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Many industries face the challenge of capturing workers' knowledge to share it, particularly tacit knowledge. The operation of complex systems such as a manufacturing line is knowledge-intensive. Considering this knowledge's breadth and dynamic nature, existing knowledge-sharing solutions are inefficient and resource intensive. Conversational user interfaces are an efficient way to convey information that mimics how humans share knowledge; however, we know little about how to design them specifically for knowledge sharing, especially regarding tacit knowledge. In this work, we present an intelligent assistant that we have developed to support the elicitation of tacit knowledge from workers through systematic reflection. The system can interact with workers by voice or text and generate visualizations of shop floor data to support reflective prompts., Internet of Things, Human-Centred Artificial Intelligence

Operating a complex and dynamic system, such as an agile manufacturing line, is a knowledge-intensive task. It imposes a steep learning curve on novice operators and prompts experienced operators to continuously discover new knowledge, share it, and retain it. In practice, training novices is resource-intensive, and the knowledge discovered by experts is not shared effectively. To tackle these challenges, we developed an AI-powered pervasive system that provides cognitive augmentation to users of complex systems. We present an AI cognitive assistant that provides on-the-job training to novices while acquiring and sharing (tacit) knowledge from experts. Cognitive support is provided as dialectic recommendations for standard work instructions, decision-making, training material, and knowledge acquisition. These recommendations are adjusted to the user and context to minimize interruption and maximize relevance. In this article, we describe how we implemented the cognitive assistant, how it interacts with users, its usage scenarios, and the challenges and opportunities., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Internet of Things

Learning to operate a complex system, such as an agile production line, can be a daunting task. The high variability in products and frequent reconfigurations make it difficult to keep documentation up-to-date and share new knowledge amongst factory workers. We introduce CLAICA, a Continuously Learning AI Cognitive Assistant that supports workers in the aforementioned scenario. CLAICA learns from (experienced) workers, formalizes new knowledge, stores it in a knowledge base, along with contextual information, and shares it when relevant. We conducted a user study with 83 participants who performed eight knowledge exchange tasks with CLAICA, completed a survey, and provided qualitative feedback. Our results provide a deeper understanding of how prior training, context expertise, and interaction modality affect the user experience of cognitive assistants. We draw on our results to elicit design and evaluation guidelines for cognitive assistants that support knowledge exchange in fast-paced and demanding environments, such as an agile production line., Internet of Things, Human-Centred Artificial Intelligence

Malfunction or breakdown of certain mission critical systems (MCSs) may cause losses of life, damage the environments, and/or lead to high costs. Therefore, recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper, we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes, includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations, and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance. © 2019, Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature.

Quantitative Design for Manufacture and Assembly (DFMA) approaches are often applied during the last stages of the Product Design and Development Process (PDDP) as a unique activity, but it would be integrated more efficiently working from the first stages throughout the whole process according to the Concurrent Engineering philosophy. It is proposed a holistic PDDP where the activities and tools of DFMA Methodologies are integrated through its different stages in a single and distributed way, in order to obtain an integral implementation of DFMA in a "Product Lifecycle Management -PLM" strategy further along. This attempt is oriented to the implementation of a holistic PLM strategy in a standardized academic PDDP in the B.Eng. in Product Design Engineering at EAFIT University- Colombia. Besides, it is an initial proposal to integrate DFX methodologies in PLM, seeing that previous experiences have demonstrated that, at academic level, in projects where the application of all taught courses should be evident in the final designed products, students forget to apply considerations related to manufacturing and assembly issues. Copyright © 2002-2012 The Design Society. All rights reserved.

Usually a product design process implies collaborative work between experts located in different geographical locations. This context makes the process of taking design decisions more difficult and slow. Nowadays, definition of variables and constraints is a key issue, that experts related with product design have to face throughout the product lifecycle. A Multi-Agent system is proposed for tutoring experts in a standardized manner for the definition of variables and constraints. This domain specific knowledge will be use to build "Constraint Satisfaction Problem" models to support early stages of product design, specifically embodiment design. © 2011 Springer-Verlag.

Collaborative projects integrating companies and universities have been traditionally conducted between entities from interrelated industrial sectors. Nevertheless, theory about innovation states that the involvement of people from different disciplines, expertise, and type of business foster new perspectives and enables a robust body of knowledge that will derive innovative results. This article aims to share the experience obtained through the planning, and execution of the so-called project “First View Design Lab 2018” (FV). FV is an annual collaborative and academic project organised by Universidad EAFIT that integrates the industry and the academy, in order to find new opportunities of products and services. This project implements design methodologies, co-creation and open innovation to enable knowledge transfer, and to get innovative concepts of new products/services. In 2018, FV was focused on exploring how heterogeneous teams composed by companies from different industrial sectors and the academy contribute to get new concepts that explore other business cores, different from the ones of the companies involved. The process followed enabled overcoming important challenges, such as the reluctance of Colombian companies for working in collaborative projects with other companies and the academy. © 2019 Institution of Engineering Designers, The Design Society. All rights reserved.

Cyber physical systems (CPSs) are complex systems whose performance depends on the interactions between heterogeneous subsystems, the external environment, and the interrelation between the natural systems

Cyber-physical systems (CPSs) are rapidly proliferating in different applications. Their system features significantly differ from those of linear complex systems (LCSs). Consequently, they pose novel challenges with regard to ensuring the dependability of system operation. Maintenance of CPSs raises new theoretical and practical issues. To guarantee a high level of dependability, new and efficient system maintenance principles should be explored and operationalized in various contexts. This paper reports on the first results of the authors' work in this direction. A comprehensive literature review has been conducted with the objective of identifying the specific features of LCSs and CPSs. We analysed the major maintenance principles and approaches currently applied to complex systems to see how they can be applied to CPSs. We found that the existing maintenance principles have various relationships with CPSs: (i) some of them cannot be considered in the context of CPSs due to incongruent system features, (ii) some of them can be adapted due to certain partial congruencies, and (iii) some of them can be applied directly due to the congruency of some system features of LCSs with CPSs. It was also found and demonstrated through a number of practical examples that many specific maintenance principles need to be developed for CPSs. We assert that the system features of CPSs without parallel in LCSs primarily reveal what sort of new maintenance principles and approaches are needed. The ultimate goal of our on-going research is to define and test these new maintenance principles. In this paper, we identify and define these principles, starting from the unique system features of CPSs and aiming to develop a maintenance advisory system. © 2014 Journal of Mechanical Engineering. All rights reserved.

Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in terms of failure prevention. The compensatory action the control exerts cause a fault masking effect, hampering fault diagnosis. Likewise, the multiple information inputs CPSs have to process can affect the timely system response to faults. This article proposes a failure prognosis method, which combines time series-based forecasting methods with statistically based classification techniques in order to investigate system degradation and failure forming on system levels. This method utilizes a new approach based on the concept of the system operation mode (SOM) that offers a novel perspective for health management that allows monitoring the system behavior, through the frequency and duration of SOMs. Validation of this method was conducted by systematically injecting faults in a cyber-physical greenhouse testbed. The obtained results demonstrate that the degradation and fault forming process can be monitored by analyzing the changes of the frequency and duration of SOMs. These indicators made possible to estimate the time to failure caused by various failures in the conducted experiments. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in terms of failure prevention. The compensatory action the control exerts cause a fault masking effect, hampering fault diagnosis. Likewise, the multiple information inputs CPSs have to process can affect the timely system response to faults. This article proposes a failure prognosis method, which combines time series-based forecasting methods with statistically based classification techniques in order to investigate system degradation and failure forming on system levels. This method utilizes a new approach based on the concept of the system operation mode (SOM) that offers a novel perspective for health management that allows monitoring the system behavior, through the frequency and duration of SOMs. Validation of this method was conducted by systematically injecting faults in a cyber-physical greenhouse testbed. The obtained results demonstrate that the degradation and fault forming process can be monitored by analyzing the changes of the frequency and duration of SOMs. These indicators made possible to estimate the time to failure caused by various failures in the conducted experiments., Internet of Things, Cyber-Physical Systems

BACKGROUND: Emerging evidence suggests that androgens and estrogens have a role in respiratory health, but it is largely unknown whether levels of these hormones can affect lung function in adults from the general population. This study investigated whether serum dehydroepiandrosterone sulfate (DHEA-S), a key precursor of both androgens and estrogens in peripheral tissues, was related to lung function in adult women participating in the European Community Respiratory Health Survey (ECRHS). METHODS: Lung function and serum DHEA-S concentrations were measured in n = 2,045 and n = 1,725 women in 1999-2002 and in 2010-2013, respectively. Cross-sectional associations of DHEA-S levels (expressed as age-adjusted z-score) with spirometric outcomes were investigated, adjusting for smoking habits, body mass index, menopausal status, and use of corticosteroids. Longitudinal associations of DHEA-S levels in 1999-2002 with incidence of restrictive pattern and airflow limitation in 2010-2013 were also assessed. FINDINGS: Women with low DHEA-S (z-score<-1) had lower FEV1 (% of predicted, adjusted difference: -2.2; 95%CI: -3.5 to -0.9) and FVC (-1.7; 95%CI: -2.9 to -0.5) and were at a greater risk of having airflow limitation and restrictive pattern on spirometry than women with higher DHEA-S levels. In longitudinal analyses, low DHEA-S at baseline was associated with a greater incidence of airflow limitation after an 11-years follow-up (incidence rate ratio, 3.43; 95%CI: 1.91 to 6.14). INTERPRETATION: Low DHEA-S levels in women were associated with impaired lung function and a greater risk of developing airflow limitation later in adult life. Our findings provide new evidence supporting a role of DHEA-S in respiratory health. FUNDING: EU H2020, grant agreement no.633212.

OBJECTIVES: To compare the prevalence of different insomnia subtypes among middle-aged adults from Europe and Australia and to explore the cross-sectional relationship between insomnia subtypes, respiratory symptoms and lung function. DESIGN: Cross-sectional population-based, multicentre cohort study. SETTING: 23 centres in 10 European countries and Australia. METHODS: We included 5800 participants in the third follow-up of the European Community Respiratory Health Survey III (ECRHS III) who answered three questions on insomnia symptoms: difficulties falling asleep (initial insomnia), waking up often during the night (middle insomnia) and waking up early in the morning and not being able to fall back asleep (late insomnia). They also answered questions on smoking, general health and chronic diseases and had the following lung function measurements: forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and the FEV1/FVC ratio. Changes in lung function since ECRHS I about 20 years earlier were also analysed. MAIN OUTCOME MEASURES: Prevalence of insomnia subtypes and relationship to respiratory symptoms and function. RESULTS: Overall, middle insomnia (31.2%) was the most common subtype followed by late insomnia (14.2%) and initial insomnia (11.2%). The highest reported prevalence of middle insomnia was found in Iceland (37.2%) and the lowest in Australia (22.7%), while the prevalence of initial and late insomnia was highest in Spain (16.0% and 19.7%, respectively) and lowest in Denmark (4.6% and 9.2%, respectively). All subtypes of insomnia were associated with significantly higher reported prevalence of respiratory symptoms. Only isolated initial insomnia was associated with lower FEV1, whereas no association was found between insomnia and low FEV1/FVC ratio or decline in lung function. CONCLUSION: There is considerable geographical variation in the prevalence of insomnia symptoms. Middle insomnia is most common especially in Iceland. Initial and late ins

Typically, emerging system failures have a strong impact on the performance of industrial systems as well as on the efficiency of their operational and servicing processes. Being aware of these, maintenance and repair researchers have developed multiple failure detection and diagnosis techniques that allow early recognition of system or component failures and maintaining continuous system operation in a cost-effective way. However, these techniques have many deficiencies in the case of self-tuning first generation cyber-physical systems (1G-CPSs). The reason is that these systems compensate for the effects of emerging system failures until their resources are exhausted, and the compensatory actions not only mask the failures, but also make their recognition difficult. Late recognition of failures is however in contrast with the principles of preventive maintenance. Therefore, the promotion research concentrated on the issue of recognizing and forecasting failures under dynamic and adaptive behavior of 1G-CPSs. CPSs are enabled to compensate for failure symptoms by changing their system operation modes (SOMs). It was also observed that transitions of SOMs reduce the reliability of a signal-based failure diagnosis. It was hypothesized that the frequency and the duration of the changes of the operational states of the 1G-CPS may be strong indicators of the failure emergence phenomenon and that investigation of SOMs facilitates early detection of failures. Therefore, the completed exploratory studies were aimed at exploring how the frequency and duration of transitions of SOMs can be brought into correlation with specific types of failures, and how they can be computed as measures of failure occurrence. The obtained results revealed that system failures tend to induce unusual system operation modes that can be used as basis for failure characterization, and even for failure forecasting. The empirical research made use of a cyber-physical greenhouse testbed to, Cyber-Physical Systems

BACKGROUND: Mothers' smoking during pregnancy increases asthma risk in their offspring. There is some evidence that grandmothers' smoking may have a similar effect, and biological plausibility that fathers' smoking during adolescence may influence offspring's health through transmittable epigenetic changes in sperm precursor cells. We evaluated the three-generation associations of tobacco smoking with asthma. METHODS: Between 2010 and 2013, at the European Community Respiratory Health Survey III clinical interview, 2233 mothers and 1964 fathers from 26 centres reported whether their offspring (aged ≤51 years) had ever had asthma and whether it had coexisted with nasal allergies or not. Mothers and fathers also provided information on their parents' (grandparents) and their own asthma, education and smoking history. Multilevel mediation models within a multicentre three-generation framework were fitted separately within the maternal (4666 offspring) and paternal (4192 offspring) lines. RESULTS: Fathers' smoking before they were 15 [relative risk ratio (RRR) = 1.43, 95% confidence interval (CI): 1.01-2.01] and mothers' smoking during pregnancy (RRR = 1.27, 95% CI: 1.01-1.59) were associated with asthma without nasal allergies in their offspring. Grandmothers' smoking during pregnancy was associated with asthma in their daughters [odds ratio (OR) = 1.55, 95% CI: 1.17-2.06] and with asthma with nasal allergies in their grandchildren within the maternal line (RRR = 1.25, 95% CI: 1.02-1.55). CONCLUSIONS: Fathers' smoking during early adolescence and grandmothers' and mothers' smoking during pregnancy may independently increase asthma risk in offspring. Thus, risk factors for asthma should be sought in both parents and before conception. FUNDING: European Union (Horizon 2020, GA-633212).

Life course data on obesity may enrich the quality of epidemiologic studies analysing health consequences of obesity. However, achieving such data may require substantial resources. We investigated the use of body silhouettes in adults as a tool to reflect obesity in the past. We used large population-based samples to analyse to what extent self-reported body silhouettes correlated with the previously measured (9-23 years) body mass index (BMI) from both measured (European Community Respiratory Health Survey, N = 3 041) and self-reported (Respiratory Health In Northern Europe study, N = 3 410) height and weight. We calculated Spearman correlation between BMI and body silhouettes and ROC-curve analyses for identifying obesity (BMI ≥30) at ages 30 and 45 years. Spearman correlations between measured BMI age 30 (±2y) or 45 (±2y) and body silhouettes in women and men were between 0.62-0.66 and correlations for self-reported BMI were between 0.58-0.70. The area under the curve for identification of obesity at age 30 using body silhouettes vs previously measured BMI at age 30 (±2y) was 0.92 (95% CI 0.87, 0.97) and 0.85 (95% CI 0.75, 0.95) in women and men, respectively; for previously self-reported BMI, 0.92 (95% CI 0.88, 0.95) and 0.90 (95% CI 0.85, 0.96). Our study suggests that body silhouettes are a useful epidemiological tool, enabling retrospective differentiation of obesity and non-obesity in adult women and men.

Cyber physical systems (CPSs) are complex systems whose performance depends on the interactions between heterogeneous subsystems, the external environment, and the interrelation between the natural systems and its cyber-physical augmentations. Any fault that occurs during these diverse and interrelated interactions may cause failure or a malfunction of the entire system. The traditional multi-aspects modeling techniques developed for analyzing complex system performance can to some extent fundamentally be used in the analysis of CPSs. The problem, however, is that these modeling techniques do not consider the consequences of the integration of natural systems and cyber-physical augmentations. In this article, we propose a novel dual-aspect modeling technique that integrates natural and CPSs functional systems. This modeling technique includes natural systems, such as plants and humans as integral parts of the system, and therefore allows us to study their interactions, influences and effects. A cyber-physical greenhouse case study is presented to demonstrate the applicability of the proposed modeling technique. The proposed dual-aspect modeling technique will be used in the subsequent stages of our research to analyze and understand the interactions between natural systems and CPS augmentations with a view to identify influential factors of failure in system operation as well as the cause and types or modes failures., Design Engineering, Industrial Design Engineering

This study evaluated effect of the probiotic Proenzime® added to protein mineral salt on weight gain of Nellore cattle. Intact Nellore males (Bos indicus) aged about 10 months were on Panicum maximum pasture and supplemented by either: protein mineral salt (Gc; n = 54 animals); or protein mineral salt added probiotic Proenzime® (G3 = 3 g probiotic/day; n= 54 animals). Calves in G3 significantly increased body weight (33.28 percent) as compared with Gc. This increased net income per animal to R$9.30. Therefore, protein mineral salt supplemented with probiotic Proenzime® increase weight gain cattle, thus increasing economic gain, Este estudo avaliou o efeito do probiotico Proenzime® adicionado no sal mineral proteinado no ganho de peso em bovinos. Utilizou-se bovinos, machos inteiros da raça Nelore (Bos indicus) com aproximadamente 10 meses de idade em pastagem de Panicum maximum e suplementados com sal mineral proteinado (Gc: n= 54 animais) e sal mineral proteinado adicionado de probiotico Proenzime® (G3= 3 g de probiótico/dia; n= 54 animais). Os bezerros do grupo G3 tiveram aumento significativo no ganho de peso (33,28 p.100) em relação grupo controle. Isto aumentou a renda por animal em R$9,30. Portanto, adicionar probiotico Proenzime® no sal mineral proteinado aumenta o ganho de peso em bovinos, elevando assim o ganho econômico