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.     

Aerogels for thermal insulation in high-performance textiles

For many garment applications where protection is needed against hostile environments, part of the requirement is for insulation to shield the wearer from extremes of temperature. For an insulating garment to be fully effective, it needs to allow the wearer to move freely so that they can carry out their intended activity efficiently. Traditional materials achieve their insulation by trapping air within the structure thereby not only limiting heat loss by convection but also making good use of the low thermal conductivity of air to cocoon the wearer within a comfortable environment. To achieve effective protection with conventional textiles, it is usually necessary to have a thick fibrous layer, or series of layers, to trap a sufficient quantity of air to provide the required level of insulation. Several disadvantages arise as a result. For example, thick layers of insulating textile materials reduce the ability of the wearer to move in a normal manner so that the conduct of detailed manual tasks can become very difficult; the layers lose their insulating capacity when the trapped air is lost as they are compressed; the insulating capacity falls rapidly as moisture collects within the fibrous insulator – it does not have to become sensibly wet for this to happen; just 15% moisture regain can give a dramatic reduction in insulating capacity. Not surprisingly therefore, there has been continued interest in developing insulators that might be able to overcome the disadvantages of conventional textile materials and improve the mobility of the wearer by allowing the use of only a very thin layer of extremely-high insulating performance to provide the required thermal protection. One class of materials from which suitable candidates might be drawn is aerogels; their attractiveness derives from the fact that they show the highest thermal insulation capacity of any materials developed so far. Despite sporadic high levels of interest, commercialisation has been slow. Aerogels have been found to possess their own set of disadvantages such as fragility; rigidity; dust formation during working and cumbersome, expensive, batch-wise manufacturing processes. They may well have been destined to become a product of minor interest, confined to very specialist applications where cost was of little concern. However, methods have been developed to combine aerogels and fibres in composite structures which maintain extremely high insulating capacity whilst demonstrating sufficient flexibility for use in garments. Ways have been found to prevent the formation of powder as aerogel composite fabrics are worked. Most significant though, is the achievement, arising from a project supported by the Korean Government, of a simplified one-step production process developed with the express aim of providing a substantial reduction in the cost of aerogels. Suitably-priced aerogel is now available and this should provide fresh stimulus for research and development teams to engage in new product development work utilising aerogels in textiles and garments for thermal insulation. The mechanisms through which aerogels achieve their outstanding thermal insulating ability is unconventional, at least in terms of materials used in textiles. This issue of Textile Progress therefore includes detail about thermal transport in aerogels before reviewing the various forms in which aerogels can now be made, some of their applications and the research priorities that are now beginning to emerge.     

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.     

Tuning the interlaminar shear strength and thermo-mechanical properties of glass fiber composites by incorporation of (3-mercaptopropyl) trimethoxysilane-functionalized carbon black

The incorporation of functionalized nanoscale fillers into traditional glass fiber/unsaturated polyester (GF/UPE) composites provides a more robust mechanical attributes. The current study demonstrates the potential of 3-mercaptopropyl trimethoxysilane (MPTS)-functionalized carbon black (f-CB) for enhancing the thermo-mechanical properties of GF composites. The composites infused with 1, 3 and 5 wt% of pristine and MPTS-functionalized CB were fabricated by hand lay-up and hot press processing. Tensile testing, interlaminar shear strength (ILSS) testing and dynamic mechanical analysis were used to evaluate the performance of nanocomposites. Fourier transform infrared spectroscopy validated the MPTS functionalization of CB. Pristine CB-loaded nanocomposites exhibited marginal improvement in ultimate tensile strength (UTS), ILSS and thermo-mechanical properties. However, with the addition of f-CB, the improvement in all the studied properties was more substantial. The inclusion of 5 wt% f-CB increased the elastic modulus and UTS by 16 and 22%, respectively, whereas the ILSS was enhanced by 36%, in comparison to the neat GF composite. The scanning electron microscope analysis of fractured ILSS samples revealed better fiber-matrix adhesion and compatibility in f-CB-loaded nanocomposites. At the same filler weight percentage, the storage modulus at 25 °C was ~ 19% higher than that of neat composite. The f-CB inclusion resulted in increment of T _g by ~ 13 °C over the T _g of neat GF/UPE composite (~ 109 °C). These improvements were due to the chemical connection of f-CB to the UPE matrix and GF surface. With such improvements in thermal and mechanical properties, these nanocomposites can replace the conventional GF composites with prominent improvements in performance.     

Preliminary Structural Data Revealed That the SARS-CoV-2 B.1.617 Variant's RBD Binds to ACE2 Receptor Stronger Than the Wild Type to Enhance the Infectivity

The evolution of new SARS-CoV-2 variants around the globe has made the COVID-19 pandemic more worrisome, further pressuring the health care system and immunity. Novel variations that are unique to the receptor-binding motif (RBM) of the receptor-binding domain (RBD) spike glycoprotein, i. e. L452R-E484Q, may play a different role in the B.1.617 (also known as G/452R.V3) variant's pathogenicity and better survival compared to the wild type. Therefore, a thorough analysis is needed to understand the impact of these mutations on binding with host receptor (RBD) and to guide new therapeutics development. In this study, we used structural and biomolecular simulation techniques to explore the impact of specific mutations (L452R-E484Q) in the B.1.617 variant on the binding of RBD to the host receptor ACE2. Our analysis revealed that the B.1.617 variant possesses different dynamic behaviours by altering dynamic-stability, residual flexibility and structural compactness. Moreover, the new variant had altered the bonding network and structural-dynamics properties significantly. MM/GBSA technique was used, which further established the binding differences between the wild type and B.1.617 variant. In conclusion, this study provides a strong impetus to develop novel drugs against the new SARS-CoV-2 variants.     

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.     

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.     

Differential Regulation of Interferon Signaling Pathways in CD4+ T Cells of the Low Type-2 Obesity-Associated Asthma Phenotype

In the era of personalized medicine, insights into the molecular mechanisms that differentially contribute to disease phenotypes, such as asthma phenotypes including obesity-associated asthma, are urgently needed. Peripheral blood was drawn from 10 obese, non-atopic asthmatic adults with a high body mass index (BMI; 36.67 ± 6.90); 10 non-obese, non-atopic asthmatic adults with normal BMI (23.88 ± 2.73); and 10 healthy controls with normal BMI (23.62 ± 3.74). All asthmatic patients were considered to represent a low type-2 asthma phenotype according to selective clinical parameters. RNA sequencing (RNA-Seq) was conducted on peripheral blood CD4⁺ T cells. Thousands of differentially expressed genes were identified in both asthma groups compared with heathy controls. The expression of interferon (IFN)-stimulated genes associated with IFN-related signaling pathways was specifically affected in obese asthmatics, while the gap junction and G protein-coupled receptor (GPCR) ligand binding pathways were enriched in both asthma groups. Furthermore, obesity gene markers were also upregulated in CD4⁺ T cells from obese asthmatics compared with the two other groups. Additionally, the enriched genes of the three abovementioned pathways showed a unique correlation pattern with various laboratory and clinical parameters. The specific activation of IFN-related signaling and viral infection pathways might provide a novel view of the molecular mechanisms associated with the development of the low type-2 obesity-associated asthma phenotype, which is a step ahead in the development of new stratified therapeutic approaches.     

Optimizing Approach of Water Allocation to Off-Takes During Reduced Flows

Multi-objective optimization models with an index were developed based on farmers’ preferences, local requirements, supplies available at the head of the canal, system losses, crop demand about different growth stages, and field soil moisture balance. The models were applied using linear programming. The Model 1 determines the cropping pattern by maximizing net economic benefits using a monthly basis lumped volume available at the head of the canal and is set to the minimum and maximum area constraints along with the constraint of minimum main crop area. The areas for different crops given by the first model form input for the Model 2. The other inputs of Model 2 included periodic supply available at the head of the primary canal (7-day period in this study), root growth depth, demand, and soil moisture constants. The Model 2 optimizes the sum of relative yields of all the crops and provide the irrigation levels of various crops for specified periods. Finally, the distributed area and irrigation levels determined by Model 2 are used in conjunction with the losses to decide flow rates of off takes. The complete program was implemented in the West branch irrigated area of Mirpurkhas subdivision. The results showed that the resources were allocated to off-takes in a competitive and conflict-free manner.

     

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%.