Unsteady thermocapillary migration of bubbles

Upon the introduction of a gas bubble into a liquid possessing a uniform thermal gradient, an unsteady thermo-capillary flow begins. Ultimately, the bubble attains a constant velocity. This theoretical analysis focuses upon the transient period for a bubble in a microgravity environment and is restricted to situations wherein the flow is sufficiently slow such that inertial terms in the Navier-Stokes equation and convective terms in the energy equation may be safely neglected (i.e., both Reynolds and Marangoni numbers are small). The resulting linear equations were solved analytically in the Laplace domain with the Prandtl number of the liquid as a parameter; inversion was accomplished numerically using a standard IMSL routine. In the asymptotic long-time limit, the theory agrees with the steady-state theory of Young, Goldstein, and Block. The theory predicts that more than 90 percent of the terminal steady velocity is achieved when the smallest dimensionless time, i.e., the one based upon the largest time scale-viscous or thermal-equals unity

A Multiband OFDMA Heterogeneous Network for Millimeter Wave 5G Wireless Applications

Citation: Niknam, S., Nasir, A. A., Mehrpouyan, H., & Natarajan, B. (2016). A Multiband OFDMA Heterogeneous Network for Millimeter Wave 5G Wireless Applications. Ieee Access, 4, 5640-5648. doi:10.1109/access.2016.2604364Emerging fifth generation (5G) wireless networks require massive bandwidth in higher frequency bands, extreme network densities, and flexibility of supporting multiple wireless technologies in order to provide higher data rates and seamless coverage. It is expected that the utilization of the large bandwidth in the millimeter-wave (mmWave) band and deployment of heterogeneous networks (HetNets) will help address the data rate requirements of 5G networks. However, high pathloss and shadowing in the mmWave frequency band, strong interference in the HetNets due to massive network densification, and coordination of various air interfaces are challenges that must be addressed. In this paper, we consider a relay based multiband orthogonal frequency division multiple access HetNet in which mmWave small cells are deployed within the service area of macro cells. In particular, we attempt to exploit the distinct propagation characteristics of mmWave bands (i.e., 60 GHz-the V-band and 70-80 GHz the E-band) and the long term evolution band to maximize overall data rate of the network via efficient resource allocation. The problem is solved using a modified dual decomposition approach and then a low complexity greedy solution based on the iterative activity selection algorithm is presented. Simulation results show that the proposed approach outperforms conventional schemes

Editorial on “In-and-off body-centric nano-scale wireless communication and networks”

No abstract available

Development of biodegradable plastic composite blends based on sago derived starch and natural rubber

Polyethylene is a widely used packaging material, but its non-biodegradable nature can lead to waste disposal problems. This increases the concern in research and development of biodegradable plastics from natural resource as alternatives to petroleum-derived plastics. In this study, biodegradable plastic composites were prepared by blending thermoplastic starch with natural rubber in the present of glycerol as plasticizer. Local sago starch was cast with 0.5 to 10% of natural rubber to prepare the bioplastic. The products were characterized by differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), water absorption test, biodegradable test, hydrolysis test, and mechanical analysis. Meanwhile, composite with natural rubber latex was increased from 0.5 to 10% showing that the melting temperature is in the range of 120 to 150 °C, but with no significant difference. The water absorption characteristics, biodegradability, and tensile strength decreased by 11.21%, 30.18%, and 20.733 MPa, respectively. However, the elongation at break was increased from 26.67 to 503.3%. The findings of this study showed that sago starch has a great potential in bioplastic production with good miscibility and compatibility

An Automated Algorithm to Distinguish and Characterize Solar Flares and Associated Sequential Chromospheric Brightenings

We present a new automated algorithm to identify, track, and characterize small-scale brightening associated with solar eruptive phenomena observed in H{\alpha}. The temporal spatially-localized changes in chromospheric intensities can be separated into two categories: flare ribbons and sequential chromospheric brightenings (SCBs). Within each category of brightening we determine the smallest resolvable locus of pixels, a kernel, and track the temporal evolution of the position and intensity of each kernel. This tracking is accomplished by isolating the eruptive features, identifying kernels, and linking detections between frames into trajectories of kernels. We fully characterize the evolving intensity and morphology of the flare ribbons by observing the tracked flare kernels in aggregate. With the location of SCB and flare kernels identified, they can easily be overlaid on top of complementary data sets to extract Doppler velocities and magnetic field intensities underlying the kernels. This algorithm is adaptable to any dataset to identify and track solar features.Comment: 22 pages, 9 figure

Dynamical cluster-decay model for hot and rotating light-mass nuclear systems, applied to low-energy 32^{32}S + 24^{24}Mg →56\to ^{56}Ni reaction

The dynamical cluster-decay model (DCM) is developed further for the decay of hot and rotating compound nuclei (CN) formed in light heavy-ion reactions. The model is worked out in terms of only one parameter, namely the neck-length parameter, which is related to the total kinetic energy TKE(T) or effective Q-value $Q_{eff}(T)$ at temperature T of the hot CN, defined in terms of the both the light-particles (LP), with $A \leq$ 4, Z $\leq$ 2, as well as the complex intermediate mass fragments (IMF), with $4 2$, is considered as the dynamical collective mass motion of preformed clusters through the barrier. Within the same dynamical model treatment, the LPs are shown to have different characteristics as compared to the IMFs. The systematic variation of the LP emission cross section $\sigma_{LP}$, and IMF emission cross section $\sigma_{IMF}$, calculated on the present DCM match exactly the statistical fission model predictions. It is for the first time that a non-statistical dynamical description is developed for the emission of light-particles from the hot and rotating CN. The model is applied to the decay of $^{56}$Ni formed in the $^{32}$S + $^{24}$Mg reaction at two incident energies E$_{c.m.}$ = 51.6 and 60.5 MeV. Both the IMFs and average $\bar{TKE}$ spectra are found to compare reasonably nicely with the experimental data, favoring asymmetric mass distributions. The LPs emission cross section is shown to depend strongly on the type of emitted particles and their multiplicities

Analysis of Solar-Heated Thermal Wadis to Support Extended-Duration Lunar Exploration

The realization of the renewed exploration of the Moon presents many technical challenges; among them is the survival of lunar surface assets during periods of darkness when the lunar environment is very cold. Thermal wadis are engineered sources of stored solar energy using modified lunar regolith as a thermal storage mass that can enable the operation of lightweight robotic rovers or other assets in cold, dark environments without incurring potential mass, cost, and risk penalties associated with various onboard sources of thermal energy. Thermal wadi-assisted lunar rovers can conduct a variety of long-duration missions including exploration site surveys; teleoperated, crew-directed, or autonomous scientific expeditions; and logistics support for crewed exploration. This paper describes a thermal analysis of thermal wadi performance based on the known solar illumination of the moon and estimates of producible thermal properties of modified lunar regolith. Analysis was performed for the lunar equatorial region and for a potential Outpost location near the lunar south pole. The results are presented in some detail in the paper and indicate that thermal wadis can provide the desired thermal energy reserve, with significant margin, for the survival of rovers or other equipment during periods of darkness

An Extension of Analysis of Solar-Heated Thermal Wadis to Support Extended-Duration Lunar Exploration

The realization of the renewed exploration of the Moon presents many technical challenges; among them is the survival of lunar surface assets during periods of darkness when the lunar environment is very cold. Thermal wadis are engineered sources of stored solar energy using modified lunar regolith as a thermal storage mass that can supply energy to protect lightweight robotic rovers or other assets during the lunar night. This paper describes an extension of an earlier analysis of performance of thermal wadis based on the known solar illumination of the Moon and estimates of producible thermal properties of modified lunar regolith. The current analysis has been performed for the lunar equatorial region and validates the formerly used 1-D model by comparison of predictions to those obtained from 2-D and 3-D computations. It includes the effects of a thin dust layer covering the surface of the wadi, and incorporating either water as a phase-change material or aluminum stakes as a high thermal conductivity material into the regolith. The calculations indicate that thermal wadis can provide the desired thermal energy and temperature control for the survival of rovers or other equipment during periods of darkness

Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio‑Cyber attacks

This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain control over the resources used in that pipeline during sequence analysis. The scenario considered in the paper is based on perpetrators submitting synthetically engineered DNA samples that contain digitally encoded IP address and port number of the perpetrator’s machine in the DNA. Genetic analysis of the sample’s DNA will decode the address that is used by the software Trojan malware to activate and trigger a remote connection. This approach can open up to multiple perpetrators to create connections to hijack the DNA sequencing pipeline. As a way of hiding the data, the perpetrators can avoid detection by encoding the address to maximise similarity with genuine DNAs, which we showed previously. However, in this paper we show how Deep Learning can be used to successfully detect and identify the trigger encoded data, in order to protect a DNA sequencing pipeline from Trojan attacks. The result shows nearly up to 100% accuracy in detection in such a novel Trojan attack scenario even after applying fragmentation encryption and steganography on the encoded trigger data. In addition, feasibility of designing and synthesizing encoded DNA for such Trojan payloads is validated by a wet lab experiment