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Relaxor ferroelectrics with high energy density and efficiency are good candidates for pulsed power devices. In the present work, (1-x) (0.7Bi0·5Na0·5TiO3-0.3SrTiO3)-xNaNbO3 (x = 0.0–0.10) [(1-x)(0.7BNT-0.3ST)-xNN] ceramics were prepared using conventional solid state reaction method. The typical relaxor ferroelectric behavior was found in all compositions. The transformation of the material from non-ergodic to an ergodic realxor ferroelectric phase with increasing NN content was revealed, which facilitated the enhanced recoverable energy (Wrec) and efficiency (ƞ). Specifically, 0.92(0.7BNT-0.3ST)-0.08NN ceramic showed a high value of Wrec ~2.49 J/cm3 with the ƞ ~ 85% at a low electric-field of 170 kV/cm and retained a small variation (±11%) in Wrec and the high ƞ (>84%) over a broad temperature range (0 °C–120 °C). The remarkable performance of 0.92(0.7BNT-0.3ST)-0.08NN ceramic making it promising for applications of low electric-field and temperature-stable energy storage capacitors.

In the era of big data, practical applications in various domains continually generate large-scale time-series data . Among them, some data show significant or potential periodicity characteristics, such as meteorological and financial data . It is critical to efficiently identify the potential periodic patterns from massive time-series data and provide accurate predictions. In this paper, a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for large-scale time-series data is proposed and implemented in the Apache Spark cloud computing environment. To effectively handle the massive historical datasets, a Time Series Data Compression and Abstraction (TSDCA) algorithm is presented, which can reduce the data scale as well as accurately extracting the characteristics. Based on this, we propose a multi-layer time series periodic pattern recognition (MTSPPR) algorithm using the Fourier Spectrum Analysis (FSA) method. In addition, a Periodicity-based Time Series Prediction (PTSP) algorithm is proposed. Data in the subsequent period are predicted based on all previous period models, in which a time attenuation factor is introduced to control the impact of different periods on the prediction results. Moreover, to improve the performance of the proposed algorithms, we propose a parallel solution on the Apache Spark platform, using the Streaming real-time computing module. To efficiently process the large-scale time-series datasets in distributed computing environments , Distributed Streams (DStreams) and Resilient Distributed Datasets (RDDs) are used to store and calculate these datasets. Logical and data dependencies of RDDs in the P-TSDCA, P-MTSPPR, and P-PTSP processes are considered, and the corresponding parallel execution solutions are conducted. Extensive experimental results show that our PPTSP algorithm has significant advantages compared with other algorithms in terms of prediction accuracy and performance.

Robustness is one of the key properties in complex networks to ensure the expected level of performance and service availability in case of perturbations and failures. Network robustness is generally quantified using various classical metrics. However, whether the robustness quantification of the networks in various types of failures can be proved to be valid or not? Moreover, how does the hierarchy of a network impacts the robustness, is still not a well-explored domain. This paper presents the robustness quantification of hierarchical complex networks under targeted attacks. We analyze ten different real-world networks with varying graph characteristics using the classical robustness metrics. The level of the hierarchy of the considered networks is computed using the Global Reaching Centrality (GRC) measure. To depict the targeted attacks, we remove (decommission) specific network nodes based on the nodal degree and node betweenness centrality. Moreover, to compare various networks with varying size and characteristics, we employ deterioration strategy to evaluate the effect of the failures on hierarchical networks. Our results reveal a strong relationship between hierarchy and robustness of the networks. Moreover, the presented results reveal that the robustness inferences based on the classical robustness measures may be inaccurate. It can be inferred from the analysis that the classical robustness metrics may not be able to quantify the structural robustness of hierarchical complex networks appropriately, which lay down a need for new robustness metrics for robustness quantification. This publication was made possible by NPRP grant # [ 8-519-1-108 ] from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the author[ s ]. Scopus

Video streaming is one of the most prevailing and bandwidth consuming Internet applications today. Advancements in technology and prevalence of video capturing devices result in massive multi-sourced (aka crowdsourced) live video broadcasting over the Internet. A single scene may be captured by multiple spectators from different angles (views), enabling an opportunity for interactive multiview video by integrating these individually captured views. Such multi-sourced multiview video offers more realistic and immersive experience of a scene. In this paper, we present a Quality of Experience (QoE) driven, cost effective Crowdsourced Multiview Live Streaming (CMLS) system. The CMLS aims to minimize the overall system cost by selecting optimal cloud site for video transcoding and the number of representations, based on the view popularity and viewer's available bandwidth. In addition, we present a QoE metric considering delay and received video quality. We formulate the selection of optimal cloud site and number of representations to meet the required QoE as a resource allocation problem using Integer Programming (IP). Moreover, we present a Greedy Minimal Cost (GMC) algorithm to perform resource allocation efficiently. We use real live video traces collected from three large-scale live video providers (Twitch.tv, YouTube Live, and YouNow) to evaluate our proposed strategy. We evaluate the GMC algorithm considering the overall cost, QoE, video quality, and average latency between viewers and transcoding location. We compare our results with the optimal solution and the state-of-the art policy used in a popular video steaming system. Our results demonstrate that the GMC achieves near optimal results and substantially outperforms the state-of-the art policy. This publication was made possible by NPRP grant # [ 8-519-1-108 ] from the Qatar National Research Fund (a member of Qatar Foundation). We are thankful to the Denny Stohr for providing YouNow dataset. The findings achieved herein are solely the responsibility of the author[s]. Scopus

This paper gives a detailed description of the design and synthesis of some organic and inorganic sensitizers for DSSC’s. Five organic and inorganic dyes were used as sensitizers to fabricate DSSC’s. Fresh extracts of different organic and inorganic materials were used as sensitizers in the DSSC’s. The organic and inorganic dyes which are used are Beet Root, Fig, Ruthenium, Ruthenium Chloride and Blue Dye. The photo-electrochemical measurements for these DSSC’s indicate the variation in open circuit voltage (VOC) from 400 mV to 621 mV, and that in short circuit current density (JSC) ranges from 1.312 mA/cm2 to 12 mA/cm2. Ruthenium sensitizer shows maximum value of VOC (621 mV). The photo-to-electric conversion efficiency of Ruthenium based DSSC is found to be 3.86%.

Advancements in smart devices, wearable gadgets, sensors, and communication paradigm have enabled the vision of smart cities, pervasive healthcare, augmented reality and interactive multimedia, Internet of Every Thing (IoE), and cognitive assistance, to name a few. All of these visions have one thing in common, i.e., delay sensitivity and instant response. Various new technologies designed to work at the edge of the network, such as fog computing, cloudlets, mobile edge computing, and micro data centers have emerged in the near past. We use the name ``edge computing'' for this set of emerging technologies. Edge computing is a promising paradigm to offer the required computation and storage resources with minimal delays because of ``being near'' to the users or terminal devices. Edge computing aims to bring cloud resources and services at the edge of the network, as a middle layer between end user and cloud data centers, to offer prompt service response with minimal delay. Two major aims of edge computing can be denoted as: (a) minimize response delay by servicing the users’ request at the network edge instead of servicing it at far located cloud data centers, and (b) minimize downward and upward traffic volumes in the network core. Minimization of network core traffic inherently brings energy efficiency and data cost reductions. Downward network traffic can be minimized by servicing set of users at network edge instead of service provider's data centers (e.g., multimedia and shared data) Content Delivery Networks (CDNs), and upward traffic can be minimized by processing and filtering raw data (e.g., sensors monitored data) and uploading the processed information to cloud. This survey presents a detailed overview of potentials, trends, and challenges of edge computing. The survey illustrates a list of most significant applications and potentials in the area of edge computing. State of the art literature on edge computing domain is included in the survey to guide readers towards the current trends and future opportunities in the area of edge computing. This publication was made possible by NPRP grant # [ 8-519-1-108 ] from the Qatar National Research Fund (a member of Qatar Foundation). Samee U. Khan's work was supported by (while serving at) the National Science Foundation . The findings achieved herein are solely the responsibility of the author[s]. Scopus

Edge networking is a complex and dynamic computing paradigm that aims to push cloud re-sources closer to the end user improving responsiveness and reducing backhaul traffic. User mobility, preferences, and content popularity are the dominant dynamic features of edge networks. Temporal and social features of content, such as the number of views and likes are leveraged to estimate the popularity of content from a global perspective. However, such estimates should not be mapped to an edge network with particular social and geographic characteristics. In next generation edge networks, i.e., 5G and beyond 5G, machine learning techniques can be applied to predict content popularity based on user preferences, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network. This article investigates the application of machine learning techniques for in-network caching in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (method, objective, and features), (b) caching strategy (policy, location, and replacement), and (c) edge network (type and delivery strategy). A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning in edge networks.

Cloud computing services have gained tremendous popularity and widespread adoption due to their flexible and on-demand nature. Cloud computing services are hosted in Cloud Data Centers (CDC) that deploy thousands of computation, storage, and communication devices leading to high energy utilization and carbon emissions. Renewable energy resources replace fossil fuels based grid energy to effectively reduce carbon emissions of CDCs. Moreover, waste heat generated from electronic components can be utilized in absorption based cooling systems to offset cooling costs of data centers. However, data centers need to be located at ideal geographical locations to reap benefits of renewable energy and waste heat recovery options. Modular Data Centers (MDC) can enable energy as a location paradigm due to their shippable nature. Moreover, workload can be transferred between intelligently placed geographically dispersed data centers to utilize renewable energy available elsewhere with virtual machine migration techniques. However, adoption of aforementioned sustainability techniques and technologies opens new challenges, such as, intermittency of power supply from renewable resources and higher capital costs. In this paper, we examine sustainable CDCs from various aspects to survey the enabling techniques and technologies. We present case studies from both academia and industry that demonstrate favorable results for sustainability measures in CDCs. Moreover, we discuss state-of-the-art research in sustainable CDCs. Furthermore, we debate the integration challenges and open research issues to sustainable CDCs.

The phonon variations, photoluminescence emission and electrical properties were systematically studied in Er3+ doped Bi3YO6 (Bi3Y1−xErxO6) oxide-ion conductors. The unit cell parameters (a and V) undergo a first increase and subsequent decrease trend with x, which is attributed to the fluctuations of the O occupation at 48i site. Five fundamental and new phonon mode splitting by Er3+ were observed by reflective FT-IR Reflectance spectroscopy, suggesting the locally structural distortion associated with the Er3+ octahedral occupancy. The Bi3Y1−xErxO6 ceramics of x = 0.2 exhibits the most intensive photoluminescence emission and a smallest activation energy of 1.1 eV. The high oxide-ion conductivity (>0.02 S/cm at 600 °C) was achieved, which can rival the most studied Yttria-stabilized zirconia (YSZ) system. The conductivities for all the Bi3Y1−xErxO6 ceramics present a curvature above 550 °C and a sudden decrease at x = 0.4. These changes in the conductivity were described intern of lattice vibrations for given oxide-ion systems.

The recent concept of "Health Insurance Marketplace" introduced to facilitate the purchase of health insurance by comparing different insurance plans in terms of price, coverage benefits, and quality designates a key role to the health insurance providers. Currently, the web based tools available to search for health insurance plans are deficient in offering personalized recommendations based on the coverage benefits and cost. Therefore, anticipating the users' needs we propose a cloud based framework that offers personalized recommendations about the health insurance plans. We use the Multi-attribute Utility Theory (MAUT) to help users compare different health insurance plans based on coverage and cost criteria, such as: (a) premium, (b) co-pay, (c) deductibles, (d) co-insurance, and (e) maximum benefit offered by a plan. To overcome the issues arising possibly due to the heterogeneous data formats and different plan representations across the providers, we present a standardized representation for the health insurance plans. The plan information of each of the providers is retrieved using the Data as a Service (DaaS). The framework is implemented as Software as a Service (SaaS) to offer customized recommendations by applying a ranking technique for the identified plans according to the user specified criteria. We present a cloud based health insurance plan recommendation system.We propose a standard ontological representation for all the health insurance plans.An algorithm to determine the similarities between the user requirements and plans is presented.We propose a ranking technique based on the Multi-attribute Utility Theory (MAUT).

Data centers are growing exponentially (in number and size) to accommodate the escalating user and application demands. Likewise, the concerns about the environmental impacts, energy needs, and electricity cost of data centers are also growing. Network infrastructure being the communication backbone of the data center plays a pivotal role in the data center’s scalability, performance, energy consumption, and cost. Research community is endeavoring hard to overcome the challenges faced by the legacy Data Center Networks (DCNs). Serious efforts have been made to handle the problems in various DCN areas. This survey presents significant insights to the state-of-the-art research conducted pertaining to the DCN domain along with a detailed discussion of the energy efficiency aspects of the DCNs. The authors explored: (a) DCN architectures (electrical, optical, and hybrid), (b) network traffic management and characterization, (c) DCN performance monitoring, (d) network-aware resource allocation, (e) DCN experimentation techniques, and (f) energy efficiency. The survey presents an overview of the ongoing research in the broad domain of DCNs and highlights the challenges faced by the DCN research community.

Opportunistic routing is an emerging research area in Wireless Mesh Networks (WMNs), that exploits the broadcast nature of wireless networks to find the optimal routing solution that maximizes throughput and minimizes packet loss. Opportunistic routing protocols mainly suffer from computational overheads, as most of the protocols try to find the best next forwarding node. In this paper we address the key issue of computational overhead by designing new routing technique without using pre-selected list of potential forwarders. We propose a novel opportunistic routing technique named, Coordinated Opportunistic Routing Protocol for WMNs (CORP-M). We compare CORP-M with well-known protocols, such as AODV, OLSR, and ROMER based on throughput, delivery ratio, and average end-to-end delay. Simulation results show that CORP-M, gives average throughput increase upto 32%, and increase in delivery ratio (from 10% to 20%). We also analyze the performance of CORP-M and ROMER based on various parameters, such as duplicate transmissions and network collisions, by analysis depicts that CORP-M reduces duplicate transmissions upto 70% and network collisions upto 30%.