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New insights into submicron particles impact on visibility (July, 10.1007/s11356-022-21781-y, 2022)

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第58期29卷 87982-87982页

作者： Majewski, Grzegorz Rogula-Kozlowska, Wioletta Szelag, Bartosz Aniol, Ewa Rogula-Kopiec, Patrycja Brandyk, Andrzej Walczak, Agata Radziemska, Maja Warsaw Univ Life Sci 166 Nowoursynowska St PL-02776 Warsaw Poland Main Sch Fire Serv 52-54 Slowackiego St PL-01629 Warsaw Poland Kielce Univ Technol 7 Aleja Tysiaclecia Panstwa Polskiego St PL-25314 Kielce Poland Polish Acad Sci Inst Environm Engn 34 M Sklodowska Curie St PL-41819 Zabrze Poland

The aim of the study was to analyze the impact of very fine atmospheric particles (submicron particulate matter; PM1) on visibility deterioration. Taking into consideration not only their entirely different physio-chemical properties in comparison to a well-recognized PM10 but also the origin and a growing environmental awareness of PM1, the main research problem has been solved in few steps. At first, the chemical composition of PM1 was determined in two selected urban areas in Poland. Measurements of meteorological parameters, i.e., air temperature and humidity, precipitation, atmospheric pressure, wind speed, and visibility, were also conducted. The next step of the work was the analysis of (1) seasonal changes of the concentration of PM1 and its main components, (2) the influence of chemical components of PM1 on light extinction, and (3) the influence of PM1 and humidity on visibility. Hierarchical cluster analysis, correlation matrixes and a heat map, and classification and regression tree analysis were used. The light extinction coefficient is influenced mainly by coarse mass of PM, and PM1-bound ammonium nitrate, organic matter, and by Rayleigh scattering. The less important in the light extinction coefficient shaping has PM1-bound ammonium sulfate, elemental carbon, and soil. In this way, the secondary origin PM1 components were proved to most significantly influence the visibility. The obtained results confirmed the possibility of the use of statistical agglomeration techniques to identify ranges of variation of visibility, including independent variables adopted to analyses (meteorological conditions, chemical composition of PM1, etc.).

关键词： PM1 Chemical composition Organic and elemental carbon Secondary inorganic and organic aerosol Sulfates and nitrates Mineral dust Coarse dust Light extinction coefficient Visibility classification and regression tree

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A Novel Decision tree Approach Based on Transcranial Doppler Sonography to Screen for Blunt Cervical Vascular Injuries

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JOURNAL OF ULTRASOUND IN MEDICINE 2013年第6期32卷 1023-1031页

作者： Purvis, Dianna Aldaghlas, Tayseer Trickey, Amber W. Rizzo, Anne Sikdar, Siddhartha George Mason Univ Dept Neurosci Fairfax VA 22030 USA George Mason Univ Dept Bioengn Fairfax VA 22030 USA Inova Fairfax Hosp Dept Trauma Serv Fairfax VA USA

Objectives-Early detection and treatment of blunt cervical vascular injuries prevent adverse neurologic sequelae. Current screening criteria can miss up to 22% of these injuries. The study objective was to investigate bedside transcranial Doppler sonography for detecting blunt cervical vascular injuries in trauma patients using a novel decision tree approach. Methods-This prospective pilot study was conducted at a level I trauma center. Patients undergoing computed tomographic angiography for suspected blunt cervical vascular injuries were studied with transcranial Doppler sonography. Extracranial and intracranial vasculatures were examined with a portable power M-mode transcranial Doppler unit. The middle cerebral artery mean flow velocity, pulsatility index, and their asymmetries were used to quantify flow patterns and develop an injury decision tree screening protocol. Student t tests validated associations between injuries and transcranial Doppler predictive measures. Results-We evaluated 27 trauma patients with 13 injuries. Single vertebral artery injuries were most common (38.5%), followed by single internal carotid artery injuries (30%). Compared to patients without injuries, mean flow velocity asymmetry was higher for single internal carotid artery (P = .003) and single vertebral artery (P = .004) injuries. Similarly, pulsatility index asymmetry was higher in single internal carotid artery (P = .015) and single vertebral artery (P = .042) injuries, whereas the lowest pulsatility index was elevated for bilateral vertebral artery injuries (P = .006). The decision tree yielded 92% specificity, 93% sensitivity, and 93% correct classifications. Conclusions-In this pilot feasibility study, transcranial Doppler measures were significantly associated with the blunt cervical vascular injury status, suggesting that transcranial Doppler sonography might be a viable bedside screening tool for trauma. Patient-specific hemodynamic information from transcranial Doppler ass

关键词： blunt cervical vascular injury classification and regression tree transcranial Doppler sonography trauma

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Maternal and stock effects on egg-size variation among walleye Sander vitreus stocks from the Great Lakes region

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JOURNAL OF GREAT LAKES RESEARCH 2012年第3期38卷 477-489页

作者： Wang, Hui-Yu Einhouse, Donald W. Fielder, David G. Rudstam, Lars G. Vandergoot, Christopher S. VanDeValk, Anthony J. Zorn, Troy G. Hoeoek, Tomas O. Natl Taiwan Univ Inst Oceanog 1Sec 4Roosevelt Rd Taipei 106 Taiwan Univ Michigan CILER Sch Nat Resources & Environm NOAA Great Lakes Environm Res Lab Ann Arbor MI 48105 USA New York State Dept Environm Conservat Lake Erie Fisheries Res Unit Dunkirk NY 14048 USA Michigan Dept Nat Reources Alpena Fisheries Res Stn Alpena MI 49707 USA Cornell Univ Biol Field Stn Dept Nat Resources Bridgeport NY 13030 USA Ohio Dept Nat Resources Div Wildlife Lake Erie Fisheries Unit Sandusky OH 44870 USA OBrien & Gere E Syracuse NY 13057 USA Michigan Dept Nat Resources Marquette Fisheries Res Stn Marquette MI 49855 USA Purdue Univ Dept Forestry & Nat Resources W Lafayette IN 47907 USA

Fish egg sizes vary intra-specifically among stocks and individuals, and such variation may reflect a combination of maternal and environmental influences. As egg size variation has important implications for individual and population recruitment success, it is useful to quantify egg-size variation and identify potential factors underlying such variation. We evaluated 1) within-stock maternal influences on egg size and 2) the relative elucidatory power of maternal effects versus stock in explaining inter-individual mean egg size based on eggs collected during 2007-2008 from five walleye Sander vitreus stocks in the North American Laurentian Great Lakes region. We used both linear regression models and classification and regression trees (CART) to describe egg-size variation. Egg size tended to increase with female length and for some stocks was greatest for intermediate maternal ages. However, maternal influences on egg size were relatively low and variable between years. In contrast, stock had a stronger effect;walleye egg-size variation was greater among stocks than within stocks. After controlling for the influence of maternal age and length, we found that egg size was relatively small for fish spawning in Maumee and Sandusky Rivers (western Lake Erie), intermediate in Oneida Lake and Tittabawassee River (Saginaw Bay, Lake Huron), and relatively large in Van Buren Bay (eastern Lake Erie) and Little Bay de Noc (northern Green Bay, Lake Michigan). Such inter-stock differences in maternal influence adjusted egg size appeared to be negatively associated with a system's productivity;suggesting a potential adaptive response of egg size to early life habitat conditions. (C) 2012 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

关键词： Egg size Inter-stock variation Maternal influence Adaptive classification and regression tree Walleye

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Toward the Threshold of Radiation Hazards of U in Chinese Coal through the CART Algorithm

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ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020年第3期000卷 1864-1874页

作者： Xu, Na Yang, Yuchen Peng, Mengmeng Li, Qing Xu, Chuanpeng Dai, Shifeng China Univ Min & Technol Beijing Coll Geosci & Survey Engn Beijing 100083 Peoples R China Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong 999077 Peoples R China

The high volume of coal used for combustion usually leads to a large amount of coal combustion residues (CCRs), which contain the naturally occurring radioactive materials (NORMs) decayed from U and Th in coals. The high radioactivity of NORMs can cause potential harm to humans if the CCRs are used as building materials. The activities of CCRs not only depend on the concentrations of radionuclides but also largely depend on the variations of ash yields of coal. On the other hand, ash yields significantly vary in coal from less than 1-50%. This indicates that similar concentrations of radionuclides in coal with different ash yields generally do not result in similar activities in CCRs. Therefore, it is significant to build a threshold of U in coals with different ash yield levels. In this research, based on the data of 945 coal samples from China and the selected optimal model using the classification and regression tree algorithm, the threshold of U for the radiation hazard is determined to be 7.98 mg/kg for coals with ash yields higher than 20%, while the threshold of U for the radiation hazard is 5.28 mg/kg for coals with ash yields lower than 20%.

关键词： naturally occurring radioactive materials coal combustion residues radiation hazard building materials classification and regression tree

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An efficient approach using HOS-based parameters in the LPC residual domain to classify breathy and rough voices

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2011年第2期6卷 186-196页

作者： Lee, JiYeoun Jeong, Sangbae Hahn, Minsoo Sprecher, Alicia J. Jiang, Jack J. Univ Wisconsin Sch Med Med Sci Ctr 5745A Div Otolaryngol Head & Neck Surg Madison WI 53706 USA Gyeongsang Natl Univ Sch Elect & Elect Engn Div Elect Engn Jinju 660701 South Korea Korea Adv Inst Sci & Technol Sch Engn Div Multimedia Commun & Proc Taejon 305701 South Korea

Although a considerable number of studies have been focused on the analysis of pathological voices using conventional parameters such as jitter, shimmer, and signal-to-noise ratio (SNR), these parameters have been found to be sensitive to variations in pitch extraction algorithm and cannot analyze severely disordered voice signals which exhibit irregular or aperiodic waveforms. In this paper, higher-order statistics (HOSs) analysis, which is independent of pitch period, is derived from linear predictive coding (LPC) residuals to describe breathy and rough voices. Recordings of a sustained /a/ from 23 individuals with breathy voices and 30 individuals with rough voices were collected from the disordered voice database distributed by the Japanese Society of Logopedics and Phoniatrics. We extracted conventional parameters as well as the HOS-based parameters such as the normalized skewness and the normalized kurtosis. On the other hand, we calculated HOS-based parameters from the LPC residual domain. The results showed that the HOS-based parameters and the HOS-based parameters estimated from the LPC residual are different for rough and breathy voices. Conventional parameters were not distinctive for these voices. classification and regression tree (CART) was used to combine multiple parameters and to classify breathy and rough voices. Using the HOS-based parameters, the CART achieved an accuracy of 85.0% with the optimal decision tree generated by means of the normalized skewness and kurtosis. When the HOS-based parameters using LPC residual were used, the optimal decision tree was 88.7% accurate and the variances of the normalized skewness and kurtosis were included. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Pathological voice quality Higher-order statistics Linear predictive coding classification and regression tree

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Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction

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INFORMATION SCIENCES 2013年 236卷 174-185页

作者： Chuang, Chun-Ling Kai Nan Univ Dept Informat Management Tao Yuan 338 Taiwan

Predicting business failure is an important and challenging issue that has served as an impetus for many academic studies over the past three decades. This study aims at developing CBR-based hybrid models of predicting business failure. The need to supplement CBR (Case-Based Reasoning) with other classification and diagnosis techniques is triggered by the fact that accuracy and effectiveness tend to get reduced when CBR alone is applied to handle too many attributes. To enhance the accuracy of bankruptcy prediction, the hybrid models developed by this study include: RST-CBR (combining Rough Set Theory with CBR), RST-GRA-CBR (integrating RST, Grey Relational Analysis, and CBR), and CART-CBR (combining classification and regression tree with CBR). In order to verify the ability of the proposed models to achieve optimal accuracy rate, this study further compares the predictive ability of CBR with those of other comparative models. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Decision analysis Data mining Rough set theory Business failure prediction Grey relation analysis classification and regression tree

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Case study in Six Sigma methodology: manufacturing quality improvement and guidance for managers

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PRODUCTION PLANNING & CONTROL 2012年第8期23卷 624-640页

作者： Antony, Jiju Gijo, E. V. Childe, S. J. Univ Strathclyde Dept DMEM Glasgow G1 1XJ Lanark Scotland Indian Stat Inst SQC & OR Unit Bangalore 560059 Karnataka India Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QJ Devon England

This article discusses the successful implementation of Six Sigma methodology in a high precision and critical process in the manufacture of automotive products. The Six Sigma define-measure-analyse-improve-control approach resulted in a reduction of tolerance-related problems and improved the first pass yield from 85% to 99.4%. Data were collected on all possible causes and regression analysis, hypothesis testing, Taguchi methods, classification and regression tree, etc. were used to analyse the data and draw conclusions. Implementation of Six Sigma methodology had a significant financial impact on the profitability of the company. An approximate saving of US$70,000 per annum was reported, which is in addition to the customer-facing benefits of improved quality on returns and sales. The project also had the benefit of allowing the company to learn useful messages that will guide future Six Sigma activities.

关键词： Six Sigma DMAIC Taguchi methods regression analysis classification and regression tree process capability evaluation cause validation plan matrix plot

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An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

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JOURNAL OF CHEMOMETRICS 2015年第4期29卷 213-223页

作者： Tavakoli, Hossein Ghasemi, Jahan B. KN Toosi Univ Technol Fac Chem Drug Design Silico Lab Tehran Iran Univ Tehran Fac Chem Tehran Iran

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners). Copyright (c) 2015 John Wiley & Sons, Ltd. The AdaBoost R3 algorithm was proposed by the following modifications on the AdaBoost R2: introducing extremums to the boosting set, passing the loss values through a modified Huber loss function and adding an acceptance threshold to remove the bad predictions.

关键词： tyrosine kinase inhibitors classification and regression tree radial basis functions partial least square ensemble learning adaptive boosting

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Objective Pathological Voice Quality Assessment Based on HOS Features

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2008年第12期E91D卷 2888-2891页

作者： Lee, Ji-Yeoun Jeong, Sangbae Choi, Hong-Shik Hahn, Minsoo Informat & Commun Univ Taejon 305732 South Korea Yonsei Univ Coll Med Yongdong Severance Hosp Dept OtorhinolaryngolInst Logoped & Phoniatr Seoul South Korea

This work proposes new features to improve the pathological voice quality classification performance. They are the means. the variances, and the perturbations of the higher-order statistics (HOS) such as the skewness and the kurtosis. The HOS-based features show meaningful differences among normal. grade 1, grade 2, and grade 3 voices, classified in the GRBAS scale. The jitter. the shimmer, the harmonic-to-noise ratio (HNR). and the variance of the short-time energy are utilized as the conventional features. The performances are measured by the classification and regression tree (CART) method. Specifically, the CART-based method by utilizing both the conventional features and the HOS-based ones shows its effectiveness in the pathological voice quality Measurement. with the classification accuracy of 87.8%.

关键词： pathological voice quality assessment higher-order statistics classification and regression tree GRBAS

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classification of Analgesic Drugs in Primary Packaging by Applying Multivariate Methods to Terahertz Spectra

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JOURNAL OF APPLIED SPECTROSCOPY 2021年第5期88卷 1008-1011页

作者： Lyakhnovich, A. V. Sinitsyn, G. V. Khodasevich, M. A. Borisevich, D. A. Natl Acad Sci Belarus BI Stepanov Inst Phys Minsk BELARUS

The efficiencies of several multivariate methods of recognition of drug classes in primary packaging using THz transmission spectra are compared. The advantage of support vector machines is demonstrated with a classification error <= 5.7% for two groups of analgesic drugs with a high level of heterogeneity of samples in the groups.

关键词： principal component analysis classification and regression tree linear discriminant analysis support vector machine THz spectroscopy

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