Human-technology integration with industrial conversational agents: A conceptual architecture and a taxonomy for manufacturing

Conversational agents are systems with great potential to enhance human-computer interaction in industrial settings. Although the number of applications of conversational agents in many fields is growing, there is no shared view of the elements to design and implement for chatbots in the industrial field. The paper presents the combination of many research contributions into an integrated conceptual architecture, for developing industrial conversational agents using Nickerson's methodology. The conceptual architecture consists of five core modules; every module consists of specific elements and approaches. Furthermore, the paper defines a taxonomy from the study of empirical applications of manufacturing conversational agents. Indeed, some applications of chatbots in manufacturing are available but those have never been collected in single research. The paper fills this gap by analyzing the empirical cases and presenting a qualitative analysis, with verification of the proposed taxonomy. The contribution of the article is mainly to illustrate the elements needed for the development of a conversational agent in manufacturing: researchers and practitioners can use the proposed conceptual architecture and taxonomy to more easily investigate, define, and develop all the elements for chatbot implementation

Results from a natural experiment: initial neighbourhood investments do not change objectively-assessed physical activity, psychological distress or perceptions of the neighbourhood

Abstract Background Few studies have assessed objectively measured physical activity (PA), active transportation, psychological distress and neighborhood perceptions among residents of a neighborhood before and after substantial improvements in its physical environment. Also, most research-to-date has employed study designs subject to neighborhood selection, which may introduce bias in reported findings. We built upon a previously enrolled cohort of households from two low-income predominantly African American Pittsburgh neighborhoods, matched on socio-demographic composition including race/ethnicity, income and education. One of the two neighborhoods received substantial neighborhood investments over the course of this study including, but not limited to public housing development and greenspace/landscaping. We implemented a natural experiment using matched intervention and control neighborhoods and conducted pre-post assessments among the cohort. Our comprehensive assessments included accelerometry-based PA, active transportation, psychological distress and perceptions of the neighborhood, with assessments conducted both prior to and following the neighborhood changes. In 2013, we collected data from 1003 neighborhood participants and in 2016, we re-interviewed 676 of those participants. We conducted an intent to treat analysis, with a difference-in-difference estimator using attrition weighting to account for nonresponse between 2013 and 2016. In addition, we derived an individual-level indicator of exposure to neighbourhood investment and estimated effect of exposure to investment on the same set of outcomes using covariate-adjusted models. Results We observed no statistically significant differences in activity, psychological distress, satisfaction with one’s neighborhood as a place to live or any of the other measures we observed prior to and after the neighborhood investments between the intervention and control neighborhoods or those exposed vs not exposed to investments. Conclusions Using this rigorous study design, we observed no significant changes in the intervention neighborhood above and beyond secular trends present in the control neighborhood. Although neighborhood investment may have other benefits, we failed to see improvement in PA, psychological distress or related outcomes in the low-income African American neighborhoods in our study. This may be an indication that improvements in the physical environment may not directly translate into improvements in residents’ physical activity or health outcomes without additional individual-level interventions. It is also possible that these investments were not dramatic enough to spur change within the three year period. Additional studies employing similar design with other cohorts in other settings are needed to confirm these results. Trial registration Trial Registration is not applicable since we did not prospectively assign individuals to a health-related intervention.https://deepblue.lib.umich.edu/bitstream/2027.42/148333/1/12966_2019_Article_793.pd

Adult use of cigars, little cigars, and cigarillos in Cuyahoga County, Ohio: A cross-sectional study

INTRODUCTION: Adult use of cigars, cigarillos, and little cigars has increased over the past two decades; however, little is known about the characteristics of the users. METHODS: The data were derived from 5 years (2003-2007) of the Cuyahoga County Behavioral Risk Factor Surveillance Survey, a random digit-dialed telephone survey conducted by ICF Macro International, based on the survey and methods of the Ohio BRFSS. RESULTS: Results indicate that the prevalence of current cigarette smoking across the 5 years was 23.1%. Cigar use and little cigar use were reported by 4.3% and 3.3% of respondents, respectively. Compared with cigarette users, cigar and little cigar users were far more likely to report multiple product use (12.8% vs. 63.9% and 80.5%, respectively). Cigar and little cigar users differed from cigarette smokers in demographic profile and patterns of multiple product use. DISCUSSION: Black and lower income adults were significantly more likely to report use of little cigars and use of multiple products. These disparities potentially contribute to the disproportionate rates of tobacco-related illnesses and underrepresentation of low-income and minority populations in tobacco use prevalence rates

Factors associated with development of excessive fatness in children and adolescents: a review of prospective studies

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99011/1/obr12035.pd

Pathways through which higher neighborhood crime is longitudinally associated with greater body mass index

Abstract Background Although crime and perceived safety are associated with obesity and body mass index (BMI), the pathways are less clear. Two likely pathways by which crime and perceived safety may impact obesity are through distress and physical activity. Methods We examined data from 2013 to 2014 for 644 predominantly African-American adults (mean age 57 years; 77% female) living in low-income Pittsburgh, PA neighborhoods, including self-reported perceptions of safety and emotional distress, interviewer-measured height/weight, and physical activity measured via accelerometry. We used secondary data on neighborhood crime from 2011 to 2013. We built a structural equation model to examine the longitudinal direct and indirect pathways from crime to BMI through perceived safety, distress and physical activity. Results Long-term exposure to crime was positively associated with lack of perceived safety (β = 0.11, p = 0.005) and lack of perceived safety was positively associated with BMI (β = 0.08, p = 0.03). The beneficial association between physical activity and BMI (β = −0.15, p < 0.001) was attenuated by a negative association between crime and physical activity (β = −0.09, p = 0.01). Although crime was associated with distress we found no evidence of a path from crime to BMI via distress. Conclusions Our findings suggest decrements in perceived safety and physical activity are important processes that might explain why neighborhood crime is associated with greater BMI.https://deepblue.lib.umich.edu/bitstream/2027.42/139054/1/12966_2017_Article_611.pd

One size doesn’t fit all: cross-sectional associations between neighborhood walkability, crime and physical activity depends on age and sex of residents

Abstract Background Low-income African American adults are disproportionately affected by obesity and are also least likely to engage in recommended levels of physical activity (Flegal et al. JAMA 303(3):235-41, 2010; Tucker et al. Am J Prev Med 40(4):454-61, 2011). Moderate-to-vigorous physical activity (MVPA) is an important factor for weight management and control, as well as for reducing disease risk (Andersen et al. Lancet 368(9532):299-304, 2006; Boreham and Riddoch J Sports Sci 19(12):915-29, 2001; Carson et al. PLoS One 8(8):e71417, 2013). While neighborhood greenspace and walkability have been associated with increased MVPA, evidence also suggests that living in areas with high rates of crime limits MVPA. Few studies have examined to what extent the confluence of neighborhood greenspace, walkability and crime might impact MVPA in low-income African American adults nor how associations may vary by age and sex. Methods In 2013 we collected self-reported data on demographics, functional limitations, objective measures of MVPA (accelerometry), neighborhood greenspace (geographic information system), and walkability (street audit) in 791 predominantly African-American adults (mean age 56 years) living in two United States (U.S.) low-income neighborhoods. We also acquired data from the City of Pittsburgh on all crime events within both neighborhoods. Exposure: To examine cross-sectional associations of neighborhood-related variables (i.e., neighborhood greenspace, walkability and crime) with MVPA, we used zero-inflated negative binomial regression models. Additionally, we examined potential interactions by age (over 65 years) and sex on relationships between neighborhood variables and MVPA. Results Overall, residents engaged in very little to no MVPA regardless of where they lived. However, for women, but not men, under the age of 65 years, living in more walkable neighborhoods was associated with more time engaged in MVPA in (β = 0.55, p = 0.007) as compared to their counterparts living in less walkable areas. Women and men age 65 years and over spent very little time participating in MVPA regardless of neighborhood walkability. Neither greenspace nor crime was associated with MVPA in age-sex subgroups. Conclusions Neighborhood walkability may play a stronger role on MVPA than accessible greenspace or crime in low-income urban communities. Walkability may differentially impact residents depending on their age and sex, which suggests tailoring public health policy design and implementation according to neighborhood demographics to improve activity for all.http://deepblue.lib.umich.edu/bitstream/2027.42/135725/1/12889_2016_Article_3959.pd

Natural Language Processing applications in manufacturing: a systematic literature review

Among the manufacturing sector several applications of Natural Language Processing (NLP) are emerging. NLP is a branch of Artificial Intelligence (AI) aimed at understanding, interpreting, and manipulating human language through computer-based data processing. This application is quite powerful and prospective in manufacturing context, considering the ever-increasing amount of data available within the organizations, often unstructured, non-standardized, and free text. Therefore, human analysis to extract information and useful knowledge results in a long and tedious task with limited added value. The automation of these activities moves workers to more meaningful and value-added activities; it improves efficiency in searching for and extracting information, with benefits for decision-making processes. The paper presents a systematic literature review concerning NLP applications in manufacturing, conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement methodology. Basing on the documents retrieved, a comparative analysis of the literature is presented. The analysis is carried out following two different rationales: an objective analysis, which highlights and compares the different purposes with which NLP is applied in the manufacturing field, such as knowledge base, ontology, predictive maintenance, human machine interaction and decision support system. The second analysis investigates NLP applications by exploring different production process phases involved in manufacturing activities. The research identified mature NLP applications, transversally implemented in several production process phases, with specific objectives. The paper provides a comprehensive and in-depth overview on the topic. Finally, possible future directions of development of NLP in manufacturing were defined. © 2022, AIDI - Italian Association of Industrial Operations Professors. All rights reserved

Machine learning models to predict components decay in a naval propulsion system

The decay of a single component in a naval propulsion system can affect the performance of the entire system, involving expensive maintenance costs for restoring efficient conditions. Therefore, a regular control of the decay of key components of these systems is essential for properly handle maintenance actions. Moreover, in naval propulsion systems it is necessary to consider the difficulty in implementing an onboard maintenance action or returning a vessel. Two relevant components in naval propulsion systems are the turbine and the compressor. This study develops two machine learning models to predict turbine and compressor decay, i.e. based on classification and regression approaches. The former classifies whether the components are decayed or not, thus highlighting a state of criticality, the latter predicts a specific value of each decay coefficient. For each approach, different algorithms are compared, e.g. boosted trees, linear regression or support vector machines. A case study considering sixteen inputs has been used to test the effectiveness of the proposed solution, starting from a dataset of about twelve thousand instances referred to a naval vessel. A sensitivity analysis of relevant parameters has been developed to verify the robustness of the approach

Clustering application for condition-based maintenance in time-varying processes: a review using latent dirichlet allocation

In the field of industrial process monitoring, scholars and practitioners are increasing interest in time-varying processes, where different phases are implemented within an unknown time frame. The measurement of process parameters could inform about the health state of the production assets, or products, but only if the measured parameters are coupled with the specific phase identification. A combination of values could be common for one phase and uncommon for another phase; thus, the same combination of values shows a high or low probability depending on the specific phase. The automatic identification of the production phase usually relies on clustering techniques. This is largely due to the difficulty of finding training fault data for supervised models. With these two considerations in mind, this contribution proposes the Latent Dirichlet Allocation as a natural language-processing technique for reviewing the topic of clustering applied in time-varying contexts, in the maintenance field. Thus, the paper presents this innovative methodology to analyze this specific research fields, presenting the step-by-step application and its results, with an overview of the theme

Using Natural Language Processing to uncover main topics in defect recognition literature

The issue of defect detection is particularly important namely in plant engineering, where it is crucial to ensure high-quality production by minimizing the number of defective parts. In the last years, the interest in the subject has grown a lot and the methods and approaches proposed for defect recognition are multiple. Therefore, when dealing with defect recognition researchers are faced with an increasing number of articles that slows them down in identifying the set of articles of their interest. This work aims to provide a baseline classification of articles based on emerging issues such as the investigated material, the production typology in which the material is included, and the type of analysis to be effected. For these reasons, the paper proposes an automatic solution based on text mining techniques. Specifically, the study applies Natural Language Processing (NLP) to articles’ titles, abstracts, and keywords using two different approaches: K-Means clustering algorithm and Latent Dirichlet Allocation (LDA). K-Means is used to cluster the collection of documents into related groups based on the contents of the particular documents. LDA instead is used to classify the papers using the concept of topic modeling. Articles have been collected from Scopus database. The scope of the research is limited to journal and conference articles, published in English excluding articles classified as reviews, as well as book chapters, books, notes, erratum