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The purpose of this paper is to directly link information technology (IT) education with real-world phenomena.

The selected objectives are achieved by modeling line of sight (LOS) and nonline of sight (NLOS) mobile channels using corresponding distributions. Within the described experiments, students verify whether modeled generators generate random variables accordingly to the selected distribution. The results of observations are directly compared with theoretical expectations. The methodology was evaluated by students via questionnaires.

The results show that the proposed methodology can help graduate or undergraduate students better comprehend lectured material from mobile communications or mathematical statistics.

The hands on experience using the EMONA system make the approach original.

Reviews several recent developments in the computer‐based analysis of reliability data. Starting from the traditional Weibull analysis, presents a description of developments which improve and extend the range of reliability analysis. These include: a five‐parameter distribution model for multiple failure modes; a goodness of fit test for data with suspended items; and a model selection technique which identifies the preferred distribution model, taking account of the number of failures, the number of suspensions and the number of parameters estimated from the data.

Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.

With the development of deep learning-based analytical techniques, increased research has focused on fatigue data analysis methods based on deep learning, which are gaining in popularity. However, the application of deep neural networks in the material science domain is mainly inhibited by data availability. In this paper, to overcome the difficulty of multifactor fatigue life prediction with small data sets,

A multiple neural network ensemble (MNNE) is used, and an MNNE with a general and flexible explicit function is developed to accurately quantify the complicated relationships hidden in multivariable data sets. Moreover, a variational autoencoder-based data generator is trained with small sample sets to expand the size of the training data set. A comparative study involving the proposed method and traditional models is performed. In addition, a filtering rule based on the R2 score is proposed and applied in the training process of the MNNE, and this approach has a beneficial effect on the prediction accuracy and generalization ability.

A comparative study involving the proposed method and traditional models is performed. The comparative experiment confirms that the use of hybrid data can improve the accuracy and generalization ability of the deep neural network and that the MNNE outperforms support vector machines, multilayer perceptron and deep neural network models based on the goodness of fit and robustness in the small sample case.

The experimental results imply that the proposed algorithm is a sophisticated and promising multivariate method for predicting the contact fatigue life of a coating when data availability is limited.

A data generated model based on variational autoencoder was used to make up lack of data. An MNNE method was proposed to apply in the small data case of fatigue life prediction.

Downtime data have been acquired from a 1980 MW capacity coal‐fired power station. These data refer to a pulverised fuel system which comprises ten mills, each of which, for the purpose of reliability analysis, has six sets of components liable to failure. The majority voting of the mills was a seven‐out‐of‐ten system. Individual unit failure patterns are determined using a family of exponential models and are used to provide total group reliability patterns, and a computerised alogorithm for analysis of the voting system is developed. A methodology for scheduling majority vote equipment working with non‐symmetrical overhaul downtimes is presented.

Subcontractor payments typically come through the contractor, though there can be exceptions to this, and their timing and quantum can be affected by the upstream payment practices of the owner to the contractor, as well as the payment practices of the contractor. The purpose of this paper is to study the linked effect of late and incomplete payments of both the owner and contractor on what the subcontractor receives.

The paper's analysis develops on an existing Markov chain formulation of owner payments. The probability of getting payment from an owner or contractor is represented as a function of time since claim submission. Such functions are established through goodness of fit tests using actual project data. The downstream progression of payment from owner to contractor to subcontractor is treated as a collection of series and parallel systems, for which the likelihood of payment is assessed.

A model that enables subcontractors to calculate the likelihood of getting their claims paid, based on owner and contractor historical payment practices, is developed. Subcontractors are able to calculate the conditional and unconditional probabilities of their claims being paid at any time after claim submission. The model may be used with historical payment records, or with identified typical owner and contractor payment types.

The paper presents a practical method by which a subcontractor is able to calculate age‐dependent probabilities of outstanding claim amounts being paid. Such information feeds into the subcontractor's tendering practices before entering a new project, and in the subcontractor's contract administration practices in terms of pursuing claims.

The modelling of the owner‐contractor‐subcontractor payment linkage is original. No similar modelling exists in the literature.

A plethora of studies focused on the cause and solutions for the bullwhip effect, and consequently many have successfully experimented to dampen the effect. However, the feasibility of such studies and the actual contribution for supply chain performance are yet up for debate. This paper aims to fill this gap by providing a holistic system-based perspective and proposes a fuzzy logic decision-making implementation for a single-product, three-echelon and multi-period supply chain system to mitigate such effect.

This study uses system dynamics (SD) as the central modeling method for which Vensim® is used as a tool for hybrid simulation. Further, the authors used MATLAB for undertaking fuzzy logic modeling and constructing a fuzzy inference system that is later on incorporated into SD model for interaction with the main supply chain structure.

This research illustrated the usefulness of fuzzy estimations based on experts’ linguistically and logically defined parameters instead of relying merely on the traditional demand forecasting based on time series. Despite the increased complexity of the calculations and structure of the fuzzy model, the bullwhip effect has been considerably decreased resulting in an improved supply chain performance.

This dynamic modeling approach is not only useful in supply chain management but also the model developed for this study can be integrated into a corporate financial planning model. Further, this model enables optimization for an automated system in a company, where decision-makers can adjust the fuzzy variables according to various situations and inventory policies.

This study presents a systemic approach to deal with uncertainty and vagueness in dynamic models, which might be a major cause in generating the bullwhip effect. For this purpose, the combination between fuzzy set theory and system dynamics is a significant step forward.

Libraries constitute a highly developing area as they always enrich their offered services in order to satisfy users' expectations. One of the most important factors in customization strategies and improvement of individual services must be user preferences. However, customization requires an in‐depth analysis of user preferences and an evaluation of future behavior. The main objective of this paper is to present a framework for analyzing changes of user preferences in a library.

The paper presents detailed results of two independent user satisfaction surveys conducted in an interval of five years in the Library of the Technical University of Crete and analyzes them using a proposed framework. However, the framework can be applied in every modern library. The analyses are based on non‐parametric statistical techniques and a multicriteria satisfaction analysis method, which is a multicriteria preference disaggregation approach.

Overall, user judgments for both surveys show that 50‐70 percent of users are “very satisfied” or “satisfied”. The percentage of “satisfied” users increased from 2005 to 2010 (almost 18 percent) and “very satisfied” remained almost the same.

Results are mainly focused on the evaluation of potential trends of user preferences. Furthermore, results of a benchmarking analysis are also presented, based on the evolution of satisfaction levels for the quality characteristics of the offered services.

The goal of the presented study and proposed framework and methodology is to help library decision makers track changes to user preferences and improve the provided services according to those preferences.

This paper aims to explore users’ perception of, and then intention toward using, e-book devices as new reading devices, based upon an integrated technology acceptance model (TAM), reading engagement based upon flow theory and readability. The recent introduction of e-book devices has drastically changed the way people access and use reading content. However, few studies have explored the impact of reading experience on acceptance of e-book devices.

A total of 219 participants participated in a survey after using e-book devices to analyze the research model.

This study confirmed the crucial roles played by viewing experience, perceived mobility, perceived behavioral control, skill and readability. Also perceived usefulness and text satisfaction were found to have a positive and significant association with acceptance of e-book devices.

Implications and suggestions for researchers and manufacturers are also addressed in the present study.

The current study focused on how actual reading experiences using e-book devices influences acceptance of e-book devices, through the triangular integrated model of TAM, reading engagement and readability features, and investigating users’ reading experience on the basis of responses to the characteristics of e-book devices.

This research seeks to evaluate the impact of applying lean construction principles on the performance of reinforcement operations using a discrete-event simulation (DES) approach.

Process mapping of reinforcements operations was first established through field observation and interviews with construction managers involved in the selected project. Subsequently, quantitative data were gathered and then used to identify the best probabilistic density functions for each activity duration based on the fit-quality tests. Upon testing the validity of the real-world model, a lean simulation model was developed, using ARENA software, to investigate the impact of lean construction principles on the performance of such processes.

Lean principles are effective in enhancing the performance of the selected construction process. Output performance measurements for real-world model and lean model revealed that lean construction principles led to 41% improvement in process productivity, 14% enhancement in process efficiency and 17% reduction in cycle time.

The statistical findings only represent the process under study (reinforcement process) and cannot be generalized to other construction activities. In order to draw generalizable conclusions, future works are needed to extend this study to different project sizes and more complex construction processes (e.g. bricklaying process and concrete pouring operations). Moreover, there are other factors such as labor skills, rework and uncertainty, site conditions that require further analyses for leaner construction projects.

The methodology and the techniques presented in this work can be used for decision making by analyzing various lean construction scenarios and evaluating their impacts on performance outcomes of any construction process prior to real-world implementation.