spline basis functions are used to approximate the output PDF. A FD approach based on the adaptive observer is established to diagnose the size of fault in the singular time-delayed SDC system. With the fault diagnosis information, a fault tolerant controller based on PI tracking control scheme is constructed to make the post-fault PDF still track the given distribution. The post-fault closed-loop stability analysis with the practical fault tolerant controller is carried out based on the Lyapunov stability theorem. Finally, a numerical simulation is provided to demonstrate the effectiveness of the proposed approach.     

Novel prototyping method for microfluidic devices based on thermoplastic polyurethane microcapillary film

Thermoplastic polyurethane microcapillary film (TPU-MCF), as a novel extruded product, inherently contains an array of circular micron-sized capillaries embedded inside the polymer matrix. With the aid of simple laser cutting and conventional sealing technologies, a rapid prototyping method for microfluidic devices is proposed based on the ready-made microstructure of MCFs. Two functionalized microfluidic devices: serpentine micromixer and multi-droplet generator, are rapidly fabricated to demonstrate the advantages and potential of employing this new method. The whole proof-of-concept fabrication process can be completed in 8–10 min in a simple way; each procedure is repeatable with stable performance control of microfluidic devices; and the material cost can be as low as $0.01 for each device. The TPU-MCF and this novel method are expected to provide a new perspective and alternative in microfluidic community with particular requirements.     

Scale effects in nano-channel liquid flows

Force-driven liquid argon flows both in nanoscale periodic domains and in gold nano-channels are simulated using non-equilibrium molecular dynamics to investigate the scale and wall force field effects. We examined variations in liquid density, viscosity, velocity profile, slip length, shear stress and mass flow rate in different sized periodic domains and nano-channels at a fixed thermodynamic state. In the absence of walls, liquid argon obeys Newton’s law of viscosity with the desired absolute viscosity in domains as small as 4 molecular diameters in height. Results prove that deviations from continuum solution are solely due to wall effects. Simulations in nano-channels with heights varying from 3.26 to 36 nm exhibit parabolic velocity profiles with constant slip length modeled by Navier-type slip boundary condition. Both channel averaged density and “apparent viscosity” decrease with reduced channel height, which has competing effects in determination of the mass flow rate. Density layering and wall force field induce deviations from Newton’s law of viscosity in the near-wall region, while constant “apparent viscosity” with the deformation rate from a parabolic velocity profile successfully predicts shear stress in the bulk flow region.     

Easy-to-attach vacuum modules with biochips for droplets generation from small sample volumes

This study developed a droplet biochip driven with a single vacuum module to produce droplets from small sample volumes. The vacuum module is composed of a shape memory polymer, which releases prestored energy for shape recovery when subjected to heat trigger, and works as an easy-to-attach vacuum source. The three-layer Teflon mold is designed to manufacture a vacuum module with a favorable yield (>95%). The water-in-oil emulsion droplets can be produced by attaching a single vacuum module with a microfluidic chip. The diameter of the vacuum module has been successfully reduced to 6 mm. The maximum driving pressure provided by the 15-mm diameter vacuum module attached with a 2 μL chip is approximately 9653 Pa. The produced flow rate varies with the deformation rate of the vacuum module and becomes stable at 2.4 µL/min during the droplet generation. The droplet diameters range from 180 to 240 µm. The developed disposable vacuum module is easy to attach, easy to use, easy to make, cost-effective, and automatically controllable for driving fluids on a chip for handling small sample volumes.     

ExoPranayama: a biofeedback-driven actuated environment for supporting yoga breathing practices

Both breathing and internal self-awareness are an integral part of any yoga practice. We describe and discuss the development of ExoPranayama, an actuated environment that physically manifests users’ breathing in yoga. Through a series of trials with yoga practitioners and expert teachers, we explore its role in the practice of yoga. Our interview results reveal that biofeedback through the environment supported teaching and improved self-awareness, but it impacted group cohesion. Two practical uses of the technology emerged for supporting breath control in yoga: (1) biofeedback can provide new information about users’ current internal states; (2) machine-driven feedback provides users with a future state or goal and leads to improved cohesiveness.