Opto-electronic and antibacterial activity investigations of mono-dispersed nanostructure copper oxide prepared by a novel method: reduction of reactive oxygen species (ROS)

A novel approach for synthesis of copper oxide nanoparticles is reported by separation of nucleation and growth. The nano-material was characterized by X-ray diffraction, field emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, and UV–Vis diffuse reflectance spectroscopy, transmission electron microscopy, atomic force microscopy, and Brunauer–Emmett–Teller analyses. Optical analysis of mono-dispersed nanostructure copper oxide by UV–Vis diffused reflectance spectroscopy showed the band gap value of 1.47 eV with a blue-shift in the optical band gap due to quantum confinement effect. The dynamic light scattering and zeta potential results showed fairly narrow size distribution and colloidal stability. The results showed that nano-particles were mono-dispersed spheres of 8 nm with no aggregation. Cell viability of treated murine fibroblast cell line (L-929) treated by different concentrations of nanoparticles showed significant viability up to 96% at concentrations 15 and 30 μg ml^?1. The nanoparticles exhibited outstanding and stable antibacterial activity against Staphylococcus aureus ATCC 6538 at 30 µg ml^?1. The viability and reactive oxygen species (ROS) generation in the L-929 cell line indicated that the nanoparticles were not toxic at the concentrations which were effective on bacteria. ROS analysis using DCFH-DA probe on L-929 were exposed to 7.5–60 μg ml^?1 of copper oxide nanoparticles in 6 h revealed ROS generation was decreased dramatically compare to the untreated cells and positive control.     

Big Data Based Self-Optimization Networking in Next Generation Mobile Networks

Using self-optimization techniques is the only viable solution for increasing the efficiency in next generation mobile networks. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to microcell and femto-cell users, considering the limited resources in radio access networks. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process. Based on the meaningful extracted information, the SON decision maker will be able to adjust network parameters and resource allocation factors in a more intelligent manner. The experimental results show that despite the tremendous volume of the analyzed data—which is hundreds of times bigger than usual methods—it is possible to improve the KPIs, such as throughput, up to 30% by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25% when the number of parameters is too high. Moreover, numerical results indicate the superiority of using support vector machine learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15% increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme.     

Using the TPS method for determining the thermal properties of concrete and wood at elevated temperature

The transient plane source (TPS) method is shown to be very promising for determining thermal properties of materials at room temperature as well as temperatures up to 700°C. To investigate the applicability of the method it has been used in the study for determining thermal properties of wood (spruce) and concrete. Conductivity (λ) and diffusivity (α) were determined simultaneously. The thermal properties thus obtained have been compared with some values found in literature. The paper also presents results where calculations using properties obtained with the TPS method are compared with fire test measurements. The results are very encouraging. Copyright © 2005 John Wiley & Sons, Ltd.     

A flexible energy management approach for smart healthcare on the internet of patients (IoP)

Considering the importance of biosensors on the Internet of the patient body that collect vital signs and transmit them to the coordinator, energy consumption and network lifetime are essential challenges in these networks. This paper, it has been tried to present a method based on adapting sampling rate through patient’s risk and discovered pattern by employing an intelligence method based on adaptive neuro-fuzzy inference system, interpolation function, and a biosensor patron. It causes restricting sensed and transmitted data to the coordinator. In the proposed schema, three methods containing Grid partitioning, Subtractive Clustering and fuzzy c-means have been used in two modes, including hybrid and error backpropagation, to predict the individual’s behavioral pattern and determine the patient's risk, attentively. The simulation results in MATLAB R2018b show that the proposed method reduces the network communications. It has improved energy consumption by up to three times and also reduced traffic by more than 80% compared to similar methods.

     

Accuracy and availability modeling of social networks for Internet of Things event detection applications

Wireless Networks - As Social networks are widely used by the people around the world, if this infrastructure can be used for event detection systems like fire forest detection, the overall cost of...     

Linux Support for Fast Transparent General Purpose Checkpoint/Restart of Multithreaded Processes in Loadable Kernel Module

Checkpoint/Restart is the ability to save the state of a running application so that it can later resume its execution from the time of the checkpoint. These are techniques with many potential applications, including establishment of a fault-tolerant environment, improving system resource utilization, and true migration of a process. With increasing hardware speed and size of clusters the average time between failures has been reduced. Therefore, fault tolerance and ability to checkpoint a process have become inevitable. Almost all platforms deployed for high-performance computing support process checkpoint/restart. Linux as one of the popular operating systems does not provide a general purpose implementation. Some are limited to specific type of parallel programming library, confined to some unique well-behaved type of applications, or reliant on specific features in kernel which could be missing on many occasions. Most of implementations demand elaborate practice of recompiling a whole kernel to apply required patches. In this paper, we describe the design and implementation of multithreaded process checkpoint/restart system for Linux which provide capability of dynamic extension to increase compatibility and reduce system overhead. It does not impose any requirement on the existence of a special facility in the operating system and can do checkpoint/restart of an application independent of their behavior and fully transparent. The entire system is absolutely implemented in multiple kernel loadable modules, which result in ease of use and eliminate the burden of complex system administration.     

Estimation of wax deposition in the oil production units using RBF-ANN strategy

Accurate prediction of the solid deposition is petroleum industry can result in increasing the production efficiency. This can also result in the elimination of a major industrial problem, namely the wax deposition. In this study, application of intelligent methods in prediction of the wax deposition is investigated by developing a radial basis function artificial neural network. Levenberg Marquardt algorithm is also applied to determine the optimum predictions. Results from the proposed model are also compared to Kamari et al. model revealing the better performance of the proposed RBF-ANN. The validity of the proposed model is also investigated using statistical and graphical approaches, illustrating the great capability of the proposed RBF-ANN in accurate prediction of the wax deposition. R-squared and mean squared error values of 0.9975 and 0.029251 are obtained for the proposed model, revealing the validity of the RBF-ANN in estimating the wax deposition.     

Plastic collapse pressure of cylindrical vessels containing longitudinal surface cracks

Based on nonlinear finite element analysis, the plastic collapse pressures of cylindrical vessels with longitudinal surface cracks are computed. A general formula of plastic collapse pressure of such structures are given and compared with the literature solutions. The results of the present study could be applied for the integrity assessments, failure analyses, remanent life assessment, and licence extensions of the vessels.