An Enhanced Source Location Privacy based on Data Dissemination in Wireless Sensor Networks (DeLP)

open access articleWireless Sensor Network is a network of large number of nodes with limited power and computational capabilities. It has the potential of event monitoring in unattended locations where there is a chance of unauthorized access. The work that is presented here identifies and addresses the problem of eavesdropping in the exposed environment of the sensor network, which makes it easy for the adversary to trace the packets to find the originator source node, hence compromising the contextual privacy. Our scheme provides an enhanced three-level security system for source location privacy. The base station is at the center of square grid of four quadrants and it is surrounded by a ring of flooding nodes, which act as a first step in confusing the adversary. The fake node is deployed in the opposite quadrant of actual source and start reporting base station. The selection of phantom node using our algorithm in another quadrant provides the third level of confusion. The results show that Dissemination in Wireless Sensor Networks (DeLP) has reduced the energy utilization by 50% percent, increased the safety period by 26%, while providing a six times more packet delivery ratio along with a further 15% decrease in the packet delivery delay as compared to the tree-based scheme. It also provides 334% more safety period than the phantom routing, while it lags behind in other parameters due to the simplicity of phantom scheme. This work illustrates the privacy protection of the source node and the designed procedure may be useful in designing more robust algorithms for location privac

The impact of co-infection of influenza A virus on the severity of Middle East Respiratory Syndrome Coronavirus

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Ho and colleagues recently drew attention to the consequences of co-infection with Influenza and HIV.1 We present four cases of combined infection with influenza and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infection. Nasopharyngeal swabs or tracheal aspirates were tested for MERS-CoV using real-time reverse-transcription polymerase chain reaction (RT-PCR).2, 3 Samples were tested for Influenza A, B and H1N1 by rapid molecular test (GenEXper for detection of flu A, B and 2009 H1N1, Cepheid)

Predicting Solar Irradiance using Time Series Neural Networks

Increasing the accuracy of prediction improves the performance of photovoltaic systems and alleviates the effects of intermittence on the systems stability. A Nonlinear Autoregressive Network with Exogenous Inputs (NARX) approach was applied to the Vichy-Rolla National Airport\u27s photovoltaic station. The proposed model uses several inputs (e.g. time, day of the year, sky cover, pressure, and wind speed) to predict hourly solar irradiance. Data obtained from the National Solar Radiation Database (NSRDB) was used to conduct simulation experiments. These simulations validate the use of the proposed model for short-term predictions. Results show that the NARX neural network notably outperformed the other models and is better than the linear regression model. The use of additional meteorological variables, particularly sky cover, can further improve the prediction performance

Modeling and Simulation of Microgrid

Complex computer systems and electric power grids share many properties of how they behave and how they are structured. A microgrid is a smaller electric grid that contains several homes, energy storage units, and distributed generators. The main idea behind microgrids is the ability to work even if the main grid is not supplying power. That is, the energy storage unit and distributed generation will supply power in that case, and if there is excess in power production from renewable energy sources, it will go to the energy storage unit. Therefore, the electric grid becomes decentralized in terms of control and production. To deal with this change, one needs to interpret the electrical grid as a system of systems (SoS) and build new models that capture the dynamic behavior of the microgrid. In this paper, different models of electric components in a microgrid are presented. These models use complex system modeling techniques such as agent-based methods and system dynamics, or a combination of different methods to represent various electric elements. Examples show the simulation of the solar microgrid is presented to show the emergent properties of the interconnected system. Results and waveforms are discussed

Identifying Characteristics of a “Good School” in the British and Saudi Arabian Education Systems

This study aims at establishing whether primary schools in the Saudi education system conform to the characteristics of what are referred to as ‘good schools’ in the British education system. The findings established through this study show that only 43.75% of primary schools in Saudi conform to the characteristics of what are referred to as ‘good schools’ in Britain.  Moreover, it is established that there are more similarities than differences in the roles played by headteachers in these two education systems when it comes to fostering effective schools and developing schools as learning organisations. Nevertheless, it is established that there are more headteachers in British primary schools than in Saudi primary schools who take up roles geared towards fostering effective schools and developing schools as learning organisations. This disparity has been attributed to the fact that in the Saudi education system the role of headteachers is highly regulated and constrained due to the bureaucratic and centralised nature of the country’s education system whereas in the British education system headteachers have more autonomy and control over school management. Generally, this study provides invaluable insights that can be used to improve the professional practice of educators. It illuminates different characteristics and roles that can contribute to the realisation of effective schools and schools as learning organisations. It also provides an explicit outlook towards school leadership in the global context. As the world is increasingly becoming globalised, it is crucial for educators to acquaint themselves with how different systems of education function

A Modified Generalized Laguerre-Gauss Collocation Method for Fractional Neutral Functional-Differential Equations on the Half-Line

The modified generalized Laguerre-Gauss collocation (MGLC) method is applied to obtain an approximate solution of fractional neutral functional-differential equations with proportional delays on the half-line. The proposed technique is based on modified generalized Laguerre polynomials and Gauss quadrature integration of such polynomials. The main advantage of the present method is to reduce the solution of fractional neutral functional-differential equations into a system of algebraic equations. Reasonable numerical results are achieved by choosing few modified generalized Laguerre-Gauss collocation points. Numerical results demonstrate the accuracy, efficiency, and versatility of the proposed method on the half-line

Solar Irradiance Forecasting using Deep Neural Networks

Predicting solar irradiance has been an important topic in renewable energy generation. Prediction improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. The irradiance can be predicted using statistical methods such as artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average (ARMA). However, they either lack accuracy because they cannot capture long-term dependency or cannot be used with big data because of the scalability. This paper presents a method to predict the solar irradiance using deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model without specifying what form the variation should take and allow the extraction of high-level features. The DRNN is used to predict the irradiance. The data utilized in this study is real data obtained from natural resources in Canada. The simulation of this method will be compared to several common methods such as support vector regression and feedforward neural networks (FNN). The results show that deep learning neural networks can outperform all other methods, as the performance tests indicate

Chaotic Behavior in High-Gain Interleaved DC-DC Converters

In this paper, chaotic behavior in high gain dc-dc converters with current mode control is explored. The dc-dc converters exhibit some chaotic behavior because they contain switches. Moreover, in power electronics (circuits with more passive elements), the dynamics become rich in nonlinearity and become difficult to capture with linear analytical models. Therefore, studying modeling approaches and analysis methods is required. Most of the high-gain dc-dc boost converters cannot be controlled with only voltage mode control due to the presence of right half plane zero that narrows down the stability region. Therefore, the need of current mode control is necessary to ensure the stability of this type of boost converter. A significant number of the work reported so far has concentrated on explaining the chaos phenomena in the language of the nonlinear dynamics literature. In addition to analyzing and studying chaotic behaviors, this presents some ideas about moving toward gainful utilization of the nonlinear properties of power electronics. Simulation and experimental studies are included to validate the theory, and results will be discussed

Cut-and-Project Tilings Constructed From Crystallographic Tilings.

An important method to construct aperiodic tilings is the method of canonical projection from higher dimensional lattices. Lattices are the orbits of special type of crystallographic groups. For example, Penrose tilings can be obtained from a lattice tiling of E5, by the cut-and project method. Developing a mathematical theory of crystallographic tilings and generalising the method of canonical projection to other crystallographic groups than lattices. Using this method one can hope to construct (interesting), completely new types of aperiodic tilings

Solutions of Some Difference Equations Systems and Periodicity

In this article, analysis and investigation have been conducted on the periodic nature as well as the type of the solutions of the subsequent schemes of rational difference equations with a nonzero real numbers initial conditions