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This paper is concerned with non-fragile robust H_∞ control problems for nonlinear networked control systems (NCSs) with time-varying delay and unknown actuator failures. The paper aims to discuss these issues.

The system parameters are allowed to have time-varying uncertainties and the actuator faults are unknown but whose upper and lower bounds are known. By using some lemmas, uncertainties can be replaces with the known values. By taking the exogenous disturbance and network transmission delay into consideration, a delay nonlinear system model is constructed.

Based on Lyapunov stability theory, linear matrix inequalities (LMIs) and free weighting matrix methods, the sufficient conditions for the existence of the non-fragile robust H_∞ controller gain are derived and which can obtained by solving the LMIs. Finally, a numerical example is provided to illustrate the effectiveness of the proposed methods.

The introduced approach is interesting for NCSs with time-varying delay and unknown actuator failures.

The purpose of this paper is with robust control problem for event-triggered networked control systems (NCSs) with actuator failures and time-varying transmission delays.

A random sequence is introduced to describe the actuator faults, and a novel event-triggering communication scheme is adopted in the sensor-to-controller channel. By taking the event-triggered mechanism and network transmission delay into consideration, a delay system model is constructed.

Based on Lyapunov stability theory and free weighting matrix method, the feasibility criteria for co-designing both the controller gain and the trigger parameters are derived. Finally, a simulation example is exploited to demonstrate the effectiveness of the proposed linear matrix inequalities (LMIs) approach.

The introduced approach is interesting for NCSs with actuator failures and time-varying transmission delays.

A significant number of inventions and innovations appeared during the time known as the Industrial Revolution. Changes in technology quickly transformed American life in the past and continue to impact businesses, communities, and schools today. Technologies like Skype™, Prezi^®, and Poll Everywhere™ provide new methods for engaging students in learning and assess their understanding of content. Each of these technologies is free and can be used in one-computer classrooms or computer labs. These technologies can enhance the learning experience by engaging students in critical thinking and collaboration, allowing real-world applications of the content, and profiling various perspectives while utilizing digital tools that captivate students’ interests. We showcase a myriad of ways that these technologies can be seamlessly integrated into a social studies unit on Industrialization.

Inclusion, state mandated achievement tests, current instructional materials and practice and the academic needs of an increasingly diverse student population have converged necessitating an in-depth review of instructional strategy research accomplished with students with mild disabilities. After confirming the value of social studies content this chapter provides context for the exploration of instructional strategies for social studies instruction by first investigating research on social studies textbooks, teacher use of these texts and some student characteristics that make using these materials difficult. Background and implications of mandated assessments and the inclusion movement is provided. A review of intervention strategy research concludes with a discussion on the implications for instruction. Suggestions on how to embed strategies into ongoing daily instruction are provided.

The authors employ single‐factor models to estimate daily temperature variations for the valuation of weather derivatives. Classical financial models are adapted to fit temperature seasonality to a time series. As an example, Monte Carlo simulations of heating and cooling degree‐days are used as the underlying for weather derivatives that reference temperatures in regions of Spain. The article also discusses potential applications to hedging energy‐related risks.

Heart diseases have become one of the most causes of death among Egyptians. With 500 deaths per 100,000 occurring annually in Egypt, it has been noticed that medical data faces a high-dimensional problem that leads to a decrease in the classification accuracy of heart data. So the purpose of this study is to improve the classification accuracy of heart disease data for helping doctors efficiently diagnose heart disease by using a hybrid classification technique.

This paper used a new approach based on the integration between dimensionality reduction techniques as multiple correspondence analysis (MCA) and principal component analysis (PCA) with fuzzy c means (FCM) then with both of multilayer perceptron (MLP) and radial basis function networks (RBFN) which separate patients into different categories based on their diagnosis results in this paper, a comparative study of the performance performed including six structures such as MLP, RBFN, MLP via FCM–MCA, MLP via FCM–PCA, RBFN via FCM–MCA and RBFN via FCM–PCA to reach to the best classifier.

The results show that the MLP via FCM–MCA classifier structure has the highest ratio of classification accuracy and has the best performance superior to other methods; and that Smoking was the most factor causing heart disease.

This paper shows the importance of integrating statistical methods in increasing the classification accuracy of heart disease data.

This study aims to propose a model to examine the relationships between e-government development, cybersecurity commitment, business usage and economic prosperity of the country.

Structural equation modeling was used to analyze the country-level variables to explain the second-order impact of e-government development through the mediating role of cybersecurity commitment.

Findings suggest that e-government development demonstrated a stronger association with cybersecurity commitment and business usage. There is preliminary evidence that the improvement of cybersecurity measures initiated by e-government development will drive business usage and improve macroeconomic conditions.

This paper has constructed a theoretical model and validated it using publicly available archival data. Further, this study hypothesizes and demonstrates empirically the direct, as well as indirect relationships between e-government development, cybersecurity commitment, business usage and economic prosperity. To summarize, the study unearths the role of a nation’s cybersecurity commitment and how it is associated with other macro parameters in a country.

As an initial step, the present study highlights the pivotal role of e-government and its positive influence on cybersecurity commitment at the country level. Further, this study also recognizes the role of cyber commitment to boost information communication and technology usage in business, the use of e-government services for the profitability of the business and effectively influence economic prosperity.