Enhancing the microwave dielectric properties of Sr<Subscript>2</Subscript>Ca<Subscript>3</Subscript>Ta<Subscript>4</Subscript>TiO<Subscript>17</Subscript> ceramics by Zr Substitution

The compositions in Sr₂Ca₃Ta₄Ti_1?xZr_xO₁₇ (0?≤?x?≤?0.12) series were designed and fabricated by solid state sintering method. All the compositions formed single phases and crystallized in an orthorhombic crystal structure. Zr substitution led to the enhancing of the microwave dielectric properties by tuning the τ_f value through zero and increased the Q_uf_o value from 12,540 to 14,970 GHz with a slight decrease in ε_r. In the present study, a good combination of ε_r ~?51, Q_uf_o ~?145,43 GHz and τ_f ~ 3 ppm/°C were obtained for Sr₂Ca₃Ta₄Ti_0.90Zr_0.1O₁₇ ceramic sintered at 1575 °C for 4 h.     

Recent trends in graphene materials synthesized by CVD with various carbon precursors

Graphene is a single layer of carbon atoms arranged in an sp²-hybridized structure with properties far superior compared to other materials. Research and development in graphene synthesis have been rapidly growing the past few years, especially using chemical vapor deposition (CVD) over various types of carbon precursor. The nature and the type of carbon precursor is one important parameter of growth by CVD, especially for graphene production, since they can dramatically impact graphene growth yield and rate. However, effects of the used carbon precursor on graphene growth mechanisms are rarely discussed. In the course of large-scale and low-cost graphene preparation, this review on the recent trends regarding the utilization of diverse carbon precursors used to synthesize graphene via the CVD method is of great interest for development of improved or alternative synthesis methods. The details and the mechanisms involved in graphene synthesis using carbon precursors in the form of gaseous, liquids and solids are compared, analyzed and discussed thoroughly. In this review, we present a thorough overview on the impact and mechanisms of carbon precursors in achieving high-quality graphene with competitive edge in the near future.     

A New Nonparametric EWMA Control Chart for Monitoring Process Variability

The exponentially weighted moving average (EWMA) control chart is one of a potentially powerful process monitoring tool of the statistical process control. The EWMA chart has now been widely used because of its excellent ability to detect small to moderate shifts in the process parameter(s). In this study, we propose a new nonparametric/distribution‐free EWMA chart for efficiently monitoring the changes in the process variability. We use extensive Monte Carlo simulations to compute the run length profiles of the proposed EWMA chart. For a better performance comparison, the proposed EWMA chart is compared with a recent existing EWMA chart that has already shown to have better performance than the existing control charts. It turns out that the proposed EWMA chart performs substantially and uniformly better than the existing powerful EWMA chart. The working and implementation of the proposed and existing EWMA charts with the help of an illustrative example are also included in this study. Copyright © 2017 John Wiley & Sons, Ltd.     

Plasmonic electro-absorption modulator and polarization selector

Silicon photonics has begun to use index tunable plasmonic materials, such as graphene and indium tin oxide (ITO), because these materials can have their properties tailored by an external voltage. In this article, a compact plasmonic device, which can work as an optical mode controller and electro-absorption (EA) modulator at 1550 nm, is proposed. The performance of the device is theoretically investigated for ITO. Numerical simulations show that, with a suitable applied voltage to thin ITO layers, the proposed structure can select either only one mode (TE/TM) or both modes initially and then modulate the selected mode: a modulation depth of about 30–50 dB/μm is achieved with a proper choice of the dielectric spacer material. In addition, the proposed structure performance is analysed by replacing the waveguide materials with germanium. The proposed nanoscale device, with high modulation bandwidth (f_3 dB = 700–250 GHz) and low-energy consumption (~4.0 fJ/bit), may find applications in the future integrated nanophotonics.     

Dynamical Comparison of Several Third-Order Iterative Methods for Nonlinear Equations

There are several ways that can be used to classify or compare iterative methods for nonlinear equations, for instance; order of convergence, informational efficiency, and efficiency index. In this work, we use another way, namely the basins of attraction of the method. The purpose of this study is to compare several iterative schemes for nonlinear equations. All the selected schemes are of the third-order of convergence and most of them have the same efficiency index. The comparison depends on the basins of attraction of the iterative techniques when applied on several polynomials of different degrees. As a comparison, we determine the CPU time (in seconds) needed by each scheme to obtain the basins of attraction, besides, we illustrate the area of convergence of these schemes by finding the number of convergent and divergent points in a selected range for all methods. Comparisons confirm the fact that basins of attraction differ for iterative methods of different orders, furthermore, they vary for iterative methods of the same order even if they have the same efficiency index. Consequently, this leads to the need for a new index that reflects the real efficiency of the iterative scheme instead of the commonly used efficiency index.     

Simulation,Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches

Artificial intelligence (AI) is expanding its roots in medical diagnostics. Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications. Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure. High blood pressure, diabetes mellitus, and glomerulonephritis are the root causes of kidney disease. Therefore, the present study is proposed a set of multiple techniques such as simulation, modeling, and optimization of intelligent kidney disease prediction (SMOIKD) which is based on computational intelligence approaches. Initially, seven parameters were used for the fuzzy logic system (FLS), and then twenty-five different attributes of the kidney dataset were used for the artificial neural network (ANN) and deep extreme machine learning (DEML). The expert system was proposed with the assistance of medical experts. For the quick and accurate evaluation of the proposed system, Matlab version 2019 was used. The proposed SMOIKD-FLS-ANN-DEML expert system has shown 94.16% accuracy. Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.     

Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach

Congestion control is one of the main obstacles in cyberspace traffic. Overcrowding in internet traffic may cause several problems; such as high packet hold-up, high packet dropping, and low packet output. In the course of data transmission for various applications in the Internet of things, such problems are usually generated relative to the input. To tackle such problems, this paper presents an analytical model using an optimized Random Early Detection (RED) algorithm-based approach for internet traffic management. The validity of the proposed model is checked through extensive simulation-based experiments. An analysis is observed for different functions on internet traffic. Four performance metrics are taken into consideration, namely, the possibility of packet loss, throughput, mean queue length and mean queue delay. Three sets of experiments are observed with varying simulation results. The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.     

Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud Computing

Cloud computing has gained significant use over the last decade due to its several benefits, including cost savings associated with setup, deployments, delivery, physical resource sharing across virtual machines, and availability of on-demand cloud services. However, in addition to usual threats in almost every computing environment, cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm. Furthermore, since there are a growing number of attacks directed at cloud environments (including dictionary attacks, replay code attacks, denial of service attacks, rootkit attacks, code injection attacks, etc.), customers require additional assurances before adopting cloud services. Moreover, the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches. In this study, the model based on the root of trust for continuous integration and continuous deployment is proposed, instead of only relying on a single sign-on authentication method that typically uses only id and password. The underlying study opted hardware security module by utilizing the Trusted Platform Module (TPM), which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers. The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.     

Protected Fair Secret Sharing Based Bivariate Asymmetric Polynomials in Satellite Network

Verifiable secret sharing mainly solves the cheating behavior between malicious participants and the ground control center in the satellite network. The verification stage can verify the effectiveness of secret shares issued by the ground control center to each participant and verify the effectiveness of secret shares shown by participants. We use a lot of difficult assumptions based on mathematical problems in the verification stage, such as solving the difficult problem of the discrete logarithm, large integer prime factorization, and so on. Compared with other verifiable secret sharing schemes designed for difficult problems under the same security, the verifiable secret sharing scheme based on the Elliptic Curve Cryptography (ECC) system has the advantages of less computational overhead and shorter key. At present, the binary polynomial is a single secret scheme and cannot provide effective verification. Therefore, based on a Protected Verifiable Synchronous Multi Secret Sharing (PVS-MSS) scheme, this paper is designed based on bivariate asymmetric polynomials. The advanced verifiable attribute is introduced into the Protected Secret Sharing (PSS) scheme. This paper extends the protected synchronous multi-secret sharing scheme based on bivariate polynomial design. The ECC system constructs the security channel between the ground control center and participants and constructs the verification algorithm. Through the verification algorithm, any participant can verify the consistency and effectiveness of the secret shadow and secret share received from other participants or presented by the secret distribution center. Therefore, no additional key agreement protocol is required; participants do not need to negotiate the session key for encryption; the secret share polynomial can generate the session key between participants and speed up the secret reconstruction process. The verification stage has lower computational complexity than the verifiable scheme constructed by Rivest Shamir Adleman (RSA) and other encryption methods. Chinese Remainder Theorem (CRT) is used to update the secret shadow. The secret shadow does not need to be updated with the change of the scheme shared secret, and the public value update efficiency is higher. Reduce the complexity of sharing secret updates in a synchronous multi-secret sharing scheme.     

Improving Routine Immunization Coverage Through Optimally Designed Predictive Models

Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.