A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel
Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaus...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yusri, I.M. [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
22 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease - Soke, Fatih ELSEVIER, 2019, an international journal, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:90 ; year:2018 ; pages:665-686 ; extent:22 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.rser.2018.03.095 |
|---|
| Katalog-ID: |
ELV04314487X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV04314487X | ||
| 003 | DE-627 | ||
| 005 | 20230626003222.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180726s2018 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.rser.2018.03.095 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001033.pica |
| 035 | |a (DE-627)ELV04314487X | ||
| 035 | |a (ELSEVIER)S1364-0321(18)30191-6 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 44.90 |2 bkl | ||
| 084 | |a 44.65 |2 bkl | ||
| 100 | 1 | |a Yusri, I.M. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel |
| 264 | 1 | |c 2018transfer abstract | |
| 300 | |a 22 | ||
| 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 Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. | ||
| 520 | |a Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. | ||
| 650 | 7 | |a CO2 |2 Elsevier | |
| 650 | 7 | |a HCCI |2 Elsevier | |
| 650 | 7 | |a WCO |2 Elsevier | |
| 650 | 7 | |a LR |2 Elsevier | |
| 650 | 7 | |a GHG |2 Elsevier | |
| 650 | 7 | |a n-butanol |2 Elsevier | |
| 650 | 7 | |a MLP |2 Elsevier | |
| 650 | 7 | |a GBu10 |2 Elsevier | |
| 650 | 7 | |a WTO |2 Elsevier | |
| 650 | 7 | |a trainbfg |2 Elsevier | |
| 650 | 7 | |a GBu15 |2 Elsevier | |
| 650 | 7 | |a SHL |2 Elsevier | |
| 650 | 7 | |a RBF |2 Elsevier | |
| 650 | 7 | |a UN |2 Elsevier | |
| 650 | 7 | |a bTDC |2 Elsevier | |
| 650 | 7 | |a trainscg |2 Elsevier | |
| 650 | 7 | |a CAD HRRmax |2 Elsevier | |
| 650 | 7 | |a NSE |2 Elsevier | |
| 650 | 7 | |a traingdx |2 Elsevier | |
| 650 | 7 | |a RSE |2 Elsevier | |
| 650 | 7 | |a trainrp |2 Elsevier | |
| 650 | 7 | |a AI |2 Elsevier | |
| 650 | 7 | |a BMEP |2 Elsevier | |
| 650 | 7 | |a MAPE |2 Elsevier | |
| 650 | 7 | |a RSM |2 Elsevier | |
| 650 | 7 | |a EU |2 Elsevier | |
| 650 | 7 | |a BTE |2 Elsevier | |
| 650 | 7 | |a HnOME |2 Elsevier | |
| 650 | 7 | |a CAD Pmax |2 Elsevier | |
| 650 | 7 | |a GNA |2 Elsevier | |
| 650 | 7 | |a BDC |2 Elsevier | |
| 650 | 7 | |a FF |2 Elsevier | |
| 650 | 7 | |a ANN |2 Elsevier | |
| 650 | 7 | |a DLS |2 Elsevier | |
| 650 | 7 | |a AFR |2 Elsevier | |
| 650 | 7 | |a purelin |2 Elsevier | |
| 650 | 7 | |a Logsig |2 Elsevier | |
| 650 | 7 | |a CuHRR |2 Elsevier | |
| 650 | 7 | |a BSFC |2 Elsevier | |
| 650 | 7 | |a Tansig |2 Elsevier | |
| 650 | 7 | |a TDC |2 Elsevier | |
| 650 | 7 | |a SI |2 Elsevier | |
| 650 | 7 | |a THL |2 Elsevier | |
| 650 | 7 | |a GBu5 |2 Elsevier | |
| 650 | 7 | |a LHV |2 Elsevier | |
| 650 | 7 | |a MSE |2 Elsevier | |
| 650 | 7 | |a ECI-multi |2 Elsevier | |
| 650 | 7 | |a CI |2 Elsevier | |
| 650 | 7 | |a KGE |2 Elsevier | |
| 650 | 7 | |a PME |2 Elsevier | |
| 650 | 7 | |a COV |2 Elsevier | |
| 650 | 7 | |a G100 |2 Elsevier | |
| 650 | 7 | |a RMSE |2 Elsevier | |
| 650 | 7 | |a CO |2 Elsevier | |
| 650 | 7 | |a FIP |2 Elsevier | |
| 650 | 7 | |a ppm |2 Elsevier | |
| 650 | 7 | |a IMEP |2 Elsevier | |
| 650 | 7 | |a rpm |2 Elsevier | |
| 650 | 7 | |a NOx |2 Elsevier | |
| 650 | 7 | |a FIT |2 Elsevier | |
| 650 | 7 | |a SOHC |2 Elsevier | |
| 650 | 7 | |a HC |2 Elsevier | |
| 650 | 7 | |a 2-butanol |2 Elsevier | |
| 650 | 7 | |a BVP |2 Elsevier | |
| 650 | 7 | |a DA |2 Elsevier | |
| 650 | 7 | |a MSRE |2 Elsevier | |
| 700 | 1 | |a Abdul Majeed, A.P.P. |4 oth | |
| 700 | 1 | |a Mamat, R. |4 oth | |
| 700 | 1 | |a Ghazali, M.F. |4 oth | |
| 700 | 1 | |a Awad, Omar I. |4 oth | |
| 700 | 1 | |a Azmi, W.H. |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Soke, Fatih ELSEVIER |t Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease |d 2019 |d an international journal |g Amsterdam [u.a.] |w (DE-627)ELV003073483 |
| 773 | 1 | 8 | |g volume:90 |g year:2018 |g pages:665-686 |g extent:22 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.rser.2018.03.095 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 44.90 |j Neurologie |q VZ |
| 936 | b | k | |a 44.65 |j Chirurgie |q VZ |
| 951 | |a AR | ||
| 952 | |d 90 |j 2018 |h 665-686 |g 22 | ||
| author_variant |
i y iy |
|---|---|
| matchkey_str |
yusriimabdulmajeedappmamatrghazalimfawad:2018----:rveoteplctoorsossraeehdnatfcanuantoknnieefracadxasei |
| hierarchy_sort_str |
2018transfer abstract |
| bklnumber |
44.90 44.65 |
| publishDate |
2018 |
| allfields |
10.1016/j.rser.2018.03.095 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001033.pica (DE-627)ELV04314487X (ELSEVIER)S1364-0321(18)30191-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl 44.65 bkl Yusri, I.M. verfasserin aut A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel 2018transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. CO2 Elsevier HCCI Elsevier WCO Elsevier LR Elsevier GHG Elsevier n-butanol Elsevier MLP Elsevier GBu10 Elsevier WTO Elsevier trainbfg Elsevier GBu15 Elsevier SHL Elsevier RBF Elsevier UN Elsevier bTDC Elsevier trainscg Elsevier CAD HRRmax Elsevier NSE Elsevier traingdx Elsevier RSE Elsevier trainrp Elsevier AI Elsevier BMEP Elsevier MAPE Elsevier RSM Elsevier EU Elsevier BTE Elsevier HnOME Elsevier CAD Pmax Elsevier GNA Elsevier BDC Elsevier FF Elsevier ANN Elsevier DLS Elsevier AFR Elsevier purelin Elsevier Logsig Elsevier CuHRR Elsevier BSFC Elsevier Tansig Elsevier TDC Elsevier SI Elsevier THL Elsevier GBu5 Elsevier LHV Elsevier MSE Elsevier ECI-multi Elsevier CI Elsevier KGE Elsevier PME Elsevier COV Elsevier G100 Elsevier RMSE Elsevier CO Elsevier FIP Elsevier ppm Elsevier IMEP Elsevier rpm Elsevier NOx Elsevier FIT Elsevier SOHC Elsevier HC Elsevier 2-butanol Elsevier BVP Elsevier DA Elsevier MSRE Elsevier Abdul Majeed, A.P.P. oth Mamat, R. oth Ghazali, M.F. oth Awad, Omar I. oth Azmi, W.H. oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:90 year:2018 pages:665-686 extent:22 https://doi.org/10.1016/j.rser.2018.03.095 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 90 2018 665-686 22 |
| spelling |
10.1016/j.rser.2018.03.095 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001033.pica (DE-627)ELV04314487X (ELSEVIER)S1364-0321(18)30191-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl 44.65 bkl Yusri, I.M. verfasserin aut A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel 2018transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. CO2 Elsevier HCCI Elsevier WCO Elsevier LR Elsevier GHG Elsevier n-butanol Elsevier MLP Elsevier GBu10 Elsevier WTO Elsevier trainbfg Elsevier GBu15 Elsevier SHL Elsevier RBF Elsevier UN Elsevier bTDC Elsevier trainscg Elsevier CAD HRRmax Elsevier NSE Elsevier traingdx Elsevier RSE Elsevier trainrp Elsevier AI Elsevier BMEP Elsevier MAPE Elsevier RSM Elsevier EU Elsevier BTE Elsevier HnOME Elsevier CAD Pmax Elsevier GNA Elsevier BDC Elsevier FF Elsevier ANN Elsevier DLS Elsevier AFR Elsevier purelin Elsevier Logsig Elsevier CuHRR Elsevier BSFC Elsevier Tansig Elsevier TDC Elsevier SI Elsevier THL Elsevier GBu5 Elsevier LHV Elsevier MSE Elsevier ECI-multi Elsevier CI Elsevier KGE Elsevier PME Elsevier COV Elsevier G100 Elsevier RMSE Elsevier CO Elsevier FIP Elsevier ppm Elsevier IMEP Elsevier rpm Elsevier NOx Elsevier FIT Elsevier SOHC Elsevier HC Elsevier 2-butanol Elsevier BVP Elsevier DA Elsevier MSRE Elsevier Abdul Majeed, A.P.P. oth Mamat, R. oth Ghazali, M.F. oth Awad, Omar I. oth Azmi, W.H. oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:90 year:2018 pages:665-686 extent:22 https://doi.org/10.1016/j.rser.2018.03.095 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 90 2018 665-686 22 |
| allfields_unstemmed |
10.1016/j.rser.2018.03.095 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001033.pica (DE-627)ELV04314487X (ELSEVIER)S1364-0321(18)30191-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl 44.65 bkl Yusri, I.M. verfasserin aut A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel 2018transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. CO2 Elsevier HCCI Elsevier WCO Elsevier LR Elsevier GHG Elsevier n-butanol Elsevier MLP Elsevier GBu10 Elsevier WTO Elsevier trainbfg Elsevier GBu15 Elsevier SHL Elsevier RBF Elsevier UN Elsevier bTDC Elsevier trainscg Elsevier CAD HRRmax Elsevier NSE Elsevier traingdx Elsevier RSE Elsevier trainrp Elsevier AI Elsevier BMEP Elsevier MAPE Elsevier RSM Elsevier EU Elsevier BTE Elsevier HnOME Elsevier CAD Pmax Elsevier GNA Elsevier BDC Elsevier FF Elsevier ANN Elsevier DLS Elsevier AFR Elsevier purelin Elsevier Logsig Elsevier CuHRR Elsevier BSFC Elsevier Tansig Elsevier TDC Elsevier SI Elsevier THL Elsevier GBu5 Elsevier LHV Elsevier MSE Elsevier ECI-multi Elsevier CI Elsevier KGE Elsevier PME Elsevier COV Elsevier G100 Elsevier RMSE Elsevier CO Elsevier FIP Elsevier ppm Elsevier IMEP Elsevier rpm Elsevier NOx Elsevier FIT Elsevier SOHC Elsevier HC Elsevier 2-butanol Elsevier BVP Elsevier DA Elsevier MSRE Elsevier Abdul Majeed, A.P.P. oth Mamat, R. oth Ghazali, M.F. oth Awad, Omar I. oth Azmi, W.H. oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:90 year:2018 pages:665-686 extent:22 https://doi.org/10.1016/j.rser.2018.03.095 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 90 2018 665-686 22 |
| allfieldsGer |
10.1016/j.rser.2018.03.095 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001033.pica (DE-627)ELV04314487X (ELSEVIER)S1364-0321(18)30191-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl 44.65 bkl Yusri, I.M. verfasserin aut A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel 2018transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. CO2 Elsevier HCCI Elsevier WCO Elsevier LR Elsevier GHG Elsevier n-butanol Elsevier MLP Elsevier GBu10 Elsevier WTO Elsevier trainbfg Elsevier GBu15 Elsevier SHL Elsevier RBF Elsevier UN Elsevier bTDC Elsevier trainscg Elsevier CAD HRRmax Elsevier NSE Elsevier traingdx Elsevier RSE Elsevier trainrp Elsevier AI Elsevier BMEP Elsevier MAPE Elsevier RSM Elsevier EU Elsevier BTE Elsevier HnOME Elsevier CAD Pmax Elsevier GNA Elsevier BDC Elsevier FF Elsevier ANN Elsevier DLS Elsevier AFR Elsevier purelin Elsevier Logsig Elsevier CuHRR Elsevier BSFC Elsevier Tansig Elsevier TDC Elsevier SI Elsevier THL Elsevier GBu5 Elsevier LHV Elsevier MSE Elsevier ECI-multi Elsevier CI Elsevier KGE Elsevier PME Elsevier COV Elsevier G100 Elsevier RMSE Elsevier CO Elsevier FIP Elsevier ppm Elsevier IMEP Elsevier rpm Elsevier NOx Elsevier FIT Elsevier SOHC Elsevier HC Elsevier 2-butanol Elsevier BVP Elsevier DA Elsevier MSRE Elsevier Abdul Majeed, A.P.P. oth Mamat, R. oth Ghazali, M.F. oth Awad, Omar I. oth Azmi, W.H. oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:90 year:2018 pages:665-686 extent:22 https://doi.org/10.1016/j.rser.2018.03.095 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 90 2018 665-686 22 |
| allfieldsSound |
10.1016/j.rser.2018.03.095 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001033.pica (DE-627)ELV04314487X (ELSEVIER)S1364-0321(18)30191-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl 44.65 bkl Yusri, I.M. verfasserin aut A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel 2018transfer abstract 22 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. CO2 Elsevier HCCI Elsevier WCO Elsevier LR Elsevier GHG Elsevier n-butanol Elsevier MLP Elsevier GBu10 Elsevier WTO Elsevier trainbfg Elsevier GBu15 Elsevier SHL Elsevier RBF Elsevier UN Elsevier bTDC Elsevier trainscg Elsevier CAD HRRmax Elsevier NSE Elsevier traingdx Elsevier RSE Elsevier trainrp Elsevier AI Elsevier BMEP Elsevier MAPE Elsevier RSM Elsevier EU Elsevier BTE Elsevier HnOME Elsevier CAD Pmax Elsevier GNA Elsevier BDC Elsevier FF Elsevier ANN Elsevier DLS Elsevier AFR Elsevier purelin Elsevier Logsig Elsevier CuHRR Elsevier BSFC Elsevier Tansig Elsevier TDC Elsevier SI Elsevier THL Elsevier GBu5 Elsevier LHV Elsevier MSE Elsevier ECI-multi Elsevier CI Elsevier KGE Elsevier PME Elsevier COV Elsevier G100 Elsevier RMSE Elsevier CO Elsevier FIP Elsevier ppm Elsevier IMEP Elsevier rpm Elsevier NOx Elsevier FIT Elsevier SOHC Elsevier HC Elsevier 2-butanol Elsevier BVP Elsevier DA Elsevier MSRE Elsevier Abdul Majeed, A.P.P. oth Mamat, R. oth Ghazali, M.F. oth Awad, Omar I. oth Azmi, W.H. oth Enthalten in Elsevier Science Soke, Fatih ELSEVIER Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease 2019 an international journal Amsterdam [u.a.] (DE-627)ELV003073483 volume:90 year:2018 pages:665-686 extent:22 https://doi.org/10.1016/j.rser.2018.03.095 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ 44.65 Chirurgie VZ AR 90 2018 665-686 22 |
| language |
English |
| source |
Enthalten in Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease Amsterdam [u.a.] volume:90 year:2018 pages:665-686 extent:22 |
| sourceStr |
Enthalten in Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease Amsterdam [u.a.] volume:90 year:2018 pages:665-686 extent:22 |
| format_phy_str_mv |
Article |
| bklname |
Neurologie Chirurgie |
| institution |
findex.gbv.de |
| topic_facet |
CO2 HCCI WCO LR GHG n-butanol MLP GBu10 WTO trainbfg GBu15 SHL RBF UN bTDC trainscg CAD HRRmax NSE traingdx RSE trainrp AI BMEP MAPE RSM EU BTE HnOME CAD Pmax GNA BDC FF ANN DLS AFR purelin Logsig CuHRR BSFC Tansig TDC SI THL GBu5 LHV MSE ECI-multi CI KGE PME COV G100 RMSE CO FIP ppm IMEP rpm NOx FIT SOHC HC 2-butanol BVP DA MSRE |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease |
| authorswithroles_txt_mv |
Yusri, I.M. @@aut@@ Abdul Majeed, A.P.P. @@oth@@ Mamat, R. @@oth@@ Ghazali, M.F. @@oth@@ Awad, Omar I. @@oth@@ Azmi, W.H. @@oth@@ |
| publishDateDaySort_date |
2018-01-01T00:00:00Z |
| hierarchy_top_id |
ELV003073483 |
| dewey-sort |
3610 |
| id |
ELV04314487X |
| 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">ELV04314487X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626003222.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180726s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.rser.2018.03.095</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/GBV00000000001033.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV04314487X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1364-0321(18)30191-6</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">44.90</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.65</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yusri, I.M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">22</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">Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CO2</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">HCCI</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">WCO</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">LR</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GHG</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">n-butanol</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MLP</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GBu10</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">WTO</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">trainbfg</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GBu15</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">SHL</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">RBF</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">UN</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">bTDC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">trainscg</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CAD HRRmax</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">NSE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">traingdx</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">RSE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">trainrp</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">AI</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BMEP</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MAPE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">RSM</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">EU</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BTE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">HnOME</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CAD Pmax</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GNA</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BDC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">FF</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ANN</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">DLS</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">AFR</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">purelin</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Logsig</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CuHRR</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BSFC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Tansig</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">TDC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">SI</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">THL</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GBu5</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">LHV</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MSE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ECI-multi</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CI</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">KGE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">PME</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COV</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">G100</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">RMSE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CO</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">FIP</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ppm</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">IMEP</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">rpm</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">NOx</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">FIT</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">SOHC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">HC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">2-butanol</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BVP</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">DA</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MSRE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Abdul Majeed, A.P.P.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mamat, R.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ghazali, M.F.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Awad, Omar I.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Azmi, W.H.</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">Soke, Fatih ELSEVIER</subfield><subfield code="t">Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease</subfield><subfield code="d">2019</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV003073483</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:90</subfield><subfield code="g">year:2018</subfield><subfield code="g">pages:665-686</subfield><subfield code="g">extent:22</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.rser.2018.03.095</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">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.90</subfield><subfield code="j">Neurologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.65</subfield><subfield code="j">Chirurgie</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">90</subfield><subfield code="j">2018</subfield><subfield code="h">665-686</subfield><subfield code="g">22</subfield></datafield></record></collection>
|
| author |
Yusri, I.M. |
| spellingShingle |
Yusri, I.M. ddc 610 bkl 44.90 bkl 44.65 Elsevier CO2 Elsevier HCCI Elsevier WCO Elsevier LR Elsevier GHG Elsevier n-butanol Elsevier MLP Elsevier GBu10 Elsevier WTO Elsevier trainbfg Elsevier GBu15 Elsevier SHL Elsevier RBF Elsevier UN Elsevier bTDC Elsevier trainscg Elsevier CAD HRRmax Elsevier NSE Elsevier traingdx Elsevier RSE Elsevier trainrp Elsevier AI Elsevier BMEP Elsevier MAPE Elsevier RSM Elsevier EU Elsevier BTE Elsevier HnOME Elsevier CAD Pmax Elsevier GNA Elsevier BDC Elsevier FF Elsevier ANN Elsevier DLS Elsevier AFR Elsevier purelin Elsevier Logsig Elsevier CuHRR Elsevier BSFC Elsevier Tansig Elsevier TDC Elsevier SI Elsevier THL Elsevier GBu5 Elsevier LHV Elsevier MSE Elsevier ECI-multi Elsevier CI Elsevier KGE Elsevier PME Elsevier COV Elsevier G100 Elsevier RMSE Elsevier CO Elsevier FIP Elsevier ppm Elsevier IMEP Elsevier rpm Elsevier NOx Elsevier FIT Elsevier SOHC Elsevier HC Elsevier 2-butanol Elsevier BVP Elsevier DA Elsevier MSRE A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel |
| authorStr |
Yusri, I.M. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV003073483 |
| 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 44.90 bkl 44.65 bkl A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel CO2 Elsevier HCCI Elsevier WCO Elsevier LR Elsevier GHG Elsevier n-butanol Elsevier MLP Elsevier GBu10 Elsevier WTO Elsevier trainbfg Elsevier GBu15 Elsevier SHL Elsevier RBF Elsevier UN Elsevier bTDC Elsevier trainscg Elsevier CAD HRRmax Elsevier NSE Elsevier traingdx Elsevier RSE Elsevier trainrp Elsevier AI Elsevier BMEP Elsevier MAPE Elsevier RSM Elsevier EU Elsevier BTE Elsevier HnOME Elsevier CAD Pmax Elsevier GNA Elsevier BDC Elsevier FF Elsevier ANN Elsevier DLS Elsevier AFR Elsevier purelin Elsevier Logsig Elsevier CuHRR Elsevier BSFC Elsevier Tansig Elsevier TDC Elsevier SI Elsevier THL Elsevier GBu5 Elsevier LHV Elsevier MSE Elsevier ECI-multi Elsevier CI Elsevier KGE Elsevier PME Elsevier COV Elsevier G100 Elsevier RMSE Elsevier CO Elsevier FIP Elsevier ppm Elsevier IMEP Elsevier rpm Elsevier NOx Elsevier FIT Elsevier SOHC Elsevier HC Elsevier 2-butanol Elsevier BVP Elsevier DA Elsevier MSRE Elsevier |
| topic |
ddc 610 bkl 44.90 bkl 44.65 Elsevier CO2 Elsevier HCCI Elsevier WCO Elsevier LR Elsevier GHG Elsevier n-butanol Elsevier MLP Elsevier GBu10 Elsevier WTO Elsevier trainbfg Elsevier GBu15 Elsevier SHL Elsevier RBF Elsevier UN Elsevier bTDC Elsevier trainscg Elsevier CAD HRRmax Elsevier NSE Elsevier traingdx Elsevier RSE Elsevier trainrp Elsevier AI Elsevier BMEP Elsevier MAPE Elsevier RSM Elsevier EU Elsevier BTE Elsevier HnOME Elsevier CAD Pmax Elsevier GNA Elsevier BDC Elsevier FF Elsevier ANN Elsevier DLS Elsevier AFR Elsevier purelin Elsevier Logsig Elsevier CuHRR Elsevier BSFC Elsevier Tansig Elsevier TDC Elsevier SI Elsevier THL Elsevier GBu5 Elsevier LHV Elsevier MSE Elsevier ECI-multi Elsevier CI Elsevier KGE Elsevier PME Elsevier COV Elsevier G100 Elsevier RMSE Elsevier CO Elsevier FIP Elsevier ppm Elsevier IMEP Elsevier rpm Elsevier NOx Elsevier FIT Elsevier SOHC Elsevier HC Elsevier 2-butanol Elsevier BVP Elsevier DA Elsevier MSRE |
| topic_unstemmed |
ddc 610 bkl 44.90 bkl 44.65 Elsevier CO2 Elsevier HCCI Elsevier WCO Elsevier LR Elsevier GHG Elsevier n-butanol Elsevier MLP Elsevier GBu10 Elsevier WTO Elsevier trainbfg Elsevier GBu15 Elsevier SHL Elsevier RBF Elsevier UN Elsevier bTDC Elsevier trainscg Elsevier CAD HRRmax Elsevier NSE Elsevier traingdx Elsevier RSE Elsevier trainrp Elsevier AI Elsevier BMEP Elsevier MAPE Elsevier RSM Elsevier EU Elsevier BTE Elsevier HnOME Elsevier CAD Pmax Elsevier GNA Elsevier BDC Elsevier FF Elsevier ANN Elsevier DLS Elsevier AFR Elsevier purelin Elsevier Logsig Elsevier CuHRR Elsevier BSFC Elsevier Tansig Elsevier TDC Elsevier SI Elsevier THL Elsevier GBu5 Elsevier LHV Elsevier MSE Elsevier ECI-multi Elsevier CI Elsevier KGE Elsevier PME Elsevier COV Elsevier G100 Elsevier RMSE Elsevier CO Elsevier FIP Elsevier ppm Elsevier IMEP Elsevier rpm Elsevier NOx Elsevier FIT Elsevier SOHC Elsevier HC Elsevier 2-butanol Elsevier BVP Elsevier DA Elsevier MSRE |
| topic_browse |
ddc 610 bkl 44.90 bkl 44.65 Elsevier CO2 Elsevier HCCI Elsevier WCO Elsevier LR Elsevier GHG Elsevier n-butanol Elsevier MLP Elsevier GBu10 Elsevier WTO Elsevier trainbfg Elsevier GBu15 Elsevier SHL Elsevier RBF Elsevier UN Elsevier bTDC Elsevier trainscg Elsevier CAD HRRmax Elsevier NSE Elsevier traingdx Elsevier RSE Elsevier trainrp Elsevier AI Elsevier BMEP Elsevier MAPE Elsevier RSM Elsevier EU Elsevier BTE Elsevier HnOME Elsevier CAD Pmax Elsevier GNA Elsevier BDC Elsevier FF Elsevier ANN Elsevier DLS Elsevier AFR Elsevier purelin Elsevier Logsig Elsevier CuHRR Elsevier BSFC Elsevier Tansig Elsevier TDC Elsevier SI Elsevier THL Elsevier GBu5 Elsevier LHV Elsevier MSE Elsevier ECI-multi Elsevier CI Elsevier KGE Elsevier PME Elsevier COV Elsevier G100 Elsevier RMSE Elsevier CO Elsevier FIP Elsevier ppm Elsevier IMEP Elsevier rpm Elsevier NOx Elsevier FIT Elsevier SOHC Elsevier HC Elsevier 2-butanol Elsevier BVP Elsevier DA Elsevier MSRE |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
m a a ma maa r m rm m g mg o i a oi oia w a wa |
| hierarchy_parent_title |
Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease |
| hierarchy_parent_id |
ELV003073483 |
| dewey-tens |
610 - Medicine & health |
| hierarchy_top_title |
Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV003073483 |
| title |
A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel |
| ctrlnum |
(DE-627)ELV04314487X (ELSEVIER)S1364-0321(18)30191-6 |
| title_full |
A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel |
| author_sort |
Yusri, I.M. |
| journal |
Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease |
| journalStr |
Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2018 |
| contenttype_str_mv |
zzz |
| container_start_page |
665 |
| author_browse |
Yusri, I.M. |
| container_volume |
90 |
| physical |
22 |
| class |
610 VZ 44.90 bkl 44.65 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Yusri, I.M. |
| doi_str_mv |
10.1016/j.rser.2018.03.095 |
| dewey-full |
610 |
| title_sort |
a review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel |
| title_auth |
A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel |
| abstract |
Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. |
| abstractGer |
Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. |
| abstract_unstemmed |
Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel |
| url |
https://doi.org/10.1016/j.rser.2018.03.095 |
| remote_bool |
true |
| author2 |
Abdul Majeed, A.P.P. Mamat, R. Ghazali, M.F. Awad, Omar I. Azmi, W.H. |
| author2Str |
Abdul Majeed, A.P.P. Mamat, R. Ghazali, M.F. Awad, Omar I. Azmi, W.H. |
| ppnlink |
ELV003073483 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth |
| doi_str |
10.1016/j.rser.2018.03.095 |
| up_date |
2024-07-06T18:03:25.052Z |
| _version_ |
1803853763764551680 |
| 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">ELV04314487X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626003222.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180726s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.rser.2018.03.095</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/GBV00000000001033.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV04314487X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1364-0321(18)30191-6</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">44.90</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.65</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yusri, I.M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">22</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">Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CO2</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">HCCI</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">WCO</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">LR</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GHG</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">n-butanol</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MLP</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GBu10</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">WTO</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">trainbfg</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GBu15</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">SHL</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">RBF</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">UN</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">bTDC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">trainscg</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CAD HRRmax</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">NSE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">traingdx</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">RSE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">trainrp</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">AI</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BMEP</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MAPE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">RSM</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">EU</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BTE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">HnOME</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CAD Pmax</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GNA</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BDC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">FF</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ANN</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">DLS</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">AFR</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">purelin</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Logsig</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CuHRR</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BSFC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Tansig</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">TDC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">SI</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">THL</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">GBu5</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">LHV</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MSE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ECI-multi</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CI</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">KGE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">PME</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COV</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">G100</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">RMSE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CO</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">FIP</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ppm</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">IMEP</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">rpm</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">NOx</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">FIT</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">SOHC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">HC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">2-butanol</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">BVP</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">DA</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MSRE</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Abdul Majeed, A.P.P.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mamat, R.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ghazali, M.F.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Awad, Omar I.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Azmi, W.H.</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">Soke, Fatih ELSEVIER</subfield><subfield code="t">Reliability, validity and responsiveness of the squares test for manual dexterity in people with Parkinson’s disease</subfield><subfield code="d">2019</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV003073483</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:90</subfield><subfield code="g">year:2018</subfield><subfield code="g">pages:665-686</subfield><subfield code="g">extent:22</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.rser.2018.03.095</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">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.90</subfield><subfield code="j">Neurologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.65</subfield><subfield code="j">Chirurgie</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">90</subfield><subfield code="j">2018</subfield><subfield code="h">665-686</subfield><subfield code="g">22</subfield></datafield></record></collection>
|
| score |
7.397996 |