Relief from vascular occlusion using photothermal ablation of thrombus with a multimodal perspective

Fibrinolytic therapy for arterial or venous thrombotic disorders involves the systemic administration of thrombolytics such as streptokinase,which is associated with serious bleeding complications.With this study,we provide a proof-of-concept of photothermal thrombus ablation with gold nanorods exposed to near-infrared irradiation,both in vitro using materials generated from purified fibrinogen or plasma and in vivo in murine blood vessels.This is the first report of the application of photothermal therapy as an anti-thrombotic measure.Remarkably,the addition of streptokinase had a multimodal additive effect with regard to acceleration of photothermal lysis of thrombi even at a dose significantly below the therapeutic concentration,thus minimizing the lifethreatening side effects and adverse complications.This combinatorial approach exhibits great promise for lysing pathological clots while effectively overcoming the drawbacks of existing therapies.     

An improved fault detection crow search algorithm for wireless sensor network

In wireless sensor networks (WSNs), the collected data during monitoring environment can have some faulty data, and these faults can lead to the failure of a system. These faults may occur due to many factors such as environmental interference, low battery, and sensors aging etc. We need an efficient fault detection technique for preventing the failures of a WSN or an IoT system. To address this major issue, we have proposed a new nature-inspired approach for fault detection for WSNs called improved fault detection crow search algorithm (IFDCSA). IFDCSA is an improved version of the original crow search algorithm (CSA). The proposed algorithm first injects the faults into the datasets, and then the faults are classified using improved CSA and machine learning classifiers. The proposed work has been evaluated on the three real-world datasets, ie, Intel lab data, multihop labeled data, and SensorScope data, and predicts the faults with an average accuracy of 99.94%. The results of the proposed algorithm have been compared with the three different machine learning classifiers (random forest, k-nearest neighbors, and decision trees) and Zidi model. The proposed algorithm outperforms the other classifiers/models, thus generating higher accuracy and lower features without degrading the performance of the system. Index Terms—big data, crow search algorithm, IoT, machine learning, nature-inspired algorithm, wireless sensor network.     

An Enhanced Crow Search Inspired Feature Selection Technique for Intrusion Detection Based Wireless Network System

Wireless Personal Communications - Recent development of cognitive computing driven evolutionary techniques improve the overall quality of service and user experience in wireless communication...     

Lifetime Enhancement of Sensor Networks by the Moth Flame Optimization

Wireless Personal Communications - Advancements in wireless communication technologies have facilitated the deployment of large-scale Wireless Sensor Networks (WSNs). Due to the constraint of...     

Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images

Multimedia Tools and Applications - The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare...     

New Approximate Expressions for α–η–μ and α–κ–μ Fading Channels Subjected to Inverse Gaussian Shadowing With Applications

Time synchronization is an essential characteristic for the faithful functioning of wireless sensor networks. Hypothetically, it is stated that the two clocks are considered to be synchronized with each other if their clock offset and frequency components within the oscillator are running at the same rate. But, practically, there is some degree of inaccuracy in the manufacturing of these clocks; as a result, their frequency sources have slight variations. This paper proposes a unique mathematical model which is based on maximum probable theory maximum probable clock offset estimation, along with the propagation delay factor for finding the best probable estimate for clock offset. In addition, a novel two-way message passing scheme-based network communication model is presented here. Based on results, it has been predicted that estimating the clock offset will help in improving the efficiency of the existing time-synchronization protocols i.e. time-sync protocol for sensor networks (TPSNs). It was observed that the standard error deviation of modified TPSN with MPCEO was reduced from 2.324 to 0.064 ms. This will make the modified TPSN model more accurate, efficient, and reliable.

     

Tracking Control of Uncertain Nonlinear Systems With Unknown Constant Input Delay

A robust delay compensator has been developed for a class of uncertain nonlinear systems with an unknown constant input delay.The control law consists of feedback terms based on the integral of past control values and a novel filtered tracking error,capable of compensating for input delays.Suitable Lyapunov-Krasovskii functionals are used to prove global uniformly ultimately bounded(GUUB)tracking,provided certain sufficient gain conditions,dependent on the bound of the delay,are satisfied.Simulation results illustrate the performance and robustness of the controller for different values of input delay.     

Thermophysical properties of densified pitch based carbon/carbon materials—II. Bidirectional composites

Bidirectional carbon/carbon composites were developed using high-pressure impregnation/carbonization technique with PAN based carbon fabric as reinforcement and coal tar and synthetic pitches as matrix precursors. Microstructure of these composites has been evaluated using scanning electron microscope and polarized light optical microscope. Thermophysical properties i.e. thermal conductivity and specific heat have been evaluated both at room temperature and between 40 and 300 °C. The temperature dependence of thermal diffusion, specific heat and thermal conductivity has been studied and correlated with microstructure of carbon/carbon composites. It is found that the specific heat of carbon/carbon composites shows increase with temperature with an inverse slope in the temperature range of 150-200 °C. Accordingly, though the thermal conductivity decrease with temperature due to increased phonon interactions, it shows reversible action between 150 and 200 °C.     

Characterization of cobalt precipitation tube synthesized through “silica garden” route

Cobalt precipitation tube synthesized through “silica garden” route was characterized using a number of analytical techniques. One of the prominent phases was identified as hydrated β-Co₂SiO₄. Particle clusters with diameter smaller than 40 nm could be observed. Chemical composition of the exterior and interior wall of the tube was remarkably different with distinct concentration gradient for O, Na, Si, Cl and Co. Synthesized tube was weakly magnetic with low retentivity and coercivity. Chemical analyses confirmed presence of non stoichiometric compositions. Cobalt precipitation tube was hygroscopic and contained both physically and chemically bound moisture.