3,465 research outputs found
Public-private partnerships in China's urban water sector
During the past decades, the traditional state monopoly in urban water management has been debated heavily, resulting in different forms and degrees of private sector involvement across the globe. Since the 1990s, China has also started experiments with new modes of urban water service management and governance in which the private sector is involved. It is premature to conclude whether the various forms of private sector involvement will successfully overcome the major problems (capital shortage, inefficient operation, and service quality) in China¿s water sector. But at the same time, private sector involvement in water provisioning and waste water treatments seems to have become mainstream in transitional China
Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners
In this paper, we suggest a novel method to aid lifelong learners to access
relevant OER based learning content to master skills demanded on the labour
market. Our software prototype 1) applies Text Classification and Text Mining
methods on vacancy announcements to decompose jobs into meaningful skills
components, which lifelong learners should target; and 2) creates a hybrid OER
Recommender System to suggest personalized learning content for learners to
progress towards their skill targets. For the first evaluation of this
prototype we focused on two job areas: Data Scientist, and Mechanical Engineer.
We applied our skill extractor approach and provided OER recommendations for
learners targeting these jobs. We conducted in-depth, semi-structured
interviews with 12 subject matter experts to learn how our prototype performs
in terms of its objectives, logic, and contribution to learning. More than 150
recommendations were generated, and 76.9% of these recommendations were treated
as useful by the interviewees. Interviews revealed that a personalized OER
recommender system, based on skills demanded by labour market, has the
potential to improve the learning experience of lifelong learners.Comment: This paper has been accepted to be published in the proceedings of
CSEDU 2020 by SciTePres
Listening to Corrosion
Using condition monitoring techniques to achieve predictive maintenance is a prominent topic for military systems. Some of the main challenges related to this topic will be introduced, and after that a specific application will be used to demonstrate the successful development of a corrosion monitoring technique. One of the effective ways to cope with corrosion as a failure mechanism is to use dedicated sensors. Preferably, these sensors do not interfere with the prevalent corrosion process, i.e. they ‘listen to corrosion’ as it occurs spontaneously. A potentially interesting monitoring technique is based on electrochemical noise (EN), which is the spontaneous charge transfer generated by the corrosion process. A unique property of this technique is the possibility to identify corrosion processes based on their EN signature. This work describes the analysis of EN signals, based on which corrosion identification can be performed. Metastable pitting of AISI304 stainless steel serves as an example of the analysis procedure. The effectiveness of the procedure is then demonstrated by means of the identification of microbiologically influenced corrosion (MIC), which is generally regarded as one of the most difficult to predict corrosion mechanisms
Cost-effectiveness modelling of three different hysterosalpingography diagnostic strategies in addition to standard fertility management for couples with unexplained infertility in the United Kingdom
Funding: This research was supported by Guerbet, Paris, France. Guerbet had no influence on the results of this researchPeer reviewedPostprin
OER Recommendations to Support Career Development
This Work in Progress Research paper departs from the recent, turbulent
changes in global societies, forcing many citizens to re-skill themselves to
(re)gain employment. Learners therefore need to be equipped with skills to be
autonomous and strategic about their own skill development. Subsequently,
high-quality, on-line, personalized educational content and services are also
essential to serve this high demand for learning content. Open Educational
Resources (OERs) have high potential to contribute to the mitigation of these
problems, as they are available in a wide range of learning and occupational
contexts globally. However, their applicability has been limited, due to low
metadata quality and complex quality control. These issues resulted in a lack
of personalised OER functions, like recommendation and search. Therefore, we
suggest a novel, personalised OER recommendation method to match skill
development targets with open learning content. This is done by: 1) using an
OER quality prediction model based on metadata, OER properties, and content; 2)
supporting learners to set individual skill targets based on actual labour
market information, and 3) building a personalized OER recommender to help
learners to master their skill targets. Accordingly, we built a prototype
focusing on Data Science related jobs, and evaluated this prototype with 23
data scientists in different expertise levels. Pilot participants used our
prototype for at least 30 minutes and commented on each of the recommended
OERs. As a result, more than 400 recommendations were generated and 80.9% of
the recommendations were reported as useful.Comment: This paper has been accepted to be published in the proceedings of
IEEE Frontiers In Education (FIE) 2020 by IEEE Xplor
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