RCare: Extending Secure Health Care to Rural Area Using VANETs

In this paper, we propose a delay-tolerant secure long-term health care scheme, RCare, for collecting patient’s sensitive personal health information (PHI). Specifically, to minimize the overall health care cost, RCare provides network connectivity to rural areas using conventional transportation vehicles (e.g., cars, buses) as relay nodes. These vehicles are expected to store, carry, and forward the PHI to the health-service-provider located mostly at the city area following an opportunistic routing. RCare improves network performance by providing incentive to the cooperative vehicles, and encompasses identity based cryptography to ensure security and privacy of the PHI during the routing period by using short digital signature and pseudo-identity. Network fairness and resistance to different possible attacks are also ensured by RCare. Extensive security and performance analyses demonstrate that RCare is able to achieve desired security requirements with effectiveness in terms of high delivery ratio with acceptable communication delay.     

Implementation and performance evaluation of a distributed conjugate gradient method in a cloud computing environment

Cloud computing is an emerging technology where information technology resources are provisioned to users in a set of a unified computing resources on a pay per use basis. The resources are dynamically chosen to satisfy a user service level agreement and a required level of performance. A cloud is seen as a computing platform for heavy load applications. Conjugate gradient (CG) method is an iterative linear solver that is used by many scientific and engineering applications to solve a linear system of algebraic equations. CG generates a heavy load of computation, and therefore, it slows the performance of the applications using it. Distributing CG is considered as a way to increase its performance. However, running a distributed CG, based on a standard API, such as Message Passing Interface, in a cloud face many challenges, such as the cloud processing and networking capabilities. In this work, we present an in‐depth analysis of the CG algorithm and its complexity to develop adequate distributed algorithms. The implementation of these algorithms and their evaluation in our cloud environment reveal the gains and losses achieved by distributing the CG. The performance results show that despite the complexity of the CG processing and communication, a speedup gain of at least 1157.7 is obtained using 128 cores compared with National Aeronautics and Space Administration Advanced Supercomputing sequential execution. Given the emergence of clouds, the results in this paper analyzes performance issues when a generic public cloud, along with a standard development library, such as Message Passing Interface, is used for high‐performance applications, without the need of some specialized hardware and software. Copyright © 2012 John Wiley & Sons, Ltd.     

Influence of humidity,temperature, and annealing on microstructure and tensile properties of electrospun polyacrylonitrile nanofibers

This study investigates the microstructure and mechanical properties of electrospun nanofibers from polyacrylonitrile (PAN)‐dimethylformamide (DMF) solution at different relative humidity (RH) in the range from 14% to 60% and two different temperatures (20°C and 40°C). Nanofibers produced at low RH (22% or less at 20°C) exhibit relatively smooth surface and solid core, whereas at higher RH (30% or higher at 20°C) rough surface and porous core are observed. The resulting morphology is explained by means of H₂O/DMF/PAN ternary phase diagram. At higher RH, the water diffusion into polymer‐solution jet brings thermodynamic instability into the system leading to separation of polymer‐rich phase and polymer‐lean phase, where the later contributes to porosity. Higher process temperature (40°C) yields larger miscibility area in the ternary phase diagram leading to formation of porous structure at relatively higher RH (40%). Tensile strength of nanofibrous yarns is found to vary from 80 MPa to 130 MPa depending on the processing temperature and RH. The amount of porosity is found to affect the tensile properties of nanofibers most significantly, although diameter and crystallinity play important role. Annealing is found to alleviate surface roughness and porosity and increase crystallinity. Tensile strength of nanofibrous yarns is found to improve by up to 25% after annealing. POLYM. ENG. SCI., 58:998–1009, 2018. © 2017 Society of Plastics Engineers     

Monocrystalline Labeling Enables Stable Plasmonic Enhancement for Isolation-Free Extracellular Vesicle Analysis

Sensitive detection of extracellular vesicles (EVs) as emerging biomarkers has shown great promises for disease diagnosis. Plasmonic metal nanostructures conjugated with molecules that bind specific biomarker targets are widely used for EVs sensing but involve tradeoffs between particle-size-dependent signal intensity and conjugation efficiency. One solution to this problem would be to induce nucleation on nanoparticles that have successfully bound a target biomarker to permit in situ nanoparticle growth for signal amplification, but approaches that are evaluated to date require harsh conditions or lack nucleation specificity, prohibiting their effective use with most biological specimens. This study describes a one-step in situ strategy to induce monocrystalline copper shell growth on gold nanorod probes without decreasing signal by disrupting probe-target interactions or lipid bilayer integrity to enable EV biomarker detections. This approach increases the detected nanoparticle signal about two orders of magnitude after a 10 min copper nanoshell growth reaction. This has significant implications for improved disease detection, as indicated by the ability of a novel immunoassay using this approach to detect low abundance EVs carrying a pathogen-derived biomarker, after their direct capture from serum, to facilitate the diagnosis of tuberculosis cases in a diagnostically challenging pediatric cohort.     

Molecular Dynamics Simulations of a Catalytic Multivalent Peptide–Nanoparticle Complex

Molecular modeling of a supramolecular catalytic system is conducted resulting from the assembling between a small peptide and the surface of cationic self-assembled monolayers on gold nanoparticles, through a multiscale iterative approach including atomistic force field development, flexible docking with Brownian Dynamics and µs-long Molecular Dynamics simulations. Self-assembly is a prerequisite for the catalysis, since the catalytic peptides do not display any activity in the absence of the gold nanocluster. Atomistic simulations reveal details of the association dynamics as regulated by defined conformational changes of the peptide due to peptide length and sequence. Our results show the importance of a rational design of the peptide to enhance the catalytic activity of peptide–nanoparticle conjugates and present a viable computational approach toward the design of enzyme mimics having a complex structure–function relationship, for technological and nanomedical applications.     

Synthesis of nanolayered ternary borides powders (MAB phases) by sustainable molten salt shielded synthesis/sintering (MS3) process

Journal of Materials Science - In this paper, we report the synthesis of Fe2AlB2, Mn2AlB2, and MoAlB powders by using molten salt shielded synthesis/sintering (MS3)—a sustainable processing...     

Three-Dimensional Fractional Order Generalized Thermoelastic Problem Under the Effect of Rotation in a Half Space

The current article is concerned with three-dimensional problems for a homogeneous, isotropic thermoelastic half-space subjected to a time-dependent heat source on the surface, which is traction-free under the effect of rotation. The governing equations are taken in the context of fractional order generalized thermoelasticity (Green–Lindsay) theory. Normal mode analysis technique and eigenvalue approaches have been used to solve the non-dimensional equations. Numerical results for temperature, thermal stress and displacement distributions are presented graphically and discussed.     

Artificial neural-network model-based observers

Describes a pseudorandom testing scheme for fault diagnosis of analog integrated circuits. The goal is to implement a BIST technique with both a built-in pattern generator and a response analyzer for fault diagnosis. We have chosen a diagnostic framework for the analog ICs using a pseudorandom noise generator as the test-pattern generator and a model-based observer to detect and diagnose faults. The observer is implemented through a multilayer feedforward ANN trained with a back-error propagation (BEP) algorithm. Both the test-pattern generator and the model-based observer proposed in this article can be implemented either on- or offline depending on the need of the application and silicon area overhead.