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Propagation of planar (i.e. one directional), longitudinal (i.e. uniaxial strain), steady (i.e. time‐invariant) structured shock waves within metal matrix composites (MMCs) is studied computationally. Waves of this type are typically generated during blast‐wave loading or ballistic impact and play a major role in the way blast/ballistic impact loads are introduced in, and applied to, a target structure. Hence, the knowledge of the basic physics of propagation of these waves is critical for designing structures with superior blast and impact protection capabilities. The purpose of this paper is to help advance the use of computational engineering analyses and simulations in the areas of design and application of the MMC protective structures.

To derive the overall response of the composite material to shock type loading, a dynamic‐mixture model is employed. Within this model, the known constitutive responses of the constituent materials are combined using the appropriate mixture rules. These mixture rules are of a dynamic character since they depend on the current state of the composite material and cannot be applied prior to the beginning of the analysis.

The approach is applied to a prototypical MMC consisting of an aluminum matrix and SiC particulates. Both the intermediate‐to‐strong shock regime (in which the contribution of stress deviators to the stress field can be ignored) and the weak shock regime (in which stress deviators provide a significant contribution to the stress field) are investigated. Finally, the computational results are compared with their experimental counterparts available in the open literature in order to validate the computational procedure employed.

Prediction of the spallation‐type failure in a metal‐matrix composite material (modeled using the dynamic‐mixture model) has not been done previously.

Under the development trend of intelligent manufacturing, the unstructured environment requires the robot to have a good generalization performance to adapt to the scene changes. The purpose of this paper aims to present a learning from demonstration (LfD) method (task parameterized [TP]-dynamic movement primitives [DMP]-GMR) that combines DMPs and TP-LfD to improve generalization performance and solve object manipulation tasks.

The dynamic time warping algorithm is applied to processing demonstration data to obtain a more standard learning model in the proposed method. The DMPs are used to model the basic trajectory learning model. The Gaussian mixture model is introduced to learn the force term of DMPs and solve the problem of learning from multiple demonstration trajectories. The robot can learn more local geometric features and generalize the learned model to unknown situations by adding task parameters.

An evaluation criterion based on curve similarity calculated by the Frechet distance was constructed to evaluate the model’s interpolation and extrapolation performance. The model’s generalization performance was assessed on 2D virtual data sets, and first, the results show that the proposed method has better interpolation and extrapolation performance than other methods.

The proposed model was applied to the axle-hole assembly task on real robots, and the robot’s posture in grasping and placing the axle part was taken as the task parameter of the model. The experiment results show that The proposed model is competitive with other models.

In this article, we consider the nonparametric identification of Markov dynamic games models in which each firm has its own unobserved state variable, which is persistent over time. This class of models includes most models in the Ericson and Pakes (1995) and Pakes and McGuire (1994) framework. We provide conditions under which the joint Markov equilibrium process of the firms’ observed and unobserved variables can be nonparametrically identified from data. For stationary continuous action games, we show that only three observations of the observed component are required to identify the equilibrium Markov process of the dynamic game. When agents’ choice variables are discrete, but the unobserved state variables are continuous, four observations are required.

This paper seeks to discuss a modeling tool for explaining credit‐risk contagion in credit portfolios.

Presents a “collective risk” model that models the credit risk of a portfolio, an approach typical of insurance mathematics.

ACD models are self‐exciting point processes that offer a good representation of cascading phenomena due to bankruptcies. In other words, they model how a credit event might trigger other credit events. The model herein discussed is proposed as a robust global model of the aggregate loss of a credit portfolio; only a small number of parameters are required to estimate aggregate loss.

Discusses a modeling tool for explaining credit‐risk contagion in credit portfolios.

Our paper discusses simulation-based Bayesian inference using information from previous draws to build the proposals. The aim is to produce samplers that are easy to implement, that explore the target distribution effectively, and that are computationally efficient and mix well.

Short-term motivation encompasses specific, challenging and attainable goals that develop in the limited timespan. On the other hand, long-term motivation indicates a sort of continuing commitment that is required to complete assigned task. As short-term motivational problems span for a limited period of time, such as a session, therefore, they need to be addressed in real time to keep the learner engaged in the learning process. Similarly, long-term learners’ motivation plays an equally important role to retain the learner in the long run and minimize the risk of dropout. Therefore, the purpose of this study is to incorporate a comprehensive learner motivation model that is based on short-term and long-term aspects of the learners' motivation. This approach enables Web-based educational systems to identify the real-time motivational state of the learner and provide personalized interventions to keep the learners engaged in learning process.

Recent research regarding personalized Web-based educational systems demonstrates learner’s motivation to be an essential component of the learning model. This is because of the fact that low motivation results in either students’ less engagement or complete drop out from the learning activities. A learner motivation model is considered to be a set of perceptions and beliefs that the system has developed about a learner. This includes both short-term and long-term motivations of leaners.

This study proposed a framework of a domain independent learners’ motivation model based on firm educational theories. The proposed framework consists of two modules. The primary module deals with real-time identification of motivation and logging off activities such as login, forum participation and adherence to assessment deadline. Secondary module maintains the profile of leaners associated with both short-term and long-term motivation. A study was conducted to verify the impact of learners’ motivation model and personalized interventional strategies based on proposed model, using Systematical Information Education Method assessment standards. The results show an increase in motivational index and the characteristics associated with motivation during the conducted study.

Motivational diagnosis is important for both traditional classrooms and Web-based education systems. It is one of the major elements that contribute in the success of the learning process. However, dropout rate among online students is very high, which leads to incorporate motivational elements in more personalized way because motivated students will retain the course until they successfully complete it. Hence, identifying learner’s motivation, updating learners’ motivation model based on this identification and providing personalized interventions are the key for the success of Web-based educational systems.

This chapter develops a return forecasting methodology that allows for instability in the relationship between stock returns and predictor variables, model uncertainty, and parameter estimation uncertainty. The predictive regression specification that is put forward allows for occasional structural breaks of random magnitude in the regression parameters, uncertainty about the inclusion of forecasting variables, and uncertainty about parameter values by employing Bayesian model averaging. The implications of these three sources of uncertainty and their relative importance are investigated from an active investment management perspective. It is found that the economic value of incorporating all three sources of uncertainty is considerable. A typical investor would be willing to pay up to several hundreds of basis points annually to switch from a passive buy-and-hold strategy to an active strategy based on a return forecasting model that allows for model and parameter uncertainty as well as structural breaks in the regression parameters.

With the transition into a knowledge economy, the concept of leading knowledge workers (KWs) has gained an increasing amount of attention in organisational studies and among practitioners. The emerging literature on the leadership of KW addresses an important phenomenon, but theoretical underpinnings and empirical inquiry into leadership effectiveness in a KW context do not agree on a common conceptualisation of KWs. Thus, a concerted research effort seems warranted.

The purpose of this study is to take stock of the existing literature on the leadership of KW. Based on a critical literature review, this paper provides a timely synthesis of the diffuse literature and identifies research gaps facing the leadership of KW field.

This paper suggests that the literature to date is deficient in terms of theory and evidence for how KWs are different from other classes of workers and argues that this deficiency stands in the way of developing ideas about how KWs could be effectively led.

This paper extends a discussion on establishing “KW” as a clear, independent construct and how the nomological network in which KW is situated (i.e. leadership antecedents, and workplace outcomes) may be elucidated, extended and researched.

This paper extends beyond the identified research gaps and findings to present an agenda for future research. Specifically, we propose that insights from research in educational psychology should be used as a platform for theorising about how to lead in a KW context.

The purpose of this paper is to assess the role of local labour market conditions and pupil educational attainment as primary determinants of the post-compulsory schooling decision.

Through the specification of a nested logit model, the restrictive independence of irrelevant alternatives (IIA) assumption inherent in the multinomial logit (MNL) model is relaxed across multiple unordered outcomes.

The analysis shows that the factors influencing schooling decisions differ for males and females. For females, on average, the key drivers of the schooling decision are expected wage returns based on youth educational attainment, attitudes to school and parental aspirations, rather than local labour market conditions. For males, higher local unemployment rates encourage greater investment in education.

The contribution of this paper to the existing literature is threefold. First, a nested logit model is proposed as an alternative to a MNL. The former can formally incorporate the structured and sequential decision-making process that youths may engage with in relation to the post-compulsory schooling decision, as well as relaxing the restrictive IIA assumption inherent in the MNL across multiple unordered outcomes, an issue the authors discuss in more detail in the Methodology section below. Second, the analysis is based on extremely rich socio-economic data from the Longitudinal Study of Young People in England, matched to local labour market data and administrative data from the National Pupil Database and Pupil Level Annual School Census, which provide a broad set of unusually high-quality measures of prior attainment. The authors argue that such high-quality data and an appropriate model specification allows identification of the determinants of the post-compulsory decision in a more detailed manner than many previous analyses. Third, the data have the scale necessary to consider whether the determinants of post-compulsory schooling decisions vary by gender, a particularly important issue given the differential education participation rates of males and females (e.g. in this cohort, females are about 10 percentage points more likely to go on to higher education in the UK than males), and the gendered choices of occupation (see, e.g. Bertrand, 2011). The work will, therefore, provide recent empirical evidence from England on gender differences in the determinants of education choices.

Currently, many experimental studies on the properties and behavior of rubberized concrete are available in the literature. These findings have motivated scholars to propose models for estimating some properties of rubberized concrete using traditional and advanced techniques. However, with the advancement of computational techniques and new estimation models, selecting a model that best estimates concrete's property is becoming challenging.

In this study, over 1,000 different experimental findings were obtained from the literature and used to investigate the capabilities of ten different machine learning algorithms in modeling the hardened density, compressive, splitting tensile, and flexural strengths, static and dynamic moduli, and damping ratio of rubberized concrete through adopting three different prediction approaches with respect to the inputs of the model.

In general, the study's findings have shown that XGBoosting and FFBP models result in the best performances compared to other techniques.

Previous studies have focused on the compressive strength of rubberized concrete as the main parameter to be estimated and rarely went into other characteristics of the material. In this study, the capabilities of different machine learning algorithms in predicting the properties of rubberized concrete were investigated and compared. Additionally, most of the studies adopted the direct estimation approach in which the concrete constituent materials are used as inputs to the prediction model. In contrast, this study evaluates three different prediction approaches based on the input parameters used, referred to as direct, generalized, and nondestructive methods.