Definition of a General Conceptualization Method for the Expert Knowledge

2000 ◽  
pp. 458-469 ◽  
Author(s):  
Almudena Sierra-Alonso
Keyword(s):  
Expert Knowledge ◽  
Definition Of

The SWORFISH Project Approach for Modeling Wood Material Modifications in Timber Structures

2013 ◽  
Vol 778 ◽  
pp. 418-423
Author(s):  
Jakub Sandak ◽  
Anna Sandak ◽  
Mariapaola Riggio ◽  
Ilaria Santoni ◽  
Dusan Pauliny
Keyword(s):  
Experimental Data ◽  
Expert Knowledge ◽  
Surface Finishing ◽  
Special Focus ◽  
Original Material ◽  
Timber Structures ◽  
Wood Material ◽  
Project Approach ◽  
Special Software ◽  
Definition Of

A special software simulating changes to wood due to various processes (either treatment or degradation) has been developed within the SWORFISH (Superb Wood Surface Finishing) project. The definition of the material modifications due to processes is based on the expert knowledge and/or experimental data. The dedicated algorithm simulates material modifications (with a special focus on surface) taking into account original material characteristics (evaluated by means of NDT techniques) and setting of process parameters. In this way, it is possible to analyze the sequence of processes (i.e. material modifications) and to estimate properties of the resulting product. Two case studies are presented for illustration of the potential uses of the SWORFISH approach in the field of timber structures.

Characterizing Opioid Overdoses Using Emergency Medical Services Data

2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 62S-71S
Author(s):  
Josie J. Sivaraman ◽  
Scott K. Proescholdbell ◽  
David Ezzell ◽  
Meghan E. Shanahan
Keyword(s):  
Machine Learning ◽  
North Carolina ◽  
Emergency Medical Services ◽  
Expert Knowledge ◽  
Case Definition ◽  
Medical Services ◽  
Opioid Overdose ◽  
Machine Learning Classification ◽  
Emergency Medical ◽  
Definition Of

Objectives Tracking nonfatal overdoses in the escalating opioid overdose epidemic is important but challenging. The objective of this study was to create an innovative case definition of opioid overdose in North Carolina emergency medical services (EMS) data, with flexible methodology for application to other states’ data. Methods This study used de-identified North Carolina EMS encounter data from 2010-2015 for patients aged >12 years to develop a case definition of opioid overdose using an expert knowledge, rule-based algorithm reflecting whether key variables identified drug use/poisoning or overdose or whether the patient received naloxone. We text mined EMS narratives and applied a machine-learning classification tree model to the text to predict cases of opioid overdose. We trained models on the basis of whether the chief concern identified opioid overdose. Results Using a random sample from the data, we found the positive predictive value of this case definition to be 90.0%, as compared with 82.7% using a previously published case definition. Using our case definition, the number of unresponsive opioid overdoses increased from 3412 in 2010 to 7194 in 2015. The corresponding monthly rate increased by a factor of 1.7 from January 2010 (3.0 per 1000 encounters; n = 261 encounters) to December 2015 (5.1 per 1000 encounters; n = 622 encounters). Among EMS responses for unresponsive opioid overdose, the prevalence of naloxone use was 83%. Conclusions This study demonstrates the potential for using machine learning in combination with a more traditional substantive knowledge algorithm-based approach to create a case definition for opioid overdose in EMS data.

scholarly journals The Limits of Copyright Office Expertise

2020 ◽  
Author(s):  
Aaron Perzanowski
Keyword(s):  
United States ◽  
Subject Matter ◽  
Expert Knowledge ◽  
The United States ◽  
Policy Recommendations ◽  
Copyright Policy ◽  
Back Seat ◽  
Subject Matter Expertise ◽  
Definition Of ◽  
Economic Competence

The mismatch between the expanding administrative and regulatory obligations of the United States Copyright Office and its limited institutional expertise is an emerging problem for the copyright system. The Office’s chief responsibility—registration and recordation of copyright claims—has taken a back seat in recent years to a more ambitious set of substantive rulemakings and policy recommendations. As the triennial rulemaking under the Digital Millennium Copyright Act highlights, the Office is frequently called upon to answer technological questions far beyond its plausible claims of subject matter expertise. This Article traces the Office’s history, identifies its substantial but discrete areas of expertise, and reveals the ways in which the Office has overstepped any reasonable definition of its expert knowledge. The Article concludes with a set ofrecommendations to better align the Office’s agenda with its expertise by, first, reducing the current regulatory burdens on the Office, and second, building greater technological and economic competence within the Office, better equipping it to address contemporary questions of copyright policy.

scholarly journals La red informal como una modalidad de trabajo en equipo entre docentes universitarios / The informal network as a mode of teamwork among university professors

2014 ◽  
Vol 10 (20) ◽  
pp. 10-16
Author(s):  
Laura Lodeiro Enjo
Keyword(s):  
Expert Knowledge ◽  
Structural Complexity ◽  
Team Work ◽  
University Professors ◽  
Informal Network ◽  
Trabajo En Equipo ◽  
Definition Of ◽  
Conocimiento Experto ◽  
La Red ◽  
Phd Thesis

Se presenta a continuación la definición de equipo docente que ha sido adoptada en el marco de una tesis doctoral centrada en elicitar el conocimiento experto que generan sobre el trabajo en equipo los docentes universitarios que trabajan de este modo. Uno de los objetivos de esa tesis doctoral es establecer una escala de complejidad estructural en función de las diferentes modalidades de trabajo en equipo identificadas durante la realización del estudio. Justamente la segunda mitad del artículo se dedica a explicar y ejemplificar una de esas modalidades: la modalidad de red informal que nos permite pensar en relaciones puntuales y latentes.AbstractThis article presents de definition of teaching teamwork that has been adopted from the framework of a doctoral thesis focused on eliciting expert knowledge generated about teamwork university teachers working in this way. One goal of this PhD thesis is to establish a level of structural complexity in terms of the different modes of team work identified during the study. The second part of the article is devoted to explaining and exemplifying one of these modes: informal network that allows us to think in specific and latent relations.

scholarly journals Mapping dominant runoff processes: an evaluation of different approaches using similarity measures and synthetic runoff simulations

2016 ◽  
Vol 20 (7) ◽  
pp. 2929-2945 ◽  
Author(s):  
Manuel Antonetti ◽  
Rahel Buss ◽  
Simon Scherrer ◽  
Michael Margreth ◽  
Massimiliano Zappa
Keyword(s):  
Model Building ◽  
Expert Knowledge ◽  
Similarity Measures ◽  
Landscape Classification ◽  
Ungauged Catchments ◽  
Application Range ◽  
Model And Simulation ◽  
Swiss Plateau ◽  
Soil Information ◽  
Definition Of

Abstract. The identification of landscapes with similar hydrological behaviour is useful for runoff and flood predictions in small ungauged catchments. An established method for landscape classification is based on the concept of dominant runoff process (DRP). The various DRP-mapping approaches differ with respect to the time and data required for mapping. Manual approaches based on expert knowledge are reliable but time-consuming, whereas automatic GIS-based approaches are easier to implement but rely on simplifications which restrict their application range. To what extent these simplifications are applicable in other catchments is unclear. More information is also needed on how the different complexities of automatic DRP-mapping approaches affect hydrological simulations. In this paper, three automatic approaches were used to map two catchments on the Swiss Plateau. The resulting maps were compared to reference maps obtained with manual mapping. Measures of agreement and association, a class comparison, and a deviation map were derived. The automatically derived DRP maps were used in synthetic runoff simulations with an adapted version of the PREVAH hydrological model, and simulation results compared with those from simulations using the reference maps. The DRP maps derived with the automatic approach with highest complexity and data requirement were the most similar to the reference maps, while those derived with simplified approaches without original soil information differed significantly in terms of both extent and distribution of the DRPs. The runoff simulations derived from the simpler DRP maps were more uncertain due to inaccuracies in the input data and their coarse resolution, but problems were also linked with the use of topography as a proxy for the storage capacity of soils. The perception of the intensity of the DRP classes also seems to vary among the different authors, and a standardised definition of DRPs is still lacking. Furthermore, we argue not to use expert knowledge for only model building and constraining, but also in the phase of landscape classification.

scholarly journals Integration of expert knowledge in the definition of Swiss pear core collection

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
J. Urrestarazu ◽  
C. Kägi ◽  
A. Bühlmann ◽  
J. Gassmann ◽  
L. G. Santesteban ◽  
...  
Keyword(s):  
Core Collection ◽  
Expert Knowledge ◽  
Definition Of

The impact of modelling decisions in hydrological modelling

2021 ◽  
Author(s):  
Janneke Remmers ◽  
Ryan Teuling ◽  
Lieke Melsen
Keyword(s):  
Scientific Reasoning ◽  
Performance Metrics ◽  
Expert Knowledge ◽  
Modelling Framework ◽  
Modelling Environment ◽  
And Performance ◽  
Definition Of ◽  
The Impact ◽  
Modular Modelling ◽  
Modelling Process

<p>Scientific hydrological modellers make multiple decisions during the modelling process, e.g. related to the calibration period and performance metrics. These decisions affect the model results differently. Modelling decisions can refer to several steps in the modelling process. In this project, modelling decisions refer to the decisions made during the whole modelling process, not just the definition of the model structure. Each model output is a hypothesis of the reality; it is an interpretation of the real system underpinned by scientific reasoning and/or expert knowledge. Currently, there is a lack of knowledge and understanding about which modelling decisions are taken and why they are taken. Consequently, the influence of modelling decisions is unknown. Quantifying this influence, which is done in this study, can raise awareness among scientists. This study is based on analysis of interviews with scientific hydrological modellers, thus taking actual practices into account. Different modelling decisions were identified from the interviews, which are subsequently implemented and evaluated in a controlled modelling environment, in our case the modular modelling framework Raven. The variation in the results is analysed to determine which decisions affect the results and how they affect the results. This study pinpoints what aspects are important to consider in studying modelling decisions, and can be an incentive to clarify and improve modelling procedures.</p>

Expert Systems in Distance Education

2018 ◽  
pp. 1410-1423
Author(s):  
Duygu Mutlu-Bayraktar ◽  
Esad Esgin
Keyword(s):  
Artificial Intelligence ◽  
Distance Education ◽  
Natural Language ◽  
Expert Systems ◽  
Expert Knowledge ◽  
Computer Systems ◽  
Educational Environments ◽  
Artificial Environment ◽  
Present Solution ◽  
Definition Of

Computers have been used in educational environments to carry out applications that need expertise, such as compiling, storing, presentation, and evaluation of information. In some teaching environments that need expert knowledge, capturing and imitating the knowledge of the expert in an artificial environment and utilizing computer systems that have the ability to communicate with people using natural language might reduce the need for the expert and provide fast results. Expert systems are a study area of artificial intelligence and can be defined as computer systems that can approach a problem for which an answer is being sought like an expert and present solution recommendations. In this chapter, the definition of expert systems and their characteristics, information about the expert systems in teaching environments, and especially their utilization in distance education are given.

scholarly journals Definition of sampling units begets conclusions in ecology: the case of habitats for plant communities

2014 ◽  
Author(s):  
Martin A Mörsdorf ◽  
Virve T. Ravolainen ◽  
Leif Einar Støvern ◽  
Nigel G. Yoccoz ◽  
Ingibjörg S. Jónsdóttir ◽  
...  
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Expert Knowledge ◽  
Habitat Characteristics ◽  
Community Characteristics ◽  
Formal Approach ◽  
Study Sites ◽  
Definition Of ◽  
Density Results ◽  
Plant Community Characteristics

In ecology, expert knowledge on habitat characteristics is often used to define sampling units such as study sites. Ecologists are especially prone to such approaches when prior sampling frames are not accessible. Here we ask to what extent can different approaches to the definition of sampling units influence the conclusions that are drawn from an ecological study? We do this by comparing a formal versus a subjective definition of sampling units within a study design which is based on well-articulated objectives and proper methodology. Both approaches are applied to tundra plant communities in mesic and snowbed habitats. For the formal approach, sampling units were first defined for each habitat in concave terrain of suitable slope using GIS. In the field, these units were only accepted as the targeted habitats if additional criteria for vegetation cover were fulfilled. For the subjective approach, sampling units were defined visually in the field, based on typical plant communities of mesic and snowbed habitats. For each approach, we collected information about plant community characteristics within a total of 11 mesic and seven snowbed units distributed between two herding districts of contrasting reindeer density. Results from the two approaches differed significantly in several plant community characteristics in both mesic and snowbed habitats. Furthermore, differences between the two approaches were not consistent because their magnitude and direction differed both between the two habitats and the two reindeer herding districts. Consequently, we could draw different conclusions on how plant diversity and relative abundance of functional groups are differentiated between the two habitats depending on the approach used. We therefore challenge ecologists to formalize the expert knowledge applied to define sampling units through a set of well-articulated rules, rather than applying it subjectively. We see this as instrumental for progress in ecology as only rules based on expert knowledge are transparent and lead to results reproducible by other ecologists.

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