Modeling and Validation of a Web Ontology Language Based Disassembly Planning Information Model

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
Bicheng Zhu ◽  
Utpal Roy
Keyword(s):  
Sustainable Manufacturing ◽  
Information Model ◽  
Related Information ◽  
Modeling Methodology ◽  
Ontology Language ◽  
Residual Values ◽  
Layered Modeling ◽  
Different Levels ◽  
Disassembly Planning

Disassembly, a process of separating the end of life (EOL) product into discrete components for re-utilizing their associated residual values, is an important enabler for the sustainable manufacturing. This work focuses on the modeling of the disassembly planning related information and develops a disassembly information model (DIM) based on an extensive investigation of various informational aspects in the domain of disassembly planning. The developed DIM, which represents an appropriate systematization and classification of the products, processes, uncertainties, and degradations related information, follows a layered modeling methodology in which DIM is subdivided into layers with the intent to separate general knowledge into different levels of abstractions and reach a balance between information reusability and information usability. Two prototype disassembly planning related applications have been incorporated to validate the usability and reusability of the developed DIM.

Development and Evaluation of Fuzzy Criteria for the Diagnosis of Rheumatoid Arthritis

1996 ◽  
Vol 35 (04/05) ◽  
pp. 334-342 ◽  
Author(s):  
K.-P. Adlassnig ◽  
G. Kolarz ◽  
H. Leitich
Keyword(s):  
Rheumatoid Arthritis ◽  
Reference Model ◽  
American Rheumatism Association ◽  
Uniform Definition ◽  
Sensitivity Rate ◽  
Fuzzy Techniques ◽  
Definition Of ◽  
Different Levels ◽  
Specificity Rate

Abstract:In 1987, the American Rheumatism Association issued a set of criteria for the classification of rheumatoid arthritis (RA) to provide a uniform definition of RA patients. Fuzzy set theory and fuzzy logic were used to transform this set of criteria into a diagnostic tool that offers diagnoses at different levels of confidence: a definite level, which was consistent with the original criteria definition, as well as several possible and superdefinite levels. Two fuzzy models and a reference model which provided results at a definite level only were applied to 292 clinical cases from a hospital for rheumatic diseases. At the definite level, all models yielded a sensitivity rate of 72.6% and a specificity rate of 87.0%. Sensitivity and specificity rates at the possible levels ranged from 73.3% to 85.6% and from 83.6% to 87.0%. At the superdefinite levels, sensitivity rates ranged from 39.0% to 63.7% and specificity rates from 90.4% to 95.2%. Fuzzy techniques were helpful to add flexibility to preexisting diagnostic criteria in order to obtain diagnoses at the desired level of confidence.

scholarly journals Formation of an Export-Oriented Agricultural Economy and Regional Open Innovations

2021 ◽  
Vol 7 (1) ◽  
pp. 32
Author(s):  
Victor L. Shabanov ◽  
Marianna Ya Vasilchenko ◽  
Elena A. Derunova ◽  
Andrey P. Potapov
Keyword(s):  
Agricultural Economy ◽  
Institutional Management ◽  
Agricultural Exports ◽  
Modeling Methodology ◽  
Fixed Assets ◽  
Analysis Application ◽  
The Impact ◽  
The Relationship

The aim of the work is to find relevant indicators for assessing the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports using tools for modeling the impact of innovation and investment development on increasing production and export potential in the context of the formation of an export-oriented agricultural economy. The modeling methodology and the proposed estimating and forecasting tools for diagnosing and monitoring the state of sectoral and regional innovative agricultural systems are used to analyze the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports based on the construction of the classification of Russian regions by factors that aggregate these features to diagnose incongruence problems and to improve institutional management in regional innovative export-oriented agrosystems. Based on the results of the factor analysis application, an underestimated role of indicators of investment in agriculture, the intensity and efficiency of agricultural production, were established. Based on the results of the cluster analysis, the established five groups of regions were identified, with significant differences in the level of investment in agriculture, the volume of production of the main types of agricultural products, and the export and exported food. The research results are of practical value for use in improving institutional management when planning reforms and transformations of regional innovative agrosystems.

Sustainability assessment in the manufacturing industry: a review of recent studies

2018 ◽  
Vol 25 (8) ◽  
pp. 3162-3179 ◽  
Author(s):  
Shamraiz Ahmad ◽  
Kuan Yew Wong
Keyword(s):  
Manufacturing Industry ◽  
Sustainability Assessment ◽  
Manufacturing Sector ◽  
Social Indicators ◽  
Sustainable Manufacturing ◽  
Manufacturing Industries ◽  
Bottom Line ◽  
Content Type ◽  
The Status ◽  
Different Levels

Purpose The purpose of this paper is to review and analyze the recent sustainability assessment studies in the manufacturing industry from the triple-bottom-line (TBL) perspective. This paper aims to depict the status quo of practical sustainability assessment, summarize the different levels and boundaries of evaluation, and highlight the difficulties and further improvements needed to make the assessment more effective in the manufacturing industry. Design/methodology/approach Four keywords, namely, sustainability assessment, sustainable manufacturing, TBL and green production, were used to explore and find the relevant articles. First, this paper systematically reviewed the studies and analyzed the different levels and boundaries of sustainability assessment. Following this, the reviewed studies were critically discussed along with their merits and shortcomings. Findings The review showed that most of the sustainability assessment studies were conducted on product, company and process levels in the manufacturing industry. Nevertheless, there is still a need to focus more on plant and process level assessments to achieve the TBL objectives. Environmental assessment is comparatively matured in manufacturing industries. However, from the economic and social viewpoints, only cost analysis and workers’ safety, respectively, were considered in most of the studies. The economic and social indicators need to be more inclusive and should be validated and standardized for manufacturing industries. Originality/value Unlike previous sustainability assessment reviews in manufacturing industries which were mostly based on life cycle assessment, this paper has included environmental, social and economic aspects in one comprehensive review and focused on recent studies published from 2010 to 2017. This paper has explored the recent sustainability assessment trends and provided insights into the development of sustainability assessment in the manufacturing sector.

scholarly journals Is it possible to register the ideas, concerns and expectations behind the reason for encounter as a means of classifying patient preferences with ICPC-2?

2017 ◽  
Vol 19 (01) ◽  
pp. 1-6 ◽  
Author(s):  
Diego Schrans ◽  
Pauline Boeckxstaens ◽  
An De Sutter ◽  
Sara Willems ◽  
Dirk Avonts ◽  
...  
Keyword(s):  
Primary Care ◽  
Patient Preferences ◽  
Time Frame ◽  
Specific Procedure ◽  
Reason For Encounter ◽  
Related Information ◽  
Almost All ◽  
Danger To Others ◽  
Do So

BackgroundFamily practice aims to recognize the health problems and needs expressed by the person rather than only focusing on the disease. Documenting person-related information will facilitate both the understanding and delivery of person-focused care.AimTo explore if the patients’ ideas, concerns and expectations (ICE) behind the reason for encounter (RFE) can be coded with the International Classification of Primary Care, version 2 (ICPC-2) and what kinds of codes are missing to be able to do so.MethodsIn total, 613 consultations were observed, and patients’ expressions of ICE were narratively recorded. These descriptions were consequently translated to ICPC codes by two researchers. Descriptions that could not be translated were qualitatively analysed in order to identify gaps in ICPC-2.ResultsIn all, 613 consultations yielded 672 ICE expressions. Within the 123 that could not be coded with ICPC-2, eight categories could be defined: concern about the duration/time frame; concern about the evolution/severity; concern of being contagious or a danger to others; patient has no concern, but others do; expects a confirmation of something; expects a solution for the symptoms without specification of what it should be; expects a specific procedure; and expects that something is not done.DiscussionAlthough many ICE can be registered with ICPC-2, adding eight new categories would capture almost all ICE.

A chemical–mineralogical classification of common plutonic rocks and associations

1983 ◽  
Vol 73 (3) ◽  
pp. 135-149 ◽  
Author(s):  
F. Debon ◽  
P. Le Fort
Keyword(s):  
Analytical Data ◽  
Quartz Content ◽  
New Approach ◽  
Mineralogical Characteristics ◽  
Simple Chemical ◽  
Plutonic Rocks ◽  
The Individual ◽  
New Criteria ◽  
Different Levels

ABSTRACTA classification is proposed, based mainly on major element analytical data plotted in a coherent set of three simple chemical-mineralogical diagrams. The procedure follows two complementary steps at two different levels. The first is concerned with the individual sample: the sample is given a name (e.g. granite, adamellite, granodiorite) and its chemical and mineralogical characteristics are determined. The second one is more important: it aims at defining the type of magmatic association (or series) to which the studied sample or group of samples belongs. Three main types of association are distinguished: cafemic (from source-material mainly or completely mantle-derived), aluminous (mainly or completely derived by anatexis of continental crust), and alumino-cafemic (intermediate between the other two types). Subtypes are then distinguished among the cafemic and alumino-cafemic associations: calc-alkaline (or granodioritic), subalkaline (or monzonitic), alkaline (and peralkaline), tholeiitic (or gabbroic-trondhjemitic), etc. In the same way, numerous subtypes and variants are also distinguished among the aluminous associations using a set of complementary criteria such as quartz content, colour index, alkali ratio, quartz–alkalies relationships and alumina index.Although involving a new approach using partly new criteria, this classification is consistent with most of the divisions used in previous typologies. The method may also be used in the classification of the volcanic equivalents of common plutonic rocks.

scholarly journals Discrimination of Genealogical Groups of Arabica Coffee by the Chemical Composition of the Beans

2019 ◽  
Vol 11 (16) ◽  
pp. 141
Author(s):  
Larissa O. Fassio ◽  
Marcelo R. Malta ◽  
Gladyston R. Carvalho ◽  
Antônio A. Pereira ◽  
Ackson D. Silva ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Chemical Composition ◽  
Chemical Markers ◽  
Total Lipids ◽  
Germplasm Bank ◽  
Total Sugars ◽  
Coffee Beans ◽  
Different Levels ◽  
Important Marker

This work aimed to characterize and discriminate genealogical groups of coffee as to the chemical composition of the grains through the model created by PLS-DA method. 22 accessions of Coffea arabica, from the Active Germplasm Bank of Minas Gerais, were divided into groups according to the genealogical origin. Samples of ripe fruits were harvested selectively and processed by the wet method, to obtain pulped coffee beans, with 11% (b.u.) of water content. The raw beans were assessed as to the content of polyphenols, total sugars, total lipids, protein, caffeine, sucrose, and fatty acids. The data were submitted the chemometric analysis, PCA and PLS-DA. The results of PLS-DA identified the variables which most influence the classification of genealogical groups and possible chemical markers to accessions processed by the pulped method. The sucrose content was an important marker for the Exotic accession group. However, the content of polyphenols has been identified as a marker for the group Tymor Hybrid, and the caffeine for the bourbon group. The different fatty acids have been identified as markers for all genealogical groups, at different levels. The model PLS-DA is effective in discriminating genealogical groups from the chemical composition of the beans.

scholarly journals The Worshipers of Stones. Lacandon Sacred Stone Landscape

2020 ◽  
Vol 20 (1) ◽  
pp. 1-27
Author(s):  
Milan Kováč
Keyword(s):  
Archaeological Site ◽  
Theoretical Approaches ◽  
Different Types ◽  
Spiritual Energy ◽  
Different Levels ◽  
Rock Shelters

Abstract This article deals with the Lacandon cosmology, one of the few Maya cosmologies which has been exceptionally structured and until today, very well preserved. The present study is based mainly on associations related to stone. There are investigated the emic classifications of the Lacandon. Their classification of divine beings according to their location, and their connection to the stone houses, whether of natural or cultural origin. In the article are analyzed the most sacred Lacandon sites such as the rock shelters, cliffs and caves around the Lake Mensäbäk and Lake Yahaw Petha, as well as Yaxchilan, the archaeological site with the long tradition of Lacandon pilgrimages. The Lacandon believe in different types of transfer of spiritual energy through stone. The stones could be considered on different levels as the seat, heart or embodiment of deities. These relationships and contexts are very complex. The article tries to identify it and to offer some linguistic and theoretical approaches.

scholarly journals Dynamic Encoding of Effort and Reward throughout the Execution of Action by External Globus Pallidus Neurons in Monkeys

2018 ◽  
Vol 30 (8) ◽  
pp. 1130-1144 ◽  
Author(s):  
Simon Nougaret ◽  
Sabrina Ravel
Keyword(s):  
Globus Pallidus ◽  
Related Information ◽  
Motor Information ◽  
Expected Benefits ◽  
External Part ◽  
Types Of Information ◽  
Goal Directed Behavior ◽  
Task Parameters ◽  
Different Levels

Humans and animals must evaluate the costs and expected benefits of their actions to make adaptive choices. Prior studies have demonstrated the involvement of the basal ganglia in this evaluation. However, little is known about the role of the external part of the globus pallidus (GPe), which is well positioned to integrate motor and reward-related information, in this process. To investigate this role, the activity of 126 neurons was recorded in the associative and limbic parts of the GPe of two monkeys performing a behavioral task in which different levels of force were required to obtain different amounts of liquid reward. The results first revealed that the activity of associative and limbic GPe neurons could be modulated not only by cognitive and limbic but also motor information at the same time, both during a single period or during different periods throughout the trial, mainly in an independent way. Moreover, as a population, GPe neurons encoded these types of information dynamically throughout the trial, when each piece of information was the most relevant for the achievement of the action. Taken together, these results suggest that GPe neurons could be dedicated to the parallel monitoring of task parameters essential to adjusting and maintaining goal-directed behavior.

Data Science for Business Analytics and Business Intelligence

2021 ◽  
pp. 495-508
Author(s):  
Matthias Lederer ◽  
Patrick Schmid
Keyword(s):  
Business Intelligence ◽  
Data Science ◽  
Digital Data ◽  
Competitive Advantages ◽  
Business Analytics ◽  
Simulation And Optimization ◽  
Analytical Skills ◽  
Methods And Techniques ◽  
Different Levels

Data science as the interdisciplinary collection of methods and techniques to support businesses is becoming more and more popular. This article begins with definitions and shows how systematically competitive advantages can be built up on the basis of digital data. Essential sources and types of data-driven knowledge are introduced. Then a classification of approaches of data science concepts is explained. A distinction is made between Business Analytics and Business Intelligence as different levels of analytical skills. The paper goes into depth with these concepts and presents concrete techniques, algorithms, and application scenarios. Thus, the contribution introduces State of the Art approaches to analysis, control, monitoring but also to advanced approaches such as prediction, simulation, and optimization.

scholarly journals Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 83 ◽  
Author(s):  
Giannis Haralabopoulos ◽  
Ioannis Anagnostopoulos ◽  
Derek McAuley
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Ensemble Learning ◽  
Affective Computing ◽  
Binary Classification ◽  
User Generated Content ◽  
Emotional Information ◽  
Multiple Classifiers ◽  
Different Levels

Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5 % to 5.4 % .

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