An improved fitting algorithm for parametric macromodeling from tabulated data

This paper introduces a new scheme for the identification of multivariate behavioral maeromodels from tabulated frequencydomain data. The method produces closed-form parametric expressions that reproduce with excellent accuracy the external port behavior of the structure, both as function of frequency and one or more external parmeters. The numerical robustness of the main algorithm is demonstrated on two significant examples

The relationship between fear of failure, academic motivation and student engagement in higher education::A general linear model

Failure is an overwhelming experience that is associated with hostile, negative feelings and devastating consequences for many students. However, there is little effort on theorising fear of failure in education or examining its links with academic motivation and engagement. Researchers have called for investigating how fear operates in education and for developing a broader understanding of engagement in higher education. This study addresses this gap in knowledge. It examined the factor structure of two instruments designed to measure motivation and engagement and the influence of fear of failure on motivation and engagement in light of Self Determination Theory. It investigated how fear of failure and motivation clustered within students and if these clusters were differentially associated with engagement. Finally, it examined the modulatory role of extrinsic motivation, as a differentiated construct, in the relationship between fear of failure and engagement. Data were collected using self-reported instruments and analysed using the General Linear Model. Contributions introduced fear of failure as an influential factor of motivation and uncovered its direct and indirect effects on motivation and engagement, thus extending existing literature on fear of failure. Cluster analysis identified distinct profiles of students based on their fear of failure and motivation and established a positive link between fear of failure and extrinsic motivation. This study has extended the motivation literature by shedding new light on the positive modulatory role of extrinsic motivation, as a differentiated construct, in the relationship between fear of failure and engagement. Contributions also included the introduction of a new model that extends the self-determination continuum to acknowledge the existence of different learners and recognise the role of fear of failure among them. Finally, this study provided two modified instruments to measure motivation and engagement, thus contributing to existing measurement tools in United Kingdom higher education. Contributions to practice are implied; there is a need to recognise the significant impact of fear of failure on the dynamics of the learning environment and the importance of prompting self-inflicted behaviours. Comprehending the complexity of the learning environment in light of the complex nature of human behaviours is considered essential to improving teaching and learning

Happiness: Theoretical and Empirical Considerations

TOPIC. Although happiness is important in maintaining health, few studies of happiness can be found in the nursing literature. PURPOSE. This paper explicates the concept of happiness through examination of its defining attributes, antecedents, consequences, and measurement. SOURCES OF INFORMATION. Literature review using hand search, and databases were used as sources of information. CONCLUSION. The information provided can be used in clinical practice so that nursing strategies can be developed and tested to help people to become happy and healthy

Stability, Causality, and Passivity in Electrical Interconnect Models

Modern packaging design requires extensive signal integrity simulations in order to assess the electrical performance of the system. The feasibility of such simulations is granted only when accurate and efficient models are available for all system parts and components having a significant influence on the signals. Unfortunately, model derivation is still a challenging task, despite the extensive research that has been devoted to this topic. In fact, it is a common experience that modeling or simulation tasks sometimes fail, often without a clear understanding of the main reason. This paper presents the fundamental properties of causality, stability, and passivity that electrical interconnect models must satisfy in order to be physically consistent. All basic definitions are reviewed in time domain, Laplace domain, and frequency domain, and all significant interrelations between these properties are outlined. This background material is used to interpret several common situations where either model derivation or model use in a computer-aided design environment fails dramatically.We show that the root cause for these difficulties can always be traced back to the lack of stability, causality, or passivity in the data providing the structure characterization and/or in the model itsel

Parameterized model order reduction of delayed systems using an interpolation approach with amplitude and frequency scaling coefficients

When the geometric dimensions become electrically large or signal waveform rise times decrease, time delays must be included in the modeling. We present an innovative PMOR technique for neutral delayed differential systems, which is based on an efficient and reliable combination of univariate model order reduction methods, amplitude and frequency scaling coefficients and positive interpolation schemes. It is able to provide parameterized reduced order models passive by construction over the design space of interest. Pertinent numerical examples validate the proposed PMOR approach

Multipoint model order reduction of delayed PEEC systems

We present a new model order reduction technique for electrically large systems with delay elements, which can be modeled by means of neutral delayed differential equations. An adaptive multipoint expansion and model order reduction of equivalent first order systems are combined in the new proposed method that preserves the neutral delayed differential formulation. An adaptive algorithm to select the expansion points is presented. The proposed model order reduction technique is validated by pertinent numerical results. A comparison with a previous model order reduction algorithm based on a single point expansion is performed to show the considerably improved modeling capability of the new proposed technique

Interpolation-based parameterized model order reduction of delayed systems

Three-dimensional electromagnetic methods are fundamental tools for the analysis and design of high-speed systems. These methods often generate large systems of equations, and model order reduction (MOR) methods are used to reduce such a high complexity. When the geometric dimensions become electrically large or signal waveform rise times decrease, time delays must be included in the modeling. Design space optimization and exploration are usually performed during a typical design process that consequently requires repeated simulations for different design parameter values. Efficient performing of these design activities calls for parameterized model order reduction (PMOR) methods, which are able to reduce large systems of equations with respect to frequency and other design parameters of the circuit, such as layout or substrate features. We propose a novel PMOR method for neutral delayed differential systems, which is based on an efficient and reliable combination of univariate model order reduction methods, a procedure to find scaling and frequency shifting coefficients and positive interpolation schemes. The proposed scaling and frequency shifting coefficients enhance and improve the modeling capability of standard positive interpolation schemes and allow accurate modeling of highly dynamic systems with a limited amount of initial univariate models in the design space. The proposed method is able to provide parameterized reduced order models passive by construction over the design space of interest. Pertinent numerical examples validate the proposed PMOR approach

Reduced order modeling of delayed PEEC circuits

We propose a novel model order reduction technique that is able to accurately reduce electrically large systems with delay elements, which can be described by means of neutral delayed differential equations. It is based on an adaptive multipoint expansion and model order reduction of equivalent first order systems. The neutral delayed differential formulation is preserved in the reduced model. Pertinent numerical results validate the proposed model order reduction approach