Search

Atherosclerosis is a chronic inflammatory disease with high mortality worldwide. The reverse cholesterol transport pathway in macrophage plays an important role in the pathogenesis of coronary artery disease (CAD) and is strongly controlled by regulatory factors. The microRNAs can promote or prevent the formation of atherosclerotic lesions by post-transcriptional regulation of vital genes in this pathway. Therefore, this study was conducted to investigate the relationship between the expression levels of miR-27a, miR-329, ABCA1, and ABCG1 genes and serum levels of hs-CRP, ox-LDL, and indices of oxidative stress in the patients with established CAD and controls. A total of 84 subjects (42 patients with CAD and 42 controls) were included in this study. Expression levels of miR-27a-3p, miR-329-3p, ABCA1, and ABCG1 genes in the peripheral blood mononuclear cells (PBMCs) and serum concentration of hs-CRP and ox-LDL were measured by real time-PCR and ELISA, respectively. Also, oxidative stress parameters in the serum were evaluated by ferric-reducing antioxidant power (FRAP) and malondialdehyde (MDA) assays. ABCA1 and ABCG1 gene expression in PBMC and serum concentration of FRAP were significantly lower in the CAD group compared to the control group. Expression levels of miR-27a and miR-329 and serum levels of hs-CRP, ox-LDL, and MDA were significantly higher in the CAD group compared to the control group. Serum levels of hs-CRP, ox-LDL, and expression level of miR-27a have inversely related to ABCA1 and ABCG1 gene expression in all the subjects. An increase in the expression levels of miR-27a and miR-329 may lead to the progression of atherosclerosis plaque by downregulating the expression of ABCA1 and ABCG1 genes.

Coronary Artery Disease (CAD) is a chronic inflammatory disease caused by atherosclerosis and arteries become clogged due to plaque formation, fat accumulation, and various sorts of immune cells. IL-32 is a new proinflammatory cytokine, which enhances inflammation through inducing different inflammatory cytokines. The purpose of current research was to assess IL-32 serum levels in coronary artery disease subjects and its relationship with serum levels of IL-6 and TNF-α. Forty-two subjects diagnosed with CAD and thirty-nine control subjects were enrolled in the research. Serum levels of IL-6, TNF-α, and IL-32 were measured using the enzyme-linked immunosorbent assay (ELISA). IL-32, TNF-α, and IL-6 serum levels were significantly higher by 2.7, 3.48, and 3.2-fold in the CAD subjects than in control subjects, respectively. Moreover, no significant difference was found in TNF-α, IL-6 and IL-32 serum levels with the clogged arteries number in the CAD group. TNF-α and IL-32 serum levels in the CAD subjects with cardiac arterial stenosis in one major vessel were significantly increased than CAD subjects with cardiac arterial stenosis in more than one major vessels. ROC curve analysis revealed that serum levels of IL-32, TNF-α, and IL-6 showed good abilities in predicting CAD. Also, Multiple logistic regression analyses suggested that TNF-α, IL-6, and IL-32, serum levels of LDL and ox-LDL were independently related to the presence of CAD, while HDL serum levels were not. TNF-α, IL-32, and IL-6 showed an increase in CAD group and serum levels of these cytokines showed good abilities in predicting CAD. Our data suggested the involvement of TNF-α and IL-32 in the early stage of CAD.

Hexagonal upconverting nanoparticles (UCNPs) of NaYF4:Er3+,Yb3+ (ca. 300 nm) have been widely used to measure the temperature at the nanoscale using luminescence ratio thermometry. However, several factors limit their applications. For example, changes in the peak shape, mainly is the S-band emission, hinders their ability to be used as a universal temperature sensor. Herein, we introduce a universal calibration protocol for NaYF4:Er3+,Yb3+ upconverting nanoparticles that is robust to environmental changes and gives a precise temperature measurement. We used this new procedure to calculate the temperature profile inside a Taylor cone generated with an electrospray jet. Inside the Taylor cone the fluid velocity increases toward the tip of the cone. A constant acquisition length leads to a decrease in excitation and acquisition time. This decrease in excitation time causes a peak shape change that corrupts the temperature measurement if the entire peak shape is integrated in the calibration. Our universal calibration circumvents this problem and can be used for time-resolved applications. The temperature at the end of the Taylor cone increases due to the creation of a whispering gallery mode cavity with 980 nm excitation. We use time-resolved energy balance equations to support our optical temperature measurements inside the Taylor cone. We believe that the findings of this paper provide a foundation for time-resolved temperature measurements using NaYF4:Er3+,Yb3+ upconverting nanoparticles and can be used to understand temperature-dependent reactions such as protein unfolding inside microjet/microdroplets and microfluidic systems.

The fuel source of many simple and combined-cycle power plants usually comes from a nearby natural gas transmission pipeline at a pressure from 50 to over 70 bar. The use of a turboex-pander instead of throttling equipment offers a promising alternative to regulate the pressure of natural gas introduced to the power plant. Specifically, it helps recover part of the available energy of the compressed gas in the transmission pipeline, increase the power output and efficiency of the gas turbine system, and decrease the fuel use and harmful emissions. In this paper, the addition of such a turboexpander in a gas pressure-reduction station is studied. The recovered power is then used to drive the compression of extra air added to the combustion chamber of a heavy-duty gas turbine. The performance of this configuration is analyzed for a wide range of ambient temperatures using energy and exergy analyses. Fuel energy recovered in this way increases the output power and the efficiency of the gas turbine system by a minimum of 2.5 MW and 0.25%, respectively. The exergy efficiency of the gas turbine system increases by approximately 0.36% and the annual CO2 emissions decrease by 1.3% per MW. Reza Shirmohammadi would like to acknowledge the Erasmus + International Credit Mobility (KA107-2020 project), Alianza 4 Universidades, and International Affairs at University of Tehran and Carlos III University of Madrid. Fontina Petrakopoulou would like to thank the Spanish Ministry of Science, Innovation and Universities and the Universidad Carlos III de Madrid (Ramón y Cajal Programme, RYC-2016-20971).

In this chapter we present a new optical temperature measurement and thermal imaging technique combining near-field microscopy and Er3+ photoluminescence spectroscopy. The technique is used with two different approaches towards local temperature measurement and thermal imaging. In the first approach, gold nanostructures on top of Al0.94 Ga0.06 N thin film embedded with Er3+ ions are optically excited through the SNOM tip with 532 nm CW laser to generate thermal images with the spatial resolution comparable to the true size of the nanostructures. In the second approach, nanostructures on top of thermal sensor film are excited with 532 nm CW laser through the substrate with a large spot size (FWHM ~ 10 µm). The Er3+ emission from the film is collected in transmission mode through the SNOM tip. In this chapter the steady state temperature change under optical illumination is measured for different sized clusters made from 40 nm diameter gold nanoparticles and it is found that the maximum temperature change and temperature decay length (r1/2) into the surrounding medium increases linearly with cluster radius. Based upon this observation, we can conclude that if a large cell is embedded with optical heaters with a distance between heaters that allows for collective heating, then the thermal profile outside of the cell decays with a decay constant that is dependent upon the size of the cell. This chapter is reprinted (adapted) with permission from Nanoscale, 2017, 4(7), pp 1864–1869. Copyright 2017 Royal Society of Chemistry.

In this chapter, we present working principle and experimental details of AlGaN:Er3+ nanothermometry technique based on Er3+ emission that offers nanoscale temperature measurement and thermal imaging of nanoparticles/nanostructures under optical illumination. A thin film of Al0.94Ga0.06N with embedded Er3+ ions is used as a thermal sensor for nanoscale temperature measurement and thermal imaging of optically excited gold nanostructures under far-field illumination. This thermal sensor film is based on relative photoluminescence intensities of energy transitions from thermally coupled 2H11/2 and the 4S3/2 energy levels to the 4I15/2 energy level of the Er3+ ions. The temperature profile around an optically excited nanostructure can be generated under imaging mode, and a dynamic measurement of temperature can also be performed using this thermal sensor film.

A new optical probe technique using a laser-trapped erbium oxide nanoparticle (~150 nm) can measure absolute temperature with a spatial resolution on the size of the nanoparticle. This technique (scanning optical probe thermometry) is used to collect the thermal image of an optically excited gold nanostructures. The thermal profile has a Gaussian line shape that is a convolution of the point spread function of the scanning optical probe thermometer and the true thermal profile. A convolution analysis reveals that the point spread function of our measurement is a Gaussian with a FWHM of 165 nm. We attribute the width of this function to clustering of Er2O3 nanoparticles in solution. Also, the scanning optical probe thermometer is used to measure the temperature where vapor nucleation occurs. Subsequently, the temperature inside the vapor bubble rises to the melting point of the gold nanostructure (~1300) where a temperature plateau is observed. The rise in temperature is attributed to inhibition of thermal transfer to the surrounding liquid by the thermal insulating vapor cocoon. This chapter is reprinted (adapted) with permission from Applied Physics A. (2016) 122: 340. Copyright 2016 Springer.

© 2017 IEEE. Large scale coverage holes (CHs) resulting from correlated node failures, can significantly degrade quality of service and also jeopardise the integrity of WSNs. In the absence of centralised control, the distributed relocation of deployed nodes becomes a promising solution especially in harsh and hostile environments. In this paper, a distributed method is proposed that enables a network to partially or entirely repair itself through a collection of distributed movements of disjoint spanned trees (DS-Trees) towards the CHs. DS-Trees are spanned based on the nodes distances from the CHs, which are autonomously perceived by the nodes and their one-hop neighbours. DS-Trees around the CHs are spanned in a downstream style outwards from the holes, as nodes select their parents based on the minimum distance to the CH from their neighbours; nodes then decide whether or not to follow the movements of their DS-Trees parents. To examine the efficiency of the proposed model, its performance is compared with two Voronoi-based and one force-based node relocation algorithms. Results show that the proposed DS-Tree model either outperforms or matches the alternative approaches across a wide range of scenarios.

In this paper, we introduce a new optical temperature and thermal imaging technique combining near-field microscopy and Er3+ photoluminescence thermometry. The tip aperture of 120 nm limits the spatial resolution of near-field thermal imaging. We use the technique with two different approaches towards local temperature measurement and thermal imaging. In the first approach, gold nanostructures on top of Al0.94Ga0.06N thin film embedded with Er3+ ions are optically excited through the SNOM tip with 532 nm CW laser to generate thermal images that have a Gaussian thermal profile because heating and probing are done through a single tip aperture. In the second approach, nanostructures on top of thermal sensor film of AlGaN : Er3+ ions deposited on a transparent sapphire substrate are excited with 532 nm CW laser through the substrate with a large spot size (FWHM ∼10 μm) and Er3+ emission from the film is collected in transmission mode through the SNOM tip. We use this approach to measure steady-state thermal profiles from optically excited different sized clusters made from 40 nm gold nanoparticles. This approach yields steady-state thermal profiles that have inverse distance temperature decay away from the cluster and we find that the maximum temperature change and temperature decay length into the surrounding medium (r½) scales with cluster radius.

© 2017 IEEE. Coverage holes (CHs) due to the correlated node failures, if not appropriately addressed in a timely manner, not only disrupt network's operation but also can compromise its integrity. In the absence of centralised control, distributed node relocation can be an effective solution to recover CHs. Relocation algorithms that mainly applied to all nodes to address networks' coverage and unbalanced deployments problems, are not efficient as participating nodes beyond a certain distance and depth from the CHs do not significantly contribute to recovery of CHs. Here, the effects of the number of participating nodes and movement iterations on recovery of CHs are examined in two Voronoi-based and one force-based node relocation algorithms.

This work describes a cooperative quantum dot and plasmonic effect on improving the performance of dye synthesized solar cells, in which CdS QDs, gold nanoparticles (GNPs), and gold nanorods (GNRs) are incorporated into the active layer. The cooperative nanoparticles show a superior behavior on enhancing light absorption in comparison with single nanoparticles, which led to dye synthesized solar cells with a power conversion efficiency accounting for a 147% enhancement. The cooperative CdS QDs and plasmonic effect arose from the cooperation of the resonance enhancement of QDs and two different nanostructures. Detailed studies shed light into the influence of quantum dots and plasmonic nanostructures on dissociation, exciton generation, and charge transport as well as recombination inside these devices.

A multiuser multi-input multi-output (MU-MIMO) downlink system with block diagonalization-based precoding is presented with a study of the impact of limited bandwidth resource for the feedback-links on the sum capacity. The channel estimation is acquired at each user via downlink training, and the linear quantized form of estimated channels is fed back to the base station (BS) via noisy, finite-rate channel state information (CSI) feedback-links. Two architectures for CSI feedback are considered: parallel feedback where there is a feedback-link for each user; and serial feedback where a single feedback-link is used for all the users. The sum capacity is formulated as a function of the link parameters, enabling optimization of the number of training symbols and required feedback bits with the criterion of maximum sum capacity. The approach allows a performance comparison for different feedback architectures, the number of transmit antennas, and the number of users. The effect of time variations of the channel is analyzed via simulation, quantifying the loss of sum capacity relative to the case with perfect CSI for different Doppler frequencies.

© 2015 IEEE. Unlike sporadic node failures, coverage holes emerging from multiple temporally-correlated node failures can severely affect quality of service in a network and put the integrity of entire wireless sensor networks at risk. Conventional topology control schemes addressing such undesirable topological changes have usually overlooked the status of participating nodes in the recovery process with respect to the deployed sink node(s) in the network. In this paper, a cooperative coverage hole recovery model is proposed which utilises the simple geometrical procedure of circle inversion. In this model, autonomous nodes consider their distances to the deployed sink node(s) in addition to their local status, while relocating towards the coverage holes. By defining suitable metrics, the performance of our proposed model performance is compared with a force-based approach.

In harsh and hostile environments, swift relocation of currently deployed nodes in the absence of centralized paradigm is a challenging issue in WSNs. Reducing the burden of centralized relocation paradigms by the distributed movement models comes at the price of unpleasant oscillations and excessive movements due to nodes' local and limited interactions. If the nodes' careless movements in the distributed relocation models are not properly addressed, their power will be exhausted. Therefore, in order to exert proper amount of virtual radial/angular push/pull forces among the nodes, a fuzzy logic relocation model is proposed and by considering linear combination of the presented performance metric(s)(i.e. coverage, uniformity, and average movement), its parameters are locally and globally tuned by particle swarm optimization(PSO). In order to tune fuzzy parameters locally and globally, PSO benefits respectively from nodes' neighbours within different ranges and all the given deployed area. Performance of locally and globally tuned fuzzy relocation models is compared with one another in addition to the distributed self-spreading algorithm (DSSA). It is shown that by applying PSO to the linear combinations of desired metric(s) to obtain tuned fuzzy parameters, the relocation model outperforms and/or is comparable to DSSA in one or more performance metric(s). Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.

Physical damage and/or node power exhaustion may lead to coverage holes in WSNs. Coverage holes can be directly detected by certain proximate nodes known as boundary nodes (B-nodes). Due to the sensor nodes' redundant deployment and autonomous fault detection, holes are surrounded by a margin of B-nodes (MB-nodes). If all B-nodes in the margin take part in the hole recovery processes, either by increasing their transmission power or by relocating towards region of interest (ROI), the probability of collision, interference, disconnection, and isolation may increase affecting the rest of the network's performance and QoS. Thus, distributed boundary node selection algorithms (BNS-Algorithms) are proposed to address these issues. BNS-algorithms allow B-nodes to self-select based on available 1-hop information extracted from nodes' simple geometrical and statistical features. Our results show that the performance of the proposed distributed BNS-algorithms approaches that of their centralized counterparts. © 2011 IEEE.

In this paper, we present an orthonormal version of the new information criterion (NIC) algorithm for fast estimation and tracking of signal subspace using a two-layer linear neural network (NN). The fast orthonormal NIC referred as to FONIC algorithm guarantees the orthonormality of the weight matrix at each iteration. The proposed FONIC algorithm has a linear complexity which makes it efficient in real time applications. The FONIC algorithm provides a fast on-line learning of the optimum weights for the two-layer linear. Simulation results show better performance of FONIC algorithm than the NIC algorithm.

In this paper OFDM and MC-DS-CDMA techniques performances in down-stream (downlink) high voltage digital PLC (HV dPLC) system have been studied under the same overall working conditions (i.e., transmitted power and global data rate). By using MC-DS-CDMA we have a compromise between SNR and transmission bandwidth. However, by increasing the number of users we can attain a higher data rates for a given bandwidth. Comparisons show that by using least square (LS) channel estimation and comb type pilot frame format, MC-DS-CDMA outperforms OFDM under the same conditions at lower SNRs.

© 2001-2012 IEEE. Coverage holes (CHs) can compromise the reliability and functionality of wireless sensor networks. The recovery of CHs is challenging, especially in distributed applications where sensors have little knowledge about other sensors' actions. We propose a new game theoretic approach for recovering the CHs in a distributed manner. The key idea is that we formulate a potential game between the sensors, where each mobile sensor in the network only depends on local knowledge of its neighboring nodes and takes CH recovery actions recursively with global convergence. An appropriate combined action of physical relocation and sensing range adjustment can be taken by each sensor to reduce the CHs in an energy-efficient way. Simulation results show that the proposed game theoretic approach is able to substantially increase network lifetime and maintain network coverage in the presence of random damage events, as compared with the prior counterpart(s).

In this paper, we have proposed a secure semi-fragile watermarking technique for content authentication of digital images. A binary image is encrypted in an efficient way and then embedded as the watermark for tamper detection. The watermark is embedded by applying a quantization index modulation process on Euclidean norm of singular values of blocks in the wavelet domain. To avoid visual degradation of watermarked image, we have enhanced a model to take into consideration blocks statistics of the host image. Experimental results are presented for well known images to demonstrate effectiveness of our method.

The use of Probabilistic Neural Network (PNN) is very common in supervised pattern recognition applications. PNN is based on Bayes decision rule and it uses Gaussian Parzen windows for estimating the probability density functions (pdf) required in Bayes rule. The conventional PNN needs a single spread value for pdf estimation which is proportional to Gaussian window width. In this paper we will suggest the use of a multi-spread PNN structure whose spread values are estimated using the training data. In addition, we will introduce several new discriminating features of acoustic radiated noise which can be used for ship noise classification. These features will be used as discriminating features in the conventional and multi-spread PNN. Finally, the performance of the conventional PNN and the suggested multi-spread PNN in classifying real ship noise data will be compared. Results of this comparison show that the performance of the multi-spread PNN is better than the conventional PNN.

In this paper, we present a new algorithm for tracking the signal subspace recursively. It is based on a new interpretation of the signal subspace. We introduce a novel information criterion for signal subspace estimation. We show that the solution of the proposed constrained optimization problem results the signal subspace. In addition, we introduce three adaptive algorithms which can be used for real time implementation of the signal subspace tracking. The computational complexity of the proposed signal subspace tracking algorithms are O(nr 2) which is much less than the direct computation of singular value decomposition or even some algorithms. Simulation results in the direction of arrival (DOA) tracking context depict excellent performance of the proposed algorithm.