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Automatica ◽  
2022 ◽  
Vol 136 ◽  
pp. 110052
Andrea Martinelli ◽  
Matilde Gargiani ◽  
John Lygeros

2021 ◽  
Hao WANG ◽  
Yaqi XIE ◽  
Mingqi WEN ◽  

Jens Poeppelbuss ◽  
Martin Ebel ◽  
Jürgen Anke

AbstractSmart service innovation is the process of reconfiguring resources, structures, and value co-creation processes in service systems that result in novel data-driven service offerings. The nature of such offerings requires the involvement of multiple actors, which has been investigated by a few studies only. In particular, little is known about the multiple actors’ efforts to manage uncertainty in the process of establishing smart service systems. Empirically grounded in data from 25 interviews with industry experts, we explore how organizations act and interact in smart service innovation processes. For our data analysis, we adopt a microfoundational view to derive a theoretical model that conceptualizes actor engagement as a microfoundation for iterative uncertainty reduction in the actor-to-actor network of the smart service system. Our study contributes to information systems research on service systems engineering and digital transformation by explaining smart service innovation from both a multi-actor and a multi-level perspective, drawing on service-dominant (S-D) logic and microfoundations as well-established theoretical lenses.

2021 ◽  
Lizhou Fan

In the Web 2.0 Era, most social media archives are born digital and large-scale. With an increasing need for processing them at a fast speed, researchers and archivists have started applying data science methods in managing social media data collections. However, many of the current computational or data-driven archival processing methods are missing the critical background understandings like “why we need to use computational methods,” and “how to evaluate and improve data-driven applications.” As a result, many computational archival science (CAS) attempts, with comparatively narrow scopes and low efficiencies, are not sufficiently holistic. In this talk, we first introduce the proposed concept of “Archival Data Thinking” that highlights the desirable comprehensiveness in mapping data science mindsets to archival practices. Next, we examine several examples of implementing “Archival Data Thinking” in processing two social media collections: (i) the COVID-19 Hate Speech Twitter Archive (CHSTA) and (ii) the Counter-anti-Asian Hate Twitter Archive (CAAHTA), both of which are with millions of records and their metadata, and needs for rapid processing. Finally, as a future research direction, we briefly discuss the standards and infrastructures that can better support the implementation of “Archival Data Thinking”.

2021 ◽  
Vol 13 (23) ◽  
pp. 13340
Deborah Agostino ◽  
Marco Brambilla ◽  
Silvio Pavanetto ◽  
Paola Riva

In the cultural tourism field, there has been an increasing interest in adopting data-driven approaches that are aimed at measuring the service quality dimensions through online reviews. To date, studies measuring quality dimensions in cultural tourism settings through content analysis of online user-generated reviews are mainly based on manual approaches. When the content analysis is automated, these studies do not compare different analytical approaches. Our paper enters this field by comparing two different automated content analysis approaches to evaluate which of the two is more adequate for assessing the quality dimensions through user-generated reviews in an empirical setting of 100 Italian museums. Specifically, we compare a ‘top-down’ content analysis approach that is based on a supervised classification built on policy makers’ guidelines and a ‘bottom-up’ approach that is based on an unsupervised topic model of the online words of reviewers. The resulting museum quality dimensions are compared, showing that the ‘bottom-up’ approach reveals additional quality dimensions compared with those obtained through the ‘top-down’ approach. The misalignment of the results of the ‘top-down’ and ‘bottom-up’ approaches to quality evaluation for museums enhances the critical discussion on the contribution that data analytics can offer to support decision making in cultural tourism.

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0258573
Joseph Kamanga ◽  
Kayla Stankevitz ◽  
Andres Martinez ◽  
Robert Chiegil ◽  
Lameck Nyirenda ◽  

Introduction Open Doors, an HIV prevention project targeting key populations in Zambia, recorded low HIV positivity rates (9%) among HIV testing clients, compared to national adult prevalence (12.3%), suggesting case finding efficiency could be improved. To close this gap, they undertook a series of targeted programmatic and management interventions. We share the outcomes of these interventions, specifically changes in testing volume, HIV positivity rate, and total numbers of key populations living with HIV identified. Methods The project implemented a range of interventions to improve HIV case finding using a Total Quality Leadership and Accountability (TQLA) approach. We analyzed program data for key populations who received HIV testing six months before the interventions (October 2017–March 2018) and 12 months after (April 2018–March 2019). Interrupted time series analysis was used to evaluate the impact on HIV positivity and total case finding and trends in positivity and case finding over time, before and after the interventions. Results While the monthly average number of HIV tests performed increased by only 14% post-intervention, the monthly average number of HIV positive individuals identified increased by 290%. The average HIV positivity rate rose from 9.7% to 32.4%. Positivity rates and case finding remained significantly higher in all post-intervention months. Similar trends were observed among FSW and MSM. Conclusions The Open Doors project was able to reach large numbers of previously undiagnosed key populations by implementing a targeted managerial and technical intervention, resulting in a significant increase in the HIV positivity rate sustained over 12 months. These results demonstrate that differentiated, data-driven approaches can help close the 95-95-95 gaps among key populations.

Patrick Filippi ◽  
Brett M. Whelan ◽  
R. Willem Vervoort ◽  
Thomas F. A. Bishop

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260714
Bastien Paré ◽  
Marieke Rozendaal ◽  
Sacha Morin ◽  
Léa Kaufmann ◽  
Shawn M. Simpson ◽  

The first confirmed case of COVID-19 in Quebec, Canada, occurred at Verdun Hospital on February 25, 2020. A month later, a localized outbreak was observed at this hospital. We performed tiled amplicon whole genome nanopore sequencing on nasopharyngeal swabs from all SARS-CoV-2 positive samples from 31 March to 17 April 2020 in 2 local hospitals to assess viral diversity (unknown at the time in Quebec) and potential associations with clinical outcomes. We report 264 viral genomes from 242 individuals–both staff and patients–with associated clinical features and outcomes, as well as longitudinal samples and technical replicates. Viral lineage assessment identified multiple subclades in both hospitals, with a predominant subclade in the Verdun outbreak, indicative of hospital-acquired transmission. Dimensionality reduction identified two subclades with mutations of clinical interest, namely in the Spike protein, that evaded supervised lineage assignment methods–including Pangolin and NextClade supervised lineage assignment tools. We also report that certain symptoms (headache, myalgia and sore throat) are significantly associated with favorable patient outcomes. Our findings demonstrate the strength of unsupervised, data-driven analyses whilst suggesting that caution should be used when employing supervised genomic workflows, particularly during the early stages of a pandemic.

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