Towards a Sustainable Green Design for Next-Generation Networks

Recently distributed real-time database systems are intended to manage large volumes of dispersed data. To develop distributed real-time data processing, a reality and stay competitive well defined protocols and algorithms must be required to access and manipulate the data. An admission control policy is a major task to access real-time data which has become a challenging task due to random arrival of user requests and transaction timing constraints. This paper proposes an optimal admission control policy based on deep reinforcement algorithm and memetic algorithm which can efficiently handle the load balancing problem without affecting the Quality of Service (QoS) parameters. A Markov decision process (MDP) is formulated for admission control problem, which provides an optimized solution for dynamic resource sharing. The possible solutions for MDP problem are obtained by using reinforcement learning and linear programming with an average reward. The deep reinforcement learning algorithm reformulates the arrived requests from different users and admits only the needed request, which improves the number of sessions of the system. Then we frame the load balancing problem as a dynamic and stochastic assignment problem and obtain optimal control policies using memetic algorithm. Therefore proposed admission control problem is changed to memetic logic in such a way that session corresponds to individual elements of the initial chromosome. The performance of proposed optimal admission control policy is compared with other approaches through simulation and it depicts that the proposed system outperforms the other techniques in terms of throughput, execution time and miss ratio which leads to better QoS.

     

A framework for network quality monitoring in the VoIP environment

Monitoring speech quality in Voice over IP (VoIP) networks is important to ensure a minimal acceptable level of speech quality for IP calls running through a managed network. Information such as packet loss, codec type, jitter, end‐to‐end delay and overall speech quality enables the network manager to verify and accurately tune parameters in order to adjust network problems. The present article proposes the deployment of a monitoring architecture that collects, stores and displays speech quality information about concluded voice calls. This architecture is based on our proposed MIB (Management Information Base) VOIPQOS, deployed for speech quality monitoring purposes. Currently, the architecture is totally implemented, but under adjustment and validation tests. In the future, the VOIPQOS MIB can be expanded to automatically analyze collected data and control VoIP clients and network parameters for tuning the overall speech quality of ongoing calls. Copyright © 2006 John Wiley & Sons, Ltd.     

MESFET DC model parameter extraction using Quantum Particle Swarm Optimization

This paper presents two techniques for DC model parameter extraction for a Gallium Arsenide (GaAs) based MEtal Semiconductor Field Effect Transistor (MESFET) device. The proposed methods uses Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO) methods for optimizing the difference between measured data and simulated data. Simulated data are obtained by using four different popular DC models. These techniques avoid complex computational steps involved in traditional parameter extraction techniques. The performance comparison in terms of quality of solution and execution time of classical PSO and QPSO to extract the model parameters are presented. The validity of this approach is verified by comparing the simulated and measured results of a fabricated GaAs MESFET device with gate length of 0.7 μm and gate width of 600 μm (4 × 150). Simulation results indicate that both the technique based on PSO and QPSO accurately extracts the model parameters of MESFET.     

3D Printing and Nanotechnology: A Multiscale Alliance in Personalized Medicine

3D printing and nanotechnology have been two important tools in the development of therapeutic approaches for personalized medicine. More recently, their alliance has been improved in an effort to build innovative, versatile, multifunctional, and/or smart medical and pharmaceutical products. Therefore, an extensive review about scientific studies that ally 3D printing and nanomaterials in the development of new approaches for pharmaceutical and medical applications for the treatment and prevention of diseases is presented here. The articles are classified into five categories according to their main application: Cell growth and tissue engineering, antimicrobial, drug delivery, stimulus-response, and theranostics. Semisolid extrusion, inorganic nanoparticles, and cell growth and tissue engineering are the most reported 3D printing technique, type of nanomaterial, and application, respectively. The increase in papers dedicated to these areas is also notable, especially in the 2019 and 2020, when semisolid extrusion became the most used technique, overcoming fused deposition modelling. In fact, this review highlights that the possibility of an alliance between 3D printing and nanotechnology for the production of multiscale materials is undoubtedly a great opportunity for knowledge and innovation in the pharmaceutical and medical area.     

Correlation study between VAD preform deposition surface and Germanium doping profiles

The refractive index profile of germanium doped preforms for optical fibers is determined by the radial distribution of germanium concentration. Knowing that there is a correlation between the germanium doping profile and the deposition surface profile of vapor-phase axial deposition (VAD) preforms, the study of this correlation has been carried out in order to estimate, indirectly, the refractive index profile of VAD preforms for optical fibers during the deposition stage. This correlation was studied through the parameterization of the preform deposition surface using two parameters: the power law index profile that best fits the preform bottom profile (/spl alpha/) and the axial distance from the bottom tip to a reference height (h). A range of values of these parameters to produce VAD preforms with standard and special doping profiles has been presented. Preforms with triangular index profile can be fabricated with /spl alpha/ and h values of about 2.0 and 5.0 mm, respectively, and preforms with parabolic index profiles can be produced with /spl alpha/ and h values of about 2.0 and 4.0 mm, respectively.     

64-Mb/s data transmission beyond the relaxation oscillation frequency using monolithically integrated two laterally coupled diode lasers

In this letter, we demonstrate the possibility of using two monolithically integrated laterally coupled diode lasers for data transmission beyond the intrinsic relaxation oscillation frequency of semiconductor lasers. The resonance of the lateral modes of such structure provides a peak in the small signal modulation response of the device above the relaxation oscillation frequency of the intrinsic emitters that is studied in terms of relative intensity noise and phase noise to characterize this new, simple, and compact device. Optical transmission of a 64-Mb/s pseudorandom bit sequence signal using a microwave carrier over this resonance peak of the lateral modes above the relaxation oscillation is demonstrated for the first time to our knowledge.     

Estimation of breast tumor thermal properties using infrared images

The major goal of this paper is to help detect breast cancer early based on infrared images. Some procedures, protocols and numerical simulations were developed or performed. Two different issues are presented. The first is the development of a standardized protocol for the acquisition of breast thermal images including the design, construction and installation of mechanical apparatus. The second part is related to the greatest difficulty for the numerical computation of breast temperature profiles that is caused by the uncertainty of the real values of the thermophysical parameters of some tissues. Then, a methodology for estimating thermal properties based on these infrared images is presented. The commercial software FLUENT^TM was used for the numerical simulation. A Sequential Quadratic Programming (SQP) method was used to solve the inverse problem and to estimate the thermal conductivity and blood perfusion of breast tissues. The results showed that it is possible to estimate the thermophysical properties using the thermography. The next stage will be to use the geometry of a real breast for the numerical simulation in conjunction with a linear mapping of the temperatures measured over the breast volume.     

Financial Times Series Forecasting of Clustered Stocks

Mobile Networks and Applications - Predicting the stock market is a widely studied field, either due to the curiosity in finding an explanation for the behavior of financial assets or for financial...     

Latent communities of digital publications: The role of editors,followers, and advertisers

This research explores how digital publications may be turned into something more than just a meeting point for people who are seeking information and may become a latent community. The paper proposes that the network of editors, followers, and advertisers of digital publications determine the user sense of community towards the publication. While the pattern of connections among editors and followers reinforces the sense of community, advertisers act as intruders who weaken the feasibility of building a community. Data were collected from followers of digital publications. The dynamics of the sense of community is described as a process in which informational value and identification are essential to nurturing readers’ soft or hard commitment. Findings indicate that the strong ties between editors and followers as well as the diversity positively impacts on informational value and identification, whereas advertising intrusiveness diminishes the informational value.