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Gamification refers to the use of rules and game design techniques to involve and motivate people to achieve their goals. This work proposes an application development architecture that can generate game applications with gamification techniques. As proof of concept, the authors introduce Zeus, a platform for developing ruled-based serious game applications with gamification techniques. Zeus aims at generating gamified applications that can meet the learning goals set by users. These goals will be reflected through both learning and game attributes that users can personally select. To assess the functionality of the authors tool, they conduct a qualitative evaluation of four rule-based serious game applications developed with Zeus to help students learn about arithmetic. They make use of the Fun Toolkit to perform this evaluation in terms of the delivery of both fun and learning experiences. Their findings are encouraging in the context of learning basic arithmetic operations with Zeus game applications.

This paper presents a structural equation model that relates knowledge coordination with access to information in the process of implementing Six Sigma and their impact on the quality and economic benefits obtained. The model integrates four latent variables (knowledge coordination and access to information as independent variables; quality benefits and economic benefits as dependent variables), that are intertwined by five hypotheses validated statistically through the partial least squares technique using data from 301 responses to a survey applied in the maquiladora industry. Findings suggest that to obtain benefits associated with product quality, information and knowledge acquired from Six Sigma, projects must be carefully saved, managed, and analysed with appropriate statistical techniques applied by green and black belts. However, to obtain economic benefits, the information and knowledge must be transformed into benefits associated with quality such as reduction in delivery time, reduction of customer complains and compliance with standards demanded by the customer.

This paper reports a structural equation model to relate three critical success factors for total quality management (TQM) (i.e. managerial commitment, role of quality department, and quality policies) with customer satisfaction benefits through six hypotheses, which are statistically tested with information from 398 responses to a survey applied to Mexican manufacturing industry and using partial least squares technique integrated in WarpPLS v.6 software. The paper also reports a sensitivity analysis based on conditional probabilities for analyze low and high scenarios. Findings indicate that managerial commitment is the most important variable to ensure TQM, yet it depends on the role of the quality department for deploy quality policies and guarantee customer satisfaction. Similarly, sensibility analysis demonstrate that high levels of managerial commitment always guarantee a high performance in quality departments and good quality policies, thereby contributing to customer satisfaction. From this perspective, there are statistical evidence to declare that managers and operators are the main facilitators of TQM success.

New environmental tendencies have been incorporated into supply chains, and suppliers are playing an important role. However, the main problem when selecting a green supplier is determining the attributes that contribute to a green supply chain. Moreover, a lot of companies ignore the benefits gained from green suppliers on economic performance and marketing. Therefore, this chapter reports a structural equation model with three latent variables (Green attributes of suppliers, Marketing benefits, and Manufacturing processes benefits) and three hypotheses are proposed with relationship among them. The model is validated with information from 253 questionnaires administered to Mexican maquiladoras and evaluated using partial least squares. Findings demonstrate that maquiladoras that consider Green attributes in supplier selection process are gaining marketing and economic benefits, contributing to a green supply chain, and improving the corporate image as socially responsible organizations.

Background Usability is an essential characteristic of software development, which needs to be evaluated mainly through questionnaires. However, the questionnaires available in Spanish are limited and not available for most software's developers and users. Objectives This work aimed to design and validate a new questionnaire to evaluate software usability, considering the characteristics of efficiency, effectiveness, and satisfaction as defined by the International Standards Organization. Methods The Software Usability Assessment Questionnaire or SUAQ in Spanish language was designed and validated using Kendall's coefficient, Cronbach's Alpha, Factorial Analysis and Scree test. Results The questionnaire's validation was carried out by five experts through an evaluation of the clarity, coherence, and relevance of the proposed items. Once the questionnaire was administered to 95 participants, an overall reliability value of Cronbach's alpha of 0.839 was obtained. The factorial analysis is viable since a value of 0.757 was found in the Kaiser-Meyer-Olkin (KMO) test, and a significant value was obtained in Bartlett's sphericity test. Conclusions The proposed questionnaire proved to be valid and reliable, and therefore, suitable to evaluate software usability in the Spanish language.

This chapter reports a structural equation model which relate four latent variables associated with employee performance, knowledge transfer, supply chain complexity and flexibility, which incorporate 17 observed variables. The latent variables are related through 6 hypotheses that are tested with data gather from 269 responses to a questionnaire applied to the Mexican maquiladora industry in Mexico. The model is executed using the partial least squares technique to analyze the direct, indirect, and total effects. In the same way, the probabilities of occurrence are obtained in an independent manner, jointly and conditionally for the latent variables analyzed at their high and low levels for each of the hypotheses. Findings indicate that the external knowledge transfer is crucial within the supply chains, since it explains 44.6% of its complexity, 19.5% of the employees’ performance, as well as 10.6% of the supply chain flexibility.

Time series emerge in various applications such as financial data and production data, however, most of the generated data exhibit nonlinear inter-dependency between samples and noise, making necessary the development of methods capable of handling such nonlinearities and other abnormalities. In this paper we present an architecture for prediction of time series embedded in noise. The proposed architecture combines a convolutional and long short term memory (LSTM) layers into a structure similar to an analysis filterbank of two channels. The first element of each channel is a convolutional layer followed by a LSTM, which is able to find temporal dependencies of the signal. Finally the channels are summed to obtain a prediction. We found that the frequency response of the filters resemble a complementary filter bank response, with each channel having a maximum at different bands which could suggest that it characterizes the incoming signal in frequency. Comparisons with other methods demonstrate that the proposed method offer much better results in terms of different error measures.

The paradigm of the manufacturing systems was broken in 1980 with the beginning of the Additive Manufacturing (AM). This technology has been considered as the complement of the classic manufacturing technology, where the material is removed from a raw material until getting the final product. The addition of material in layers have been considered the new alternative to face the impact in the environment, the economy of materials and process, and the opportunity to generate new complex shapes limited by the classic manufacturing technology. The present chapter exposes the advances of the Fused Deposition Modeling (FDM), one of the seven technologies of AM which is mostly used during the past three decades. In this field, different adaptations and investigations of the technology have been focused on the increment of the capacity of the production system and improve the quality generated by this technique. The methodology used to determine the advance of AM was to employ a Systematic Literature Review using databases. The search was developed considering the keywords of AM for the construction of specific search syntax of documents associated with this technology. The documents obtained were analyzed to identify the progress in this technology. The results present the advancements of the FDM as a technology that change the industrial processing to customize the process, where the globalization makes possible to have this technology available at each desk.

Positron Emission Tomography (PET) is a technique of nuclear medicine used to obtain metabolic images of the body. PET scanners are used in research, treatment and monitoring of several diseases by providing images of metabolic activity associated with the ailments. However, data produced by PET is heavily corrupted by noise and other sources of errors causing a degradation in the quality of final reconstructed images. In order to improve the image reconstruction process a new reconstruction algorithm which addresses the problem from a variational perspective, is presented in this paper. We proposed to use a modified version of the Total Variation regularization by including a second term in order to deal better with the noise, also we propose to balance both regularizing terms by calculating weighs adapted to the PET images trough the use of anatomical information from another medical modality such as computer tomography (CT) or magnetic resonance imaging (MRI). Results on simulated images show that our proposed method is more effective in dealing with the heavy noise and preserving small structures, like possible lesions than the expectation maximization method, commonly used in commercial scanners.

This chapter discusses some of the most common supply chain evaluation methodologies in the industrial context. The chapter first addresses the use of multivariate techniques; then, we discuss the goal and the characteristics of the methods employed in this research, which include linear regression and factor analysis. The goal of this chapter is to better understand Chap. 9, which provides a comprehensive description of the methodology followed in this book to explore the impact of a series of critical success factors on supply chain performance, namely supply chain competitiveness.

This chapter discusses the exploratory analysis performed on the data collected in this research and used to develop the models that assess supply chain performance. Similarly, we discuss the aspects used to measure supply chain risks, manufacturing practices, and regional impact factors. The survey validation procedure is then discussed as well as the characteristics of the sample. Finally, we address each questionnaire construct to contribute to the understanding of the survey and its results.

Nowadays, buying a computer for family use is a frequent practice, yet the wide variety of equipment that markets offer can be overwhelming. Each computer has its own characteristics and attributes, and some of such attributes—especially qualitative features—may be difficult to assess. This chapter presents a theoretical framework that allows families to evaluate computers from a multi-attribute perspective by using two techniques: the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The former is used to weight the attributes, whereas the latter is used to propose a solution. A case study is presented to illustrate the computer selection process performed by a four-member family on four alternatives by taking into account four quantitative attributes—cost, processor speed, RAM, and hard drive capacity—and two qualitative attributes—brand prestige and after-sales service. Our findings demonstrate that our AHP-TOPSIS approach is friendly to users, especially to non-expert users, since they can perform the evaluation process on their own.

The evaluation and selection process of agricultural technology traditionally is focused on economic aspects, delegating to third parties and ignoring several attributes in the analysis. This article presents a multicriteria model that integrates the TOPSIS technique, based on a similarity index to an ideal alternative performed by a decision group, which integrates simultaneously several attributes in the analysis and having different preference levels, thus the farmers can carry out the evaluation process by themselves. The model is validated through a case study applied to the selection of an agricultural tractor, which is evaluated by four members of an agricultural cooperative, including two types of attributes. On the one hand, tangible attributes: initial cost, maintenance cost and engine power. On the other hand, intangible attributes: after-sales service and maintainability. Currently, the model is being integrated in a software to facilitate applications by farmers, avoiding assigning this task to third parties.