Conventional tests with written information used for the evaluation of sign language (SL) comprehension introduce distortions due to the translation process. This fact affects the results and conclusions drawn and, for that reason, it is necessary to design and implement the same language interpreter-independent evaluation tools. Novel web technologies facilitate the design of web interfaces that support online, multiple-choice questionnaires, while exploiting the storage of tracking data as a source of information about user interaction. This paper proposes an online, multiple-choice sign language questionnaire based on an intuitive methodology. It helps users to complete tests and automatically generates accurate, statistical results using the information and data obtained in the process. The proposed system presents SL videos and enables user interaction, fulfilling the requirements that SL interpretation is not able to cover. The questionnaire feeds a remote database with the user answers and powers the automatic creation of data for analytics. Several metrics, including time elapsed, are used to assess the usability of the SL questionnaire, defining the goals of the predictive models. These predictions are based on machine learning models, with the demographic data of the user as features for estimating the usability of the system. This questionnaire reduces costs and time in terms of interpreter dedication, as well as widening the amount of data collected while employing user native language. The validity of this tool was demonstrated in two different use cases.

This study investigates the use of similarities in the form of analogy, metaphor, and simile by students and reviewers in an undergraduate architectural design review. In contrast to studies conducted in vitro settings, this study emphasizes the importance of studying analogies, metaphors, and similes in a natural setting. All similarity relationships were coded according to their type, the level of expertise, range, frequency, goal, value judgment, and depth. The results indicate that analogies, metaphors, and similes were used spontaneously and without any difficulty by both reviewers and students. Reviewers, however, were almost twice as likely to evoke similarities. Metaphor was the most frequently used similarity relationship among the three. It was found that there was a significant relationship between the level of expertise and type of similarity, with students more likely to use analogies and less likely to use similes. It was also found that goal is the most important factor, with a significant relation to all other variables, and that embodiment is often invoked in both students’ and reviewers’ metaphors. We conclude that design education should take full advantage of students’ natural ability to benefit from similarity relationships.

In the current work, the effects of design (groove depth and groove width) and operational (temperature and velocity) parameters on aerodynamic performance parameters (coefficient of drag and coefficient of lift) of an isolated passenger car tire have been investigated. The study is conducted by using neural network-based Monte-Carlo analysis on computational fluid dynamics (CFD). The computer experiments are designed to obtain the causal relationship between tire design, operational, and aerodynamic performance parameters. The Reynolds-averaged Navier–Stokes equations-based Realizable K-ε model has been employed to analyze the variations in flow patterns around an isolated tire. The design parameters are varied over wide range and full factorial design, while considering temperature and velocity is completely explored to draw conclusive results. The multi-layer perceptron type neural network with the back-propagation algorithm is trained to map any non-linearity in causal relationships. The sensitivity analysis is performed to find the relationship between control variables and performance indicators. The importance of control variable is determined by both sensitivity and significance analyses and the paired interaction analysis is performed between selected control variables to find the interactive behavior of corresponding variables. The design parameter of groove width with 6.8% and 41% reduction in drag and lift coefficient, respectively, and conventionally overlooked operational parameter of velocity with 4% and 35% impact on drag and lift coefficient, respectively, are found to be the most significant variables. The air trapped between the longitudinal grooves and the road is found to follow the beam theory. The interaction of the groove depth and width is found to be significant with respect to coefficient of lift based on the air beam concept. The interaction of groove width and velocity is found to be significant with respect to both coefficients of lifts and drag.

Community of Practice (CoP) efficiency evaluation is a great deal in research. Indeed, having the possibility to know if a given CoP is successful or not is essential to better manage it over time. The existing approaches for efficiency evaluation are difficult and time-consuming to put into action on real CoPs. They require either to evaluate subjective constructs making the analysis unreliable, either to work out a knowledge interaction matrix that is difficult to set up. However, these approaches build their evaluation on the fact that a CoP is successful if knowledge is exchanged between the members. It is the case if there are some interactions between the actors involved in the CoP. Therefore, we propose to analyze these interactions through the exchanges of emails thanks to Natural Language Processing. Our approach is systematic and semi-automated. It requires the e-mails exchanged and the definition of the speech-acts that will be retrieved. We apply it on a real project-based CoP: the SEPOLBE research project that involves different expertise fields. It allows us to identify the CoP core group and to emphasize learning processes between members with different backgrounds (Microbiology, Electrochemistry and Civil engineering).

Parkinson's disease and Alzheimer's disease are progressive nervous system disorders that affect physical and cognitive capacities of individuals, including memory loss, motion impairment, or problem-solving dysfunctions. Leisure activities are associated with reducing the risk of dementia and are preventive policies for delaying the cognitive impairment in later stages of those neurodegenerative diseases. Electronic games related to cognitive abilities are an easy and inexpensive alternative for stimulating brain activity in this kind of patients. The previous research demonstrated the acceptance of these activities in the environment of Connected TV when playing at home and in daily care centers. Interaction in Connected TV applications has its own particularities that influence the design of the interface, including the viewing distance, the type of interaction through a remote control or other techniques, the size of the screen, or the collectiveness of consumption. Iterative testing with patients of these groups revealed how the physical characteristics and cognitive impairment of these concrete end-users affect the human–computer interaction, offering guidelines and recommendations in good practices for the Smart TV interface design. On the other hand, data analytics extracted from the interaction and evolution of the game offer important information enabling the creation of estimation prediction models about the cognitive state of the patient.

The ability to successfully conduct radical innovations is mandatory for mature industrial companies that want to remain competitive in the global market. This ability relies on several ingredients, namely: (1) the structuring of the innovation process; (2) managerial principles; (3) methodological tools; (4) the presence of a culture of innovation. This paper reports about the impact of applying the User eXperience-Fuzzy Front End (UX-FFE) model, which brings together the systemic innovation process with the social, economical, and methodological aspects on the outcomes of the innovation process. Firstly, it appears that the operational performance of the upstream innovation process relies on the quality of the social context, intrinsic to the group of co-creators, corresponding to the reported perceived experience. Secondly, the UX-FFE model application, therefore, allows optimizing the upstream innovation process performance. Indeed, we argue that the evaluation of the co-creators perceived experience brings new opportunities to optimize the operational performance of the upstream innovation process. The first part of this paper presents deeper a theoretical model, named UX-FFE, which combines a UX approach with an upstream innovation process (FFE). The main interest of this UX-FFE model is that it allows evaluating the social aspect of the upstream innovation process, which may be detrimental to the success of radical innovation projects in mature companies. The second part presents the results of previous experiments that validated the model. The results allow the design of an instrument dedicated to the evaluation of the user experience of co-creators in the ideation stage. Finally, the third part reports about the experimentation of the UX-FFE in a mature company. Results present the impact of the co-creators' experience on the performance of radical innovation projects.

Complex engineered systems design is a collaborative activity. To design a system, experts from the relevant disciplines must work together to create the best overall system from their individual components. This situation is analogous to a multiagent system in which agents solve individual parts of a larger problem in a coordinated way. Current multiagent models of design teams, however, do not capture this distributed aspect of design teams – instead either representing designers as agents which control all variables, measuring organizational outcomes instead of design outcomes, or representing different aspects of distributed design, such as negotiation. This paper presents a new model which captures the distributed nature of complex systems design by decomposing the ability to control design variables to individual computational designers acting on a problem with shared constraints. These designers are represented as a multiagent learning system which is shown to perform similarly to a centralized optimization algorithm on the same domain. When used as a model, this multiagent system is shown to perform better when the level of designer exploration is not decayed but is instead controlled based on the increase of design knowledge, suggesting that designers in multidisciplinary teams should not simply reduce the scope of design exploration over time, but should adapt based on changes in their collective knowledge of the design space. This multiagent system is further shown to produce better-performing designs when computational designers design collaboratively as opposed to independently, confirming the importance of collaboration in complex systems design.

This paper addresses the construction of digital twins (exact mirror images of real-world in cyberspace) using hidden Markov models for the futuristic manufacturing systems known as Industry 4.0. The proposed digital twin consists of two components namely model component and simulation component. The model component forms a Markov chain that encapsulates the dynamics underlying the phenomenon by using some discrete states and their transition probabilities. The simulation component recreates the phenomenon using a Monte Carlo simulation process. The efficacy of the proposed digital twin construction methodology is shown by a case study, where the digital twin of the surface roughness of a surface created by successive grinding operations is described. The developers of the cyber-physical systems will be benefitted from the outcomes of this study because these systems need the computable virtual abstractions of the manufacturing phenomena to address the issues related to the maturity index of futuristic manufacturing systems (i.e., understand, predict, decide, and adopt).

During a crisis situation, the ability of emergency department to take reliable and quick decisions is the main feature that defines the success or failure of this organization in the course of its crisis management. Decision makers spend time on identifying the decisions that will be taken for the whole of the crisis management, and on anticipating the preparation of these decisions, ensuring that they have time to properly prepare all decisions to be taken and, be able to implement them as fast as possible. However, the context and the characteristics of the crisis make the decision process complicated because there is no specific methodology to anticipate these decisions and properly manage collaboration with the other protagonists. There is also the pressure of time, a significant stress and, the emotional impact on the decision maker that lead to losing objectivity in decision making. We understand so that the right decision will be greatly facilitated and enhanced by the development of an adequate tool and process for decision-making. This tool must respect methods of the emergency department considered, and highlight the importance of experience feedback referencing to past cases, especially success and failures. We propose in this paper, software in order to handle experience feedback as a support for decision-making in crisis management “Crisis Clever System”. Several dimensions are considered in this study, from one side: organization, communication and problem-solving activities and from the other side the presentation and finding of experience feedback thanks to an analogy technique.

In this paper, we report on a data analysis process for the automated classification of mechanical components. In particular, here, we describe, how to implement a machine learning system for the automated classification of parts belonging to several sub-categories. We collect models that are typically used in the mechanical industry, and then we represent each object by a collection of features. We illustrate how to set-up a supervised multi-layer artificial neural network with an ad-hoc classification schema. We test our solution on a dataset formed by 2354 elements described by 875 features and spanned among 15 sub-categories. We state the accuracy of classification in terms of average area under ROC curves and the ability to classify 606 unknown 3D objects by similarity coefficients. Our parts’ classification system outperforms a classifier based on the Light Field Descriptor, which, as far as we know, actually represents the gold standard for the identification of most types of 3D mechanical objects.

Recently, interest in integrated assembly sequence planning (ASP) and assembly line balancing (ALB) began to pick up because of its numerous benefits, such as the larger search space that leads to better solution quality, reduced error rate in planning, and expedited product time-to-market. However, existing research is limited to the simple assembly problem that only runs one homogenous product. This paper therefore models and optimizes the integrated mixed-model ASP and ALB using Multi-objective Discrete Particle Swarm Optimization (MODPSO) concurrently. This is a new variant of the integrated assembly problem. The integrated mixed-model ASP and ALB is modeled using task-based joint precedence graph. In order to test the performance of MODPSO to optimize the integrated mixed-model ASP and ALB, an experiment using a set of 51 test problems with different difficulty levels was conducted. Besides that, MODPSO coefficient tuning was also conducted to identify the best setting so as to optimize the problem. The results from this experiment indicated that the MODPSO algorithm presents a significant improvement in term of solution quality toward Pareto optimal and demonstrates the ability to explore the extreme solutions in the mixed-model assembly optimization search space. The originality of this research is on the new variant of integrated ASP and ALB problem. This paper is the first published research to model and optimize the integrated ASP and ALB research for mixed-model assembly problem.

A ubiquitous manufacturing (UM) system is used in manufacturing for obtaining the Internet of things solutions and provides location-based manufacturing services. Human-induced uncertainty and early termination are two complications that hamper the effectiveness of an UM system based on three-dimensional (3D) printing. To resolve these complications, several solutions were considered in this study. First, fuzzy-valued parameters were defined to determine uncertainty. Subsequently, slack was derived to determine whether to restart an early terminated 3D printing process in the same 3D printing facility. Consequently, two optimization models – a fuzzy mixed-integer linear programming model and a fuzzy mixed-integer quadratic programming model – were developed in this study. Based on the two optimization models, a fuzzy 3D printing-based UM system that considers uncertainty and early termination was developed. The effectiveness of the proposed methodology was tested by conducting a regional experiment. The experimental results revealed that the proposed methodology could shorten the average cycle time by 9% and could enable 3D printing facilities to make real-time, online reprinting decisions.

Support vector machine (SVM) methods are widely used for classification and regression analysis. In many engineering applications, only one class of data is available, and then one-class SVM methods are employed. In reliability applications, the one-class data may be failure data since the data are recorded during reliability experiments when only failures occur. Different from the problems handled by existing one-class SVM methods, there is a bias constraint in the SVM model in this work and the constraint comes from the probability of failure estimated from the failure data. In this study, a new one-class SVM regression method is proposed to accommodate the bias constraint. The one class of failure data is maximally separated from a hypersphere whose radius is determined by the known probability of failure. The proposed SVM method generates regression models that directly link the states of failure modes with design variables, and this makes it possible to obtain the joint probability density of all the component states of an engineering system, resulting in a more accurate prediction of system reliability during the design stage. Three examples are given to demonstrate the effectiveness of the new one-class SVM method.

Conceptual design is a key stage of product design and has received increasing attention in recent years. However, this stage is characterized by limited information, large uncertainty, and multidisciplinary aspects. Thus, increased workload and time cost are associated with conceptual design information acquisition; sometimes, it is difficult to develop novel solutions and the feasibility of the solutions obtained according to these limited and uncertain information is difficult to guarantee. Genetics-based design (GBD) is an effective approach to develop novel solutions and improve the reuse of knowledge, which is consistent with the goal of the conceptual design process. Product-gene acquisition is the premise and basis of GBD. At present, there are few reported studies in this area; most of the existing works are constrained by the structural aspects of the acquisition process, and there are limited studies on specific implementation techniques. To explore the specific implementation technologies of product-gene acquisition, an intelligent acquisition method based on K-means clustering and mutual information-based feature selection algorithm is proposed in this paper. The product genes defined in this paper are key product information that determines the nature of the product and influences the conceptual design process. Thus, solutions obtained according to them are more feasible than that based on limited and uncertain information. An illustrative example is presented. The results show that the proposed method can achieve intelligent acquisition of product genes to a certain extent. Further, the proposed method will allow designers to quickly search for the corresponding product genes when performing similar functional design tasks.

A crisis is a complex situation, which actors have some difficulties to manage it. They are under stress to deal with problems that they cannot predict consequences. The human conditions (familial and life) and, the influence of the environment (politic, economic, media) pushes the actors to lose control of the crisis situation. The question we face in this paper is: “is it possible to predict the impact of the stress in this type of situation?” Our main hypothesis to answer is to represent experience feedback using knowledge management. To model the crisis management as systemic system emphasizing regulation loops, and the collaboration activity by showing the dimension of the communication, coordination, and cooperation. This modeling is illustrated in a terrorist attack situation in Algeria. To predict stress consequences, fuzzy set principle is adopted, based on experience feedback and situations modeling, as a generator of alternative states given a stress event.

Although, the knowledge management (KM)-culture research has helped to validate the importance of cultural values for companies' KM initiatives and provided insights into some important values, it still lacks frameworks and analysis outlining how specific types of cultural values might relate to Knowledge Management system (KMS) adoption and subsequent outcomes. In this paper, we provide a three-dimensional framework to help managers articulate how culture affects their unit's ability to create, transfer, and apply knowledge through KMS use. To illustrate the application of the framework, we also present an exploratory case study we have performed in an international organization in the area of development assistance and capacity development.

In engineering design, surrogate models are often used instead of costly computer simulations. Typically, a single surrogate model is selected based on the previous experience. We observe, based on an analysis of the published literature, that fitting an ensemble of surrogates (EoS) based on cross-validation errors is more accurate but requires more computational time. In this paper, we propose a method to build an EoS that is both accurate and less computationally expensive. In the proposed method, the EoS is a weighted average surrogate of response surface models, kriging, and radial basis functions based on overall cross-validation error. We demonstrate that created EoS is accurate than individual surrogates even when fewer data points are used, so computationally efficient with relatively insensitive predictions. We demonstrate the use of an EoS using hot rod rolling as an example. Finally, we include a rule-based template which can be used for other problems with similar requirements, for example, the computational time, required accuracy, and the size of the data.

Nowadays, the use of virtual reality/virtual acoustics (VR/VA) in archaeology for rendering lost buildings is an important topic in the cultural heritage field. Moreover, the addition of additional senses apart from the sight increases the feeling of immersion in virtual environments. The aim of this paper is to show the interaction work developed in a VA system, based on Unity and FMOD, the graphical and acoustical reconstruction of an ancient building and the development of a VR goggles with headphones to render 3D audio and video interactively. This system has been implemented to render auralizations in a binaural system and has been applied to the renderization of an old and lost theatre in València (Spain). The first building of theatre was built in the 16th century, and was rebuilt several times until the 18th century. The auralization of several theatrical excerpts of different Spanish authors of that time is also presented. The integrated system has been subjectively evaluated, obtaining very satisfactory results.