The Experts below are selected from a list of 90306 Experts worldwide ranked by ideXlab platform
Q. Laperdrix - One of the best experts on this subject based on the ideXlab platform.
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Bioaccessibility of polycyclic aromatic compounds (PAHs, PCBs) and trace elements: influencing Factors and determination in a river sediment core
Journal of Hazardous Materials, 2020Co-Authors: Florence Portet-koltalo, Maxime Debret, Thomas Gardes, Yoann Copard, Stéphane Marcotte, C.j. Morin, Q. LaperdrixAbstract:Organic matter (OM), clays, sand or time are Factors possibly influencing the bioaccessibility of polycyclic aromatic hydrocarbons (PAHs) and polychlorobiphenyls (PCBs) from sediments. An experimental design was performed to monitor and quantify this process. The bioaccessible fraction, linked to the rapidly-desorbing fraction (Frap) of contaminants, was assessed through a non-exhaustive extraction using a carboxymethyl-β-cyclodextrin polymer. OM content was the most Influential Factor as regards Frap. Clay percentage was a slightly Influential Factor for PAHs while the interaction sand × OM was a slightly Influential Factor for PCBs. Frap was also determined in a sediment core collected from Martot’s Pond (France). The higher the PAH/PCB concentration in this sediment, the higher the bioaccessible fraction. The relationship between a lower bioaccessibility and a higher number of PAHs cycles or PCB chlorines was linear. OM content impacted on Frap only for PAHs. Sequential extractions of some trace elements were also performed to evaluate their mobility. Cu, Cr, Pb, Ni were the less bioaccessible. A great part of As, Cd and Zn was found in the most bioaccessible sediment fractions. The 40–65 cm section might be considered as the most negatively impacting on the aquatic fauna, due to Cd and Zn high bioaccessible concentrations.
Yoshiki Kusama - One of the best experts on this subject based on the ideXlab platform.
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obesity as an independent Influential Factor for reduced radial arterial wave reflection in a middle aged japanese male population
Hypertension Research, 2009Co-Authors: Toshiaki Otsuka, Tomoyuki Kawada, Chikao Ibuki, Yoshiki KusamaAbstract:Obesity as an independent Influential Factor for reduced radial arterial wave reflection in a middle-aged Japanese male population
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obesity as an independent Influential Factor for reduced radial arterial wave reflection in a middle aged japanese male population
Hypertension Research, 2009Co-Authors: Toshiaki Otsuka, Tomoyuki Kawada, Chikao Ibuki, Yoshiki KusamaAbstract:This study aimed to examine whether obesity, including abdominal obesity, is an Influential Factor for radial arterial wave reflection, as expressed by the augmentation index (AI), in middle-aged Japanese men. Radial arterial pressure waveform was measured using automated applanation tonometry in 828 men (mean age: 47±5 years) during an annual health examination at a company. Radial AI was calculated from the waveforms. AI appeared to be similar between subjects with and without obesity (body mass index (BMI) ⩾25 and <25 kg m−2, respectively). However, after adjusting for age, height, heart rate, mean blood pressure, low- and high-density lipoprotein cholesterol, fasting plasma glucose, white blood cell count and other potential confounders, AI was significantly lower in subjects with obesity (71.6%, 95% confidence interval (CI); 70.2–73.0%) than in those without (75.2%, 95% CI; 74.4–76.0%, P<0.001). In a multiple linear regression analysis, BMI was negatively associated with AI (β=−0.20, P<0.001); other Factors associated with AI were heart rate (β=−0.56), mean blood pressure (β=0.44), height (β=−0.24), age (β=0.15), current smoking (β=0.09), white blood cell count (β=0.06) and low-density lipoprotein cholesterol (β=0.06). Similar associations were found when waist circumference (WC, an index of abdominal obesity) was substituted for BMI in the analysis (β=−0.12, P<0.001). BMI closely correlated with WC (r=0.87), thus suggesting that approximately 76% (a square of 0.87) of BMI can be explained by WC. In conclusion, although it does not have a major impact, obesity, including abdominal obesity, may be an Influential Factor for reduced radial AI, independently of well-known confounders, in middle-aged Japanese men.
Florence Portet-koltalo - One of the best experts on this subject based on the ideXlab platform.
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Bioaccessibility of polycyclic aromatic compounds (PAHs, PCBs) and trace elements: influencing Factors and determination in a river sediment core
Journal of Hazardous Materials, 2020Co-Authors: Florence Portet-koltalo, Maxime Debret, Thomas Gardes, Yoann Copard, Stéphane Marcotte, C.j. Morin, Q. LaperdrixAbstract:Organic matter (OM), clays, sand or time are Factors possibly influencing the bioaccessibility of polycyclic aromatic hydrocarbons (PAHs) and polychlorobiphenyls (PCBs) from sediments. An experimental design was performed to monitor and quantify this process. The bioaccessible fraction, linked to the rapidly-desorbing fraction (Frap) of contaminants, was assessed through a non-exhaustive extraction using a carboxymethyl-β-cyclodextrin polymer. OM content was the most Influential Factor as regards Frap. Clay percentage was a slightly Influential Factor for PAHs while the interaction sand × OM was a slightly Influential Factor for PCBs. Frap was also determined in a sediment core collected from Martot’s Pond (France). The higher the PAH/PCB concentration in this sediment, the higher the bioaccessible fraction. The relationship between a lower bioaccessibility and a higher number of PAHs cycles or PCB chlorines was linear. OM content impacted on Frap only for PAHs. Sequential extractions of some trace elements were also performed to evaluate their mobility. Cu, Cr, Pb, Ni were the less bioaccessible. A great part of As, Cd and Zn was found in the most bioaccessible sediment fractions. The 40–65 cm section might be considered as the most negatively impacting on the aquatic fauna, due to Cd and Zn high bioaccessible concentrations.
Kishore Bingi - One of the best experts on this subject based on the ideXlab platform.
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application of principal component analysis vs multiple linear regression in resolving Influential Factor subject to air booster compressor motor failure
International Symposium on Robotics, 2018Co-Authors: Nurfatihah Syalwiah Binti Rosli, Idris Ismail, Rosdiazli Ibrahim, Kishore BingiAbstract:Predictive maintenance is vital towards the industrial economy to improve equipment reliability, efficiency and reduce downtime. The main objective of this work was to investigate the most Influential Factors contributing to the failure of the industrial motor to improve predictive maintenance. The most significant method was employed to investigate the most Influential Factors that affect the prediction of motor failure. There are 14 parameters were used to assess the most Influential Factors to the failure of the ABC motor. The Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) was carried out to evaluate the best Influential Factor to the failure. The result revealed the group of parameters that influence the ABC motor failure. However, the finding can be used as a guideline for predictive maintenance in order to mitigate the risk of the plant shutdown.
Nurfatihah Syalwiah Binti Rosli - One of the best experts on this subject based on the ideXlab platform.
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application of principal component analysis vs multiple linear regression in resolving Influential Factor subject to air booster compressor motor failure
International Symposium on Robotics, 2018Co-Authors: Nurfatihah Syalwiah Binti Rosli, Idris Ismail, Rosdiazli Ibrahim, Kishore BingiAbstract:Predictive maintenance is vital towards the industrial economy to improve equipment reliability, efficiency and reduce downtime. The main objective of this work was to investigate the most Influential Factors contributing to the failure of the industrial motor to improve predictive maintenance. The most significant method was employed to investigate the most Influential Factors that affect the prediction of motor failure. There are 14 parameters were used to assess the most Influential Factors to the failure of the ABC motor. The Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) was carried out to evaluate the best Influential Factor to the failure. The result revealed the group of parameters that influence the ABC motor failure. However, the finding can be used as a guideline for predictive maintenance in order to mitigate the risk of the plant shutdown.
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Application of Principle Component Analysis in Resolving Influential Factor Subject to Industrial Motor Failure
2018 UKSim-AMSS 20th International Conference on Computer Modelling and Simulation (UKSim), 2018Co-Authors: Nurfatihah Syalwiah Binti Rosli, Rosdiazli Bin Ibrahim, Idris IsmailAbstract:Predictive maintenance is very important towards industrial economy by improving equipment efficiency, reliability and reducing downtime. In recent years, abundant of data of rotating equipment is readily available from various sources. However, these data are not being utilized and analyzed for improving maintenance performance. This requires advanced techniques to analyze a variety of data in order to transform into relevant information. Most problems with a lot of parameters involved were not being specific to analyze the contribution of motor failure. Therefore, this research proposed an efficient data analysis using Principle Component Analysis (PCA) in determining the most Influential Factor to the failure of the industrial motor. The result will show the parameters that influence the motor failure. This finding can be used as a guideline for predictive maintenance in order to mitigate the risk of the plant shutdown.