This study proposes the replacement of the conventional straight absorber by the newly designed longitudinally undulated. Numerical results revealed that the new absorber could dethrone the former for several reasons: Among others, it allows a more homogeneous distribution of concentrated solar radiations on its outer surface (Monte Carlo Ray Tracking results); unlike other techniques which improve the inner heat transfer by increasing simultaneously the load of the absorber and the pressure drop within it, the proposed curved absorber is going to generate in a natural way, without any additional mechanical components, vortices within the main streaming which allowed to increase the heat transfer coefficient of about 63 % with an increase of the pressure drop penalty of about 60 %. On the other hand, it allows a drastic reduction of the size of the solar collector field. All these facts lead to decreasing the wall temperature gradient bellow 40 K. Results are obtained for the Syltherm 800 Reynolds number range 2.5×104 to 12.3×104 and a fluid inlet temperature of 450 K.

This study proposes the replacement of the conventional straight absorber by the newly designed longitudinally undulated. Numerical results revealed that the new absorber could dethrone the former for several reasons: Among others, it allows a more homogeneous distribution of concentrated solar radiations on its outer surface (Monte Carlo Ray Tracking results); unlike other techniques which improve the inner heat transfer by increasing simultaneously the load of the absorber and the pressure drop within it, the proposed curved absorber is going to generate in a natural way, without any additional mechanical components, vortices within the main streaming which allowed to increase the heat transfer coefficient of about 63 % with an increase of the pressure drop penalty of about 60 %. On the other hand, it allows a drastic reduction of the size of the solar collector field. All these facts lead to decreasing the wall temperature gradient bellow 40 K. Results are obtained for the Syltherm 800 Reynolds number range 2.5×104 to 12.3×104 and a fluid inlet temperature of 450 K.

The aim of this paper is to perform a keyword analysis in two areas of research: Intellectual Capital and Knowledge Management. The keywords are of three types : keywords proposed by the authors in their articles, the keywords that users use in their queries and the densest words existing in a textual corpus. Zipf’s law usually applied for natural language is applied in this work for scientifc corpus constituted of confused full articles in each area. We wrote 8 R programs going through titles of articles, authors’ keywords, abstracts and full articles to calculate frequencies and interpret them. The keywords of intellectual capital measurement and diclosure have the highest frequencies. The measures are stated by companies in annual reports and could not be integrated in their balance sheets because the classical accounting does not take into acount intellectual capital as an asset. Knowledge management is more oriented towards the capitalization of knowledge to improve business performance and for industries. This work is the first in keyword analysis for the two areas. It could be usefull to prepare glossaries, ontologies and all semantic reasearhes of research areas.

The aim of this paper is to perform a keyword analysis in two areas of research: Intellectual Capital and Knowledge Management. The keywords are of three types : keywords proposed by the authors in their articles, the keywords that users use in their queries and the densest words existing in a textual corpus. Zipf’s law usually applied for natural language is applied in this work for scientifc corpus constituted of confused full articles in each area. We wrote 8 R programs going through titles of articles, authors’ keywords, abstracts and full articles to calculate frequencies and interpret them. The keywords of intellectual capital measurement and diclosure have the highest frequencies. The measures are stated by companies in annual reports and could not be integrated in their balance sheets because the classical accounting does not take into acount intellectual capital as an asset. Knowledge management is more oriented towards the capitalization of knowledge to improve business performance and for industries. This work is the first in keyword analysis for the two areas. It could be usefull to prepare glossaries, ontologies and all semantic reasearhes of research areas.

The aim of this paper is to perform a keyword analysis in two areas of research: Intellectual Capital and Knowledge Management. The keywords are of three types : keywords proposed by the authors in their articles, the keywords that users use in their queries and the densest words existing in a textual corpus. Zipf’s law usually applied for natural language is applied in this work for scientifc corpus constituted of confused full articles in each area. We wrote 8 R programs going through titles of articles, authors’ keywords, abstracts and full articles to calculate frequencies and interpret them. The keywords of intellectual capital measurement and diclosure have the highest frequencies. The measures are stated by companies in annual reports and could not be integrated in their balance sheets because the classical accounting does not take into acount intellectual capital as an asset. Knowledge management is more oriented towards the capitalization of knowledge to improve business performance and for industries. This work is the first in keyword analysis for the two areas. It could be usefull to prepare glossaries, ontologies and all semantic reasearhes of research areas.

The aim of this paper is to perform a keyword analysis in two areas of research: Intellectual Capital and Knowledge Management. The keywords are of three types : keywords proposed by the authors in their articles, the keywords that users use in their queries and the densest words existing in a textual corpus. Zipf’s law usually applied for natural language is applied in this work for scientifc corpus constituted of confused full articles in each area. We wrote 8 R programs going through titles of articles, authors’ keywords, abstracts and full articles to calculate frequencies and interpret them. The keywords of intellectual capital measurement and diclosure have the highest frequencies. The measures are stated by companies in annual reports and could not be integrated in their balance sheets because the classical accounting does not take into acount intellectual capital as an asset. Knowledge management is more oriented towards the capitalization of knowledge to improve business performance and for industries. This work is the first in keyword analysis for the two areas. It could be usefull to prepare glossaries, ontologies and all semantic reasearhes of research areas.

The aim of this paper is to perform a keyword analysis in two areas of research: Intellectual Capital and Knowledge Management. The keywords are of three types : keywords proposed by the authors in their articles, the keywords that users use in their queries and the densest words existing in a textual corpus. Zipf’s law usually applied for natural language is applied in this work for scientifc corpus constituted of confused full articles in each area. We wrote 8 R programs going through titles of articles, authors’ keywords, abstracts and full articles to calculate frequencies and interpret them. The keywords of intellectual capital measurement and diclosure have the highest frequencies. The measures are stated by companies in annual reports and could not be integrated in their balance sheets because the classical accounting does not take into acount intellectual capital as an asset. Knowledge management is more oriented towards the capitalization of knowledge to improve business performance and for industries. This work is the first in keyword analysis for the two areas. It could be usefull to prepare glossaries, ontologies and all semantic reasearhes of research areas.

The aim of this paper is to perform a keyword analysis in two areas of research: Intellectual Capital and Knowledge Management. The keywords are of three types : keywords proposed by the authors in their articles, the keywords that users use in their queries and the densest words existing in a textual corpus. Zipf’s law usually applied for natural language is applied in this work for scientifc corpus constituted of confused full articles in each area. We wrote 8 R programs going through titles of articles, authors’ keywords, abstracts and full articles to calculate frequencies and interpret them. The keywords of intellectual capital measurement and diclosure have the highest frequencies. The measures are stated by companies in annual reports and could not be integrated in their balance sheets because the classical accounting does not take into acount intellectual capital as an asset. Knowledge management is more oriented towards the capitalization of knowledge to improve business performance and for industries. This work is the first in keyword analysis for the two areas. It could be usefull to prepare glossaries, ontologies and all semantic reasearhes of research areas.

The aim of this paper is to perform a keyword analysis in two areas of research: Intellectual Capital and Knowledge Management. The keywords are of three types : keywords proposed by the authors in their articles, the keywords that users use in their queries and the densest words existing in a textual corpus. Zipf’s law usually applied for natural language is applied in this work for scientifc corpus constituted of confused full articles in each area. We wrote 8 R programs going through titles of articles, authors’ keywords, abstracts and full articles to calculate frequencies and interpret them. The keywords of intellectual capital measurement and diclosure have the highest frequencies. The measures are stated by companies in annual reports and could not be integrated in their balance sheets because the classical accounting does not take into acount intellectual capital as an asset. Knowledge management is more oriented towards the capitalization of knowledge to improve business performance and for industries. This work is the first in keyword analysis for the two areas. It could be usefull to prepare glossaries, ontologies and all semantic reasearhes of research areas.