Extracellular Vesicles and Asthma—More Than Just a Co-Existence

Extracellular vesicles (EVs) are membranous structures, which are secreted by almost every cell type analyzed so far. In addition to their importance for cell-cell communication under physiological conditions, EVs are also released during pathogenesis and mechanistically contribute to this process. Here we summarize their functional relevance in asthma, one of the most common chronic non-communicable diseases. Asthma is a complex persistent inflammatory disorder of the airways characterized by reversible airflow obstruction and, from a long-term perspective, airway remodeling. Overall, mechanistic studies summarized here indicate the importance of different subtypes of EVs and their variable cargoes in the functioning of the pathways underlying asthma, and show some interesting potential for the development of future therapeutic interventions. Association studies in turn demonstrate a good diagnostic potential of EVs in asthma.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

Corrosion and mechanical behavior of fusion bonded epoxy (FBE) in aqueous media

In recent years there have been many reported cases of corrosion failure in cement concrete pipelines. In the majority of cases, the failures have been attributed to rebar corrosion which is caused by the permeability of chloride from low resistivity soil and subsequent attack on a passive layer on an iron bar in the structure. As a possible alternative to cementitious materials, some organic coatings based on olefin, vinyl or epoxy-based polymers have been considered. However, due to a paucity of data on the behavior of these coatings in aqueous media— particularly product water—the possibility of their application in water transmission systems in the Kingdom has not been fully exploited. This paper deals with the studies carried out on the corrosion and mechanical behavior of fusion bonded epoxy (FBE) coating on steel in aqueous media which include product water, distilled water and saline water. The mechanical testings on coating include adhesion, bending and cathodic disbondment testings. The corrosion studies include immersion testing under static and dynamic conditions, autoclave tests and accelerated (salt-fog) tests. The analysis of results indicates chemical inertness of FBE coating in either of the aforementioned water used during testing, good adhesion and no damage to the coating during bending. Cathodic disbondment tests indicate that FBE coating sustains under cathodic protection (CP) conditions. In general, the results of mechanical and corrosion tests indicate that FBE is a promising material for internal coating on steel in water transmission systems.     

Performance tuning of glass fiber/epoxy composites through interfacial modification upon integrating with dendrimer functionalized graphene oxide

In this study, glass fiber/epoxy composites were interfacially tailored by introducing polyamidoamine (PAM) dendrimer functionalized graphene oxide (GO) into epoxy matrix. Two different composites each containing varying loading fraction (0.5, 1.0, and 1.5 wt%) of GO and GO-PAM were fabricated via hot press processing. Composites were evaluated for interlaminar shear strength (ILSS), dynamic mechanical properties and thermal conductivity. The inclusion of 1.5 wt% GO-PAM resulted ~57.3%, ~42.7%, and ~54% enhancement in ILSS, storage modulus and thermal conductivity, respectively. Almost, ~71% reduction in coefficient of thermal expansion was also observed at same GO-PAM loading. Moreover, higher glass transition temperature was observed with GO-PAM addition. GO-PAM substantially improved fiber/matrix interfacial adhesion, which was witnessed through scanning electron microscopy. The enhanced thermo-mechanical performance was attributed to interfacial covalent interactions engendered by ring opening reaction between epoxy and amine moieties of PAM dendrimers. These multiscale composites with extraordinary functional properties can outperform conventional counterparts with improved reliability and performance.     

Scan Cell Positioning for Boosting the Compression of Fan-Out Networks

Ensuring a high manufacturing test quality of an integrated electronic circuit mandates the application of a large volume test set. Even if the test data can be fit into the memory of an external tester, the consequent increase in test application time reflects into elevated production costs. Test data compression solutions have been proposed to address the test time and data volume problem by storing and delivering the test data in a compressed format, and subsequently by expanding the data on-chip. In this paper, we propose a scan cell positioning methodology that accompanies a compression technique in order to boost the compression ratio, and squash the test data even further. While we present the application of the proposed approach in conjunction with the fan-out based decompression architecture, this approach can be extended for application along with other compression solutions as well. The experimental results also confirm the compression enhancement of the proposed methodology.     

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models.     

Performance analysis of an opportunistic multi‐user cognitive network with multiple primary users

Consider a multi‐user underlay cognitive network where multiple cognitive users concurrently share the spectrum with a primary network with multiple users. The channel between the secondary network is assumed to have independent but not identical Nakagami‐m fading. The interference channel between the secondary users (SUs) and the primary users is assumed to have Rayleigh fading. A power allocation based on the instantaneous channel state information is derived when a peak interference power constraint is imposed on the secondary network in addition to the limited peak transmit power of each SU. The uplink scenario is considered where a single SU is selected for transmission. This opportunistic selection depends on the transmission channel power gain and the interference channel power gain as well as the power allocation policy adopted at the users. Exact closed form expressions for the moment‐generating function, outage performance, symbol error rate performance, and the ergodic capacity are derived. Numerical results corroborate the derived analytical results. The performance is also studied in the asymptotic regimes, and the generalized diversity gain of this scheduling scheme is derived. It is shown that when the interference channel is deeply faded and the peak transmit power constraint is relaxed, the scheduling scheme achieves full diversity and that increasing the number of primary users does not impact the diversity order. Copyright © 2014 John Wiley & Sons, Ltd.     

Study on compatibility of recycled polypropylene/high-density polyethylene blends using rheology

The compatibilization of recycled polypropylene (PP) and high-density polyethylene (HDPE) was studied using two types of compatibilizers: 5 wt% PP-grafted-maleic anhydride (5 wt% MAPP) and 5 wt% HDPE-grafted-maleic anhydride (5 wt% MAHDPE), using a cone and plate rheometer. Maleic anhydride (MAH) was grafted onto PP or HDPE via peroxide initiated melt grafting technique. Blends containing highest amount of pure HDPE exhibited maximum values for tensile strength, complex viscosity, dynamic and loss modulus compared to similar blends developed using recycled HDPE. The latter properties of all the compatibilized blends were higher compared to that of uncompatibilized blends. Studies on rheology of the pure and recycled polymer and its compatibilized blends have thrown some light on the molecular weight distribution of these materials. High shear yielding characteristics were noted for MAHDPE compatibilized blends containing high percentage of HDPE. Scanning electron microscopy showed that blends containing a high percentage of HDPE and HDPE based compatibilizer exhibit a dispersed morphology. Fourier transform infrared spectrometer characterization was conducted to check if compatibilization occurred between recycled PP and HDPE.     

Optical limiting in hydrogenated amorphous silicon-selenium thin films

Hydrogenated amorphous silicon-selenium alloy thin films grown by capacitively coupled radio-frequency glow-discharge are investigated. Nonlinear absorptive effects are evaluated with the help of open aperture z-scan technique in the 525 to 580 nm spectral range. The nonlinear absorption coefficient is found to be very large and reaching the value of 5.14 × 10^− 3 cm/W at 525 nm. The origin of the optical nonlinearities is studied and found to be due mainly to two photon absorption in the case of pulsed excitation, whereas thermal effects are thought to be dominant when the sample is excited with a continuous wave laser. Optical limiting potentialities of the thin film are experimentally observed and their thresholds are found to be very low.     

A constitutive model for the viscoplastic behavior of rubbery polymers at finite strains

Summary. A constitutive model is derived for the viscoplastic behavior of rubbery polymers at finite strains. A polymer is treated as an equivalent network of chains bridged by permanent junctions. The elastic response of the network is attributed to the elongation of strands, whereas its plastic behavior is associated with the sliding of nodes with respect to their initial positions. Unlike conventional stress–strain relations in finite viscoplasticity, the rate-of-strain tensor for the sliding of junctions is expressed in terms of the rate-of-strain tensor for macro-deformation. Constitutive equations are developed by using the laws of thermodynamics. These relations are simplified for simple shear of an incompressible medium with finite strains. The governing equations are determined by 3 material constants. To verify the model, a series of shear tests is performed on polycarbonate melts reinforced with short glass fibers. Adjustable parameters in the stress–strain relations are found by fitting the experimental data. Fair agreement is demonstrated between the observations and the results of numerical simulation. It is shown that the material constants change with the filler content in a physically plausible way.Department of Chemical Engineering, Kuwait University, Safat 13060, Kuwait