CR-CEA: A collision- and energy-aware routing method for cognitive radio wireless sensor networks

Currently, working in the overcrowded shared unlicensed spectrum band, leads to a reduction in the quality of communications in wireless networks. This makes a considerable increase in packet loss caused by collisions that necessitates packets retransmissions. In the case of wireless sensor networks (WSN), a large amount of energy of sensor nodes will be wasted by these retransmissions. Cognitive radio technology makes it possible for sensor nodes, to opportunistically use licensed bands with better propagation characteristics and less congestion. In this paper a routing method for cognitive radio wireless sensor networks (CR-CEA) is presented, that is based on a cross-layer design that jointly considers route and spectrum selection. The CR-CEA method has two main phases: next hop selection and channel selection. The routing is performed hop-by-hop with local information and decisions, which are more compatible with sensor networks. Primary user activity and prevention from interference with them, is considered in all spectrum decisions. It uniformly distributes frequency channels between adjacent nodes, which lead to a local reduction in collision probability. This clearly affects energy consumption in all sensor nodes. In CR-CEA, route selection is energy-aware and a learning-based technique is used to reduce the packet delay in terms of hop-count. The simulation results reveal that by applying cognitive radio technology to WSNs and selecting a proper operating channel, we can consciously decrease collision probability. This saves energy of sensor nodes and improves the network lifetime.     

Thermoelectric composite structure with desirable mechanical properties for high-performance multi-functional applications

Thermoelectric (TE) structures based on energy harvesting technology have played a vital role in wide-reaching applications. In this study, a composite structure consisting of a glass fabric covered with a nanocomposite membrane (polyacrylonitrile [PAN]/carbon nanotube [CNT]/copper oxide nanoparticle [CuO]) was prepared to provide thermoelectric conversion. The performance of the TE composite structure was evaluated by analyzing the mechanical properties, thermoelectric properties, and the ability of the structure to power small electronic equipment. The results showed that the nanocomposite membrane was effective in improving the electrical properties, whereas the glass fabric could significantly suppress the thermal conductivity. The results suggest that the glass fabric covered with nanocomposite fibers containing nanofillers (15 wt% CNT & 15 wt% CuO) has a high potential to enhance the resistance against external force by 56% on average, compared to the uncovered glass fabric. Besides the power factor of the TE composite structure can reach up to 19.61 μW m⁻¹ K⁻², which can power an output voltage of 3.2 V at a temperature difference from 20 to 80°C.     

The influence of solvent type and polymer concentration on the physical properties of solid state polymerized PA66 nanofiber yarn

We investigated the effects of two different solvent types and three solution concentrations on the electrospinning of solid state polymerized polyamide 66 (SSP PA66) nanofiber yarns. Nanofiber yarns were electrospun from SSP PA66 solutions in formic acid and formic acid/chloroform (3/1), using two oppositely metallic spinnerets system. Scanning electron microscopy (SEM) and X‐ray diffraction (XRD) were employed to characterize the morphology and properties of the nanofibrous yarns. Experimental results show that adding chloroform to formic acid as a binary solvent increases viscosity of polymer solution and the nanofibers diameter significantly. XRD patterns reveal that the presence of chloroform affects the crystallinity and the mechanical properties of the produced nanofibrous yarns. PA66 nanofiber yarn from 10 wt % formic acid/chloroform (3/1) solution was successfully electrospun with strength and modulus of 120.16 MPa and 1216.27 MPa respectively. It is also shown that the solution concentration has a significant effect on the modulus of the nanofibers yarns. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Thermal Cyclic Fatigue Behavior of Nanostructured YSZ/NiCrAlY Compositionally Graded Thermal Barrier Coatings

Oxidation of Metals - In this research, thermal cyclic fatigue (TCF) behavior of a compositionally graded layer (CGL) nanostructured coating was investigated and compared with the TCF behavior of a...     

Synthesis of daisy-shaped core–shell nanocomposites of chiral poly[(±)-2-(sec-butyl)aniline] in the solid state

Iranian Polymer Journal - A green and simple procedure is reported on the synthesis of a daisy-like core–shell nanocomposite of chiral poly[(±)-2-(sec-butyl)aniline]/nanosilica. This...     

Analysis of the high-pressure steam import behaviour of an integrated ethylene oxide/ethylene glycol petrochemical plant under different production scenarios

Integrated ethylene oxide/ethylene glycols (EO/EG) plants are prominent energy consumers in the petrochemical sector, particularly concerning high-pressure steam (HPS) usage which holds the potential for substantial energy savings. This study focuses on an unexplored territory: Examining the impact of EO catalyst type, selectivity, and glycols production capacity on HPS import in a plant in the Pars Special Economic Energy Zone (PSEEZ). Utilizing Python3 for data preprocessing and ordinary least squares linear regression analysis, we evaluate how varying catalyst loads and production scenarios influence HPS import. Regression models are created, encompassing normal and efficient HPS import scenarios, yielding positive outcomes in terms of correlation, mean error percentage, and R² analysis. Comparing normal and efficient HPS import models highlights potential savings, uncovering opportunities to conserve between 45 and over 200 tonnes per day of HPS. We also explore the plant's HPS behaviour under 1% selectivity and production capacity reductions. Notably, catalyst activity decline markedly escalates HPS import for hybrid catalysts, while selectivity decline decreases HPS import for high-activity catalysts. The models demonstrate that HPS import is ~150 tonnes per day more sensitive to a 1% change in selectivity compared to production capacity. Moreover, when comparing high activity and hybrid catalyst scenarios in normal and efficient cases, the most substantial HPS import difference arises under conditions of low selectivity, amounting to nearly 200 tonnes per day. Our methodology applies to other EO/EG plants. It is incorporated into our plant's energy management system, enabling continuous monitoring of steam import behaviour relative to catalyst and plant performance.     

Statistical modeling of global geogenic fluoride contamination in groundwaters

The use of groundwater with high fluoride concentrations poses a health threat to millions of people around the world. This study aims at providing a global overview of potentially fluoride-rich groundwaters by modeling fluoride concentration. A large database of worldwide fluoride concentrations as well as available information on related environmental factors such as soil properties, geological settings, and climatic and topographical information on a global scale have all been used in the model. The modeling approach combines geochemical knowledge with statistical methods to devise a rule-based statistical procedure, which divides the world into 8 different "process regions". For each region a separate predictive model was constructed. The end result is a global probability map of fluoride concentration in the groundwater. Comparisons of the modeled and measured data indicate that 60-70% of the fluoride variation could be explained by the models in six process regions, while in two process regions only 30% of the variation in the measured data was explained. Furthermore, the global probability map corresponded well with fluorotic areas described in the international literature. Although the probability map should not replace fluoride testing, it can give a first indication of possible contamination and thus may support the planning process of new drinking water projects.     

Mapping risk of cadmium and lead contamination to human health in soils of Central Iran

In order to map Cd and Pb contamination in the soils of the region of Isfahan, Central Iran, we performed indicator kriging on a set of 255 topsoil samples (0-20 cm) gathered irregularly from an area of 6800 km(2). The measured Cd concentrations exceeded the Swiss guide value in more than 80% of the samples whereas Pb concentrations exceeded the respective guide value only in 2% of the samples. Based on the simulated conditional distribution functions, the probability of exceeding the concentration of Cd and Pb from the specific threshold was computed. The results indicated that in most parts of the region the probability of contamination by Cd is very large (>0.95) whereas it is small (<0.5) for Pb. Based on a misclassification analysis, we chose the probability of 0.45 as optimum probability threshold to delineate the polluted from unpolluted areas for Cd. In addition, we performed a loss analysis to separate risks to human health from potential losses due to remediation costs. Based on this analysis a probability threshold of 0.8 was found to be the optimum threshold for the classification of polluted and unpolluted areas in the case of Cd. Health risks were found to be larger in the western parts of the region. Misclassification analysis was sufficient for risk mapping for Pb as its concentration did not reach risk levels for human health. A probability of 0.7 for Pb was found to be the optimum threshold for the delineation of polluted and unpolluted lands.