Purpose: The present study aimed to investigate the reliability of the non-exhaustive double effort(NEDE) test in running exercise and its associations with the ventilatory thresholds(VT_1 and VT_2) and the maximal la...Purpose: The present study aimed to investigate the reliability of the non-exhaustive double effort(NEDE) test in running exercise and its associations with the ventilatory thresholds(VT_1 and VT_2) and the maximal lactate steady state(MLSS).Methods: Ten healthy male adults(age: 23 ± 4 years, height: 176.6 ± 6.4 cm, body mass: 76.6 ± 10.7 kg) performed 4 procedures:(1) a ramp test for VT_1 and VT_2 determinations measured by ratio of expired ventilation to O_2 uptake(VE/VO_2) and expired ventilation to CO_2 output(VE/VCO_2) equivalents, respectively;(2) the NEDE test measured by blood lactate concentration(NEDE_(LAC)) and heart rate responses(NEDE_(HR));(3) a retest of NEDE for reliability analysis; and(4) continuous efforts to determine the MLSS intensity. The NEDE test consisted of4 sessions at different running intensities. Each session was characterized by double efforts at the same running velocity(E1 and E2, 180 s), separated by a passive recovery period(90 s rest). LAC and HR values after E1 and E2(in 4 sessions) were used to estimate the intensity equivalent to"null delta" by linear fit. This parameter represents, theoretically, the intensity equivalent to maximal aerobic capacity.Results: The intraclass correlation coefficient indicated significant reliability for NEDE_(LAC)(0.93) and NEDE_(HR)(0.79)(both p < 0.05). There were significant correlations, no differences, and strong agreement with the intensities predicted by NEDE_(LAC)(10.1 ± 1.9 km/h) and NEDE_(HR)(9.8 ± 2.0 km/h) to VT_1(10.2 ± 1.1 km/h). In addition, despite significantly lower MLSS intensity(12.2 ± 1.2 km/h), NEDE_(LAC) and NEDE_(HR) intensities were highly correlated with this parameter(0.90 and 0.88, respectively).Conclusion: The NEDE test applied to running exercise is reliable and estimates the VT_1 intensity. Additionally, NEDE intensities were lower but still correlated with VT_2 and MLSS.展开更多
A single particle aerosol mass spectrometer(SPAMS)was used to accurately quantify the contribution of vehicle non-exhaust emissions to particulate matter at typical road environment.The PM_(2.5),black carbon,meteorolo...A single particle aerosol mass spectrometer(SPAMS)was used to accurately quantify the contribution of vehicle non-exhaust emissions to particulate matter at typical road environment.The PM_(2.5),black carbon,meteorological parameters and traffic flow were recorded during the test period.The daily trend for traffic flow and speed on TEDA Street showed obvious“M”and“W”characteristics.6.3 million particles were captured via the SPAMS,including 1.3 million particles with positive and negative spectral map information.Heavy Metal,High molecular Organic Carbon,Organic Carbon,Mixed Carbon,Elemental Carbon,Rich Potassium,Levo-rotation Glucose,Rich Na,SiO_(3) and other categories were analyzed.The particle number concentration measured by SPAMS showed a good linear correlation with the mass concentrations of PM_(2.5) and BC,which indicates that the particulate matter captured by the SPAMS reflects the pollution level of fine particulate matter.EC,ECOC,OC,HM and crustal dust components were found to show high values from 7:00–9:00 AM,showing that these chemical components are directly or indirectly related to vehicle emissions.Based on the PMF model,7 major factors are resolved.The relative contributions of each factor were determined:vehicle exhaust emission(44.8%),coal-fired source(14.5%),biomass combustion(12.2%),crustal dust(9.4%),ship emission(9.0%),tires wear(6.6%)and brake pads wear(3.5%).The results show that the contribution of vehicle non-exhaust to particulate matter at roadside environment is approximately 10.1%.Vehicle non-exhaust emissions are the focus of future research in the vehicle pollutant emission control field.展开更多
Patent documents are unique external sources of information that reveal the core technology underlying new inventions. Patents also serve as a strategic data source that can be mined to discover state-of-the-art techn...Patent documents are unique external sources of information that reveal the core technology underlying new inventions. Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R＆D investments. This research incorporates an ontology schema to extract and represent patent concepts. A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery. The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims, a feature that enables the grouping of patents that define related key innovations. Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation. The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification （RFID） patents and by analyzing the legal implications of automated discovery of patent infringement.展开更多
基金financially supported by the Fundacao de AmparoàPesquisa do Estado de Sao Paulo(FAPESP,protocol 2009/08535-5)
文摘Purpose: The present study aimed to investigate the reliability of the non-exhaustive double effort(NEDE) test in running exercise and its associations with the ventilatory thresholds(VT_1 and VT_2) and the maximal lactate steady state(MLSS).Methods: Ten healthy male adults(age: 23 ± 4 years, height: 176.6 ± 6.4 cm, body mass: 76.6 ± 10.7 kg) performed 4 procedures:(1) a ramp test for VT_1 and VT_2 determinations measured by ratio of expired ventilation to O_2 uptake(VE/VO_2) and expired ventilation to CO_2 output(VE/VCO_2) equivalents, respectively;(2) the NEDE test measured by blood lactate concentration(NEDE_(LAC)) and heart rate responses(NEDE_(HR));(3) a retest of NEDE for reliability analysis; and(4) continuous efforts to determine the MLSS intensity. The NEDE test consisted of4 sessions at different running intensities. Each session was characterized by double efforts at the same running velocity(E1 and E2, 180 s), separated by a passive recovery period(90 s rest). LAC and HR values after E1 and E2(in 4 sessions) were used to estimate the intensity equivalent to"null delta" by linear fit. This parameter represents, theoretically, the intensity equivalent to maximal aerobic capacity.Results: The intraclass correlation coefficient indicated significant reliability for NEDE_(LAC)(0.93) and NEDE_(HR)(0.79)(both p < 0.05). There were significant correlations, no differences, and strong agreement with the intensities predicted by NEDE_(LAC)(10.1 ± 1.9 km/h) and NEDE_(HR)(9.8 ± 2.0 km/h) to VT_1(10.2 ± 1.1 km/h). In addition, despite significantly lower MLSS intensity(12.2 ± 1.2 km/h), NEDE_(LAC) and NEDE_(HR) intensities were highly correlated with this parameter(0.90 and 0.88, respectively).Conclusion: The NEDE test applied to running exercise is reliable and estimates the VT_1 intensity. Additionally, NEDE intensities were lower but still correlated with VT_2 and MLSS.
基金supported by the National Natural Science Foundation of China(Nos.42107114 and 42177084)the Tianjin Science and Technology Plan Project(No.20YFZCSN01000)the Fundamental Research Funds for the Central Universities(No.63221411).
文摘A single particle aerosol mass spectrometer(SPAMS)was used to accurately quantify the contribution of vehicle non-exhaust emissions to particulate matter at typical road environment.The PM_(2.5),black carbon,meteorological parameters and traffic flow were recorded during the test period.The daily trend for traffic flow and speed on TEDA Street showed obvious“M”and“W”characteristics.6.3 million particles were captured via the SPAMS,including 1.3 million particles with positive and negative spectral map information.Heavy Metal,High molecular Organic Carbon,Organic Carbon,Mixed Carbon,Elemental Carbon,Rich Potassium,Levo-rotation Glucose,Rich Na,SiO_(3) and other categories were analyzed.The particle number concentration measured by SPAMS showed a good linear correlation with the mass concentrations of PM_(2.5) and BC,which indicates that the particulate matter captured by the SPAMS reflects the pollution level of fine particulate matter.EC,ECOC,OC,HM and crustal dust components were found to show high values from 7:00–9:00 AM,showing that these chemical components are directly or indirectly related to vehicle emissions.Based on the PMF model,7 major factors are resolved.The relative contributions of each factor were determined:vehicle exhaust emission(44.8%),coal-fired source(14.5%),biomass combustion(12.2%),crustal dust(9.4%),ship emission(9.0%),tires wear(6.6%)and brake pads wear(3.5%).The results show that the contribution of vehicle non-exhaust to particulate matter at roadside environment is approximately 10.1%.Vehicle non-exhaust emissions are the focus of future research in the vehicle pollutant emission control field.
基金supported by the National Science Council research grant
文摘Patent documents are unique external sources of information that reveal the core technology underlying new inventions. Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R＆D investments. This research incorporates an ontology schema to extract and represent patent concepts. A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery. The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims, a feature that enables the grouping of patents that define related key innovations. Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation. The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification （RFID） patents and by analyzing the legal implications of automated discovery of patent infringement.
