Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation
Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual d...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Geraldi, Matheus Soares [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion - Solanki, Nayan ELSEVIER, 2017, the international journal, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:244 ; year:2022 ; day:1 ; month:04 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.energy.2022.123161 |
|---|
| Katalog-ID: |
ELV056892675 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV056892675 | ||
| 003 | DE-627 | ||
| 005 | 20230626044133.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220808s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.energy.2022.123161 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001871.pica |
| 035 | |a (DE-627)ELV056892675 | ||
| 035 | |a (ELSEVIER)S0360-5442(22)00064-0 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 15,3 |2 ssgn | ||
| 084 | |a PHARM |q DE-84 |2 fid | ||
| 084 | |a 44.40 |2 bkl | ||
| 100 | 1 | |a Geraldi, Matheus Soares |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation |
| 264 | 1 | |c 2022transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. | ||
| 520 | |a Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. | ||
| 650 | 7 | |a School buildings |2 Elsevier | |
| 650 | 7 | |a Building performance analysis |2 Elsevier | |
| 650 | 7 | |a Building energy performance |2 Elsevier | |
| 650 | 7 | |a Energy efficiency |2 Elsevier | |
| 650 | 7 | |a Energy benchmarking |2 Elsevier | |
| 700 | 1 | |a Ghisi, Enedir |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Solanki, Nayan ELSEVIER |t Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion |d 2017 |d the international journal |g Amsterdam [u.a.] |w (DE-627)ELV000529575 |
| 773 | 1 | 8 | |g volume:244 |g year:2022 |g day:1 |g month:04 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.energy.2022.123161 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a FID-PHARM | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a SSG-OPC-PHA | ||
| 936 | b | k | |a 44.40 |j Pharmazie |j Pharmazeutika |q VZ |
| 951 | |a AR | ||
| 952 | |d 244 |j 2022 |b 1 |c 0401 |h 0 | ||
| author_variant |
m s g ms msg |
|---|---|
| matchkey_str |
geraldimatheussoaresghisienedir:2022----:nertneiecbsdhrastsatoieegbnhaknaaarvnpra |
| hierarchy_sort_str |
2022transfer abstract |
| bklnumber |
44.40 |
| publishDate |
2022 |
| allfields |
10.1016/j.energy.2022.123161 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001871.pica (DE-627)ELV056892675 (ELSEVIER)S0360-5442(22)00064-0 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Geraldi, Matheus Soares verfasserin aut Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. School buildings Elsevier Building performance analysis Elsevier Building energy performance Elsevier Energy efficiency Elsevier Energy benchmarking Elsevier Ghisi, Enedir oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:244 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.energy.2022.123161 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 244 2022 1 0401 0 |
| spelling |
10.1016/j.energy.2022.123161 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001871.pica (DE-627)ELV056892675 (ELSEVIER)S0360-5442(22)00064-0 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Geraldi, Matheus Soares verfasserin aut Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. School buildings Elsevier Building performance analysis Elsevier Building energy performance Elsevier Energy efficiency Elsevier Energy benchmarking Elsevier Ghisi, Enedir oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:244 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.energy.2022.123161 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 244 2022 1 0401 0 |
| allfields_unstemmed |
10.1016/j.energy.2022.123161 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001871.pica (DE-627)ELV056892675 (ELSEVIER)S0360-5442(22)00064-0 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Geraldi, Matheus Soares verfasserin aut Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. School buildings Elsevier Building performance analysis Elsevier Building energy performance Elsevier Energy efficiency Elsevier Energy benchmarking Elsevier Ghisi, Enedir oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:244 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.energy.2022.123161 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 244 2022 1 0401 0 |
| allfieldsGer |
10.1016/j.energy.2022.123161 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001871.pica (DE-627)ELV056892675 (ELSEVIER)S0360-5442(22)00064-0 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Geraldi, Matheus Soares verfasserin aut Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. School buildings Elsevier Building performance analysis Elsevier Building energy performance Elsevier Energy efficiency Elsevier Energy benchmarking Elsevier Ghisi, Enedir oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:244 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.energy.2022.123161 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 244 2022 1 0401 0 |
| allfieldsSound |
10.1016/j.energy.2022.123161 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001871.pica (DE-627)ELV056892675 (ELSEVIER)S0360-5442(22)00064-0 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Geraldi, Matheus Soares verfasserin aut Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. School buildings Elsevier Building performance analysis Elsevier Building energy performance Elsevier Energy efficiency Elsevier Energy benchmarking Elsevier Ghisi, Enedir oth Enthalten in Elsevier Science Solanki, Nayan ELSEVIER Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion 2017 the international journal Amsterdam [u.a.] (DE-627)ELV000529575 volume:244 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.energy.2022.123161 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA 44.40 Pharmazie Pharmazeutika VZ AR 244 2022 1 0401 0 |
| language |
English |
| source |
Enthalten in Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion Amsterdam [u.a.] volume:244 year:2022 day:1 month:04 pages:0 |
| sourceStr |
Enthalten in Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion Amsterdam [u.a.] volume:244 year:2022 day:1 month:04 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Pharmazie Pharmazeutika |
| institution |
findex.gbv.de |
| topic_facet |
School buildings Building performance analysis Building energy performance Energy efficiency Energy benchmarking |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion |
| authorswithroles_txt_mv |
Geraldi, Matheus Soares @@aut@@ Ghisi, Enedir @@oth@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
ELV000529575 |
| dewey-sort |
3610 |
| id |
ELV056892675 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV056892675</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626044133.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.energy.2022.123161</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001871.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV056892675</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0360-5442(22)00064-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">15,3</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">PHARM</subfield><subfield code="q">DE-84</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.40</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Geraldi, Matheus Soares</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">School buildings</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Building performance analysis</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Building energy performance</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Energy efficiency</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Energy benchmarking</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ghisi, Enedir</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Solanki, Nayan ELSEVIER</subfield><subfield code="t">Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion</subfield><subfield code="d">2017</subfield><subfield code="d">the international journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV000529575</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:244</subfield><subfield code="g">year:2022</subfield><subfield code="g">day:1</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.energy.2022.123161</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-PHARM</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.40</subfield><subfield code="j">Pharmazie</subfield><subfield code="j">Pharmazeutika</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">244</subfield><subfield code="j">2022</subfield><subfield code="b">1</subfield><subfield code="c">0401</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Geraldi, Matheus Soares |
| spellingShingle |
Geraldi, Matheus Soares ddc 610 ssgn 15,3 fid PHARM bkl 44.40 Elsevier School buildings Elsevier Building performance analysis Elsevier Building energy performance Elsevier Energy efficiency Elsevier Energy benchmarking Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation |
| authorStr |
Geraldi, Matheus Soares |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV000529575 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation School buildings Elsevier Building performance analysis Elsevier Building energy performance Elsevier Energy efficiency Elsevier Energy benchmarking Elsevier |
| topic |
ddc 610 ssgn 15,3 fid PHARM bkl 44.40 Elsevier School buildings Elsevier Building performance analysis Elsevier Building energy performance Elsevier Energy efficiency Elsevier Energy benchmarking |
| topic_unstemmed |
ddc 610 ssgn 15,3 fid PHARM bkl 44.40 Elsevier School buildings Elsevier Building performance analysis Elsevier Building energy performance Elsevier Energy efficiency Elsevier Energy benchmarking |
| topic_browse |
ddc 610 ssgn 15,3 fid PHARM bkl 44.40 Elsevier School buildings Elsevier Building performance analysis Elsevier Building energy performance Elsevier Energy efficiency Elsevier Energy benchmarking |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
e g eg |
| hierarchy_parent_title |
Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion |
| hierarchy_parent_id |
ELV000529575 |
| dewey-tens |
610 - Medicine & health |
| hierarchy_top_title |
Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV000529575 |
| title |
Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation |
| ctrlnum |
(DE-627)ELV056892675 (ELSEVIER)S0360-5442(22)00064-0 |
| title_full |
Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation |
| author_sort |
Geraldi, Matheus Soares |
| journal |
Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion |
| journalStr |
Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Geraldi, Matheus Soares |
| container_volume |
244 |
| class |
610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Geraldi, Matheus Soares |
| doi_str_mv |
10.1016/j.energy.2022.123161 |
| dewey-full |
610 |
| title_sort |
integrating evidence-based thermal satisfaction in energy benchmarking: a data-driven approach for a whole-building evaluation |
| title_auth |
Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation |
| abstract |
Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. |
| abstractGer |
Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. |
| abstract_unstemmed |
Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA |
| title_short |
Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation |
| url |
https://doi.org/10.1016/j.energy.2022.123161 |
| remote_bool |
true |
| author2 |
Ghisi, Enedir |
| author2Str |
Ghisi, Enedir |
| ppnlink |
ELV000529575 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1016/j.energy.2022.123161 |
| up_date |
2024-07-06T21:41:30.543Z |
| _version_ |
1803867484895313920 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV056892675</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626044133.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.energy.2022.123161</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001871.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV056892675</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0360-5442(22)00064-0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">15,3</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">PHARM</subfield><subfield code="q">DE-84</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.40</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Geraldi, Matheus Soares</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Energy benchmarking are used to compare the operational performance of buildings with the corresponding stock. Multi-criteria methods emerged to consider different factors in benchmarking assessment. However, there is a lack in considering occupants’ thermal satisfaction in methods based on actual data. The objective of this article is to propose a method to integrate thermal satisfaction into energy benchmarking. The main innovation is to propose a probabilistic metric that takes into account energy consumption, construction aspects, climate conditions, systems and thermal satisfaction level to benchmark a building. The method consists of a statistical analysis to select relevant variables in the building stock, the process of discretisation of such variables, and the developing and validation of a Bayesian Network to serve as an instrument for the benchmarking method. A detailed evidence-based dataset of 426 schools in Brazil was used. Results showed that buildings with low thermal satisfaction of occupants were benchmarked as less efficient than those with high thermal satisfaction and similar energy consumption. Regarding the validation step, the benchmarking model achieved an error rate ranging from 17.78% to 29.17%. The main conclusion is that machine learning techniques can adequately integrate subjective aspects such as occupant satisfaction in data-driven energy benchmarking methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">School buildings</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Building performance analysis</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Building energy performance</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Energy efficiency</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Energy benchmarking</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ghisi, Enedir</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Solanki, Nayan ELSEVIER</subfield><subfield code="t">Rheological analysis of itraconazole-polymer mixtures to determine optimal melt extrusion temperature for development of amorphous solid dispersion</subfield><subfield code="d">2017</subfield><subfield code="d">the international journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV000529575</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:244</subfield><subfield code="g">year:2022</subfield><subfield code="g">day:1</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.energy.2022.123161</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-PHARM</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.40</subfield><subfield code="j">Pharmazie</subfield><subfield code="j">Pharmazeutika</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">244</subfield><subfield code="j">2022</subfield><subfield code="b">1</subfield><subfield code="c">0401</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.399441 |