Machinability of hypereutectic silicon-aluminum alloys

NASA Astrophysics Data System (ADS)

Tanaka, T.; Akasawa, T.

1999-08-01

The machinability of high-silicon aluminum alloys made by a P/M process and by casting was compared. The cutting test was conducted by turning on lathes with the use of cemented carbide tools. The tool wear by machining the P/M alloy was far smaller than the tool wear by machining the cast alloy. The roughness of the machined surface of the P/M alloy is far better than that of the cast alloy, and the turning speed did not affect it greatly at higher speeds. The P/M alloy produced long chips, so the disposal can cause trouble. The size effect of silicon grains on the machinability is discussed.

Thermal-mechanical modeling of laser ablation hybrid machining

NASA Astrophysics Data System (ADS)

Matin, Mohammad Kaiser

2001-08-01

Hard, brittle and wear-resistant materials like ceramics pose a problem when being machined using conventional machining processes. Machining ceramics even with a diamond cutting tool is very difficult and costly. Near net-shape processes, like laser evaporation, produce micro-cracks that require extra finishing. Thus it is anticipated that ceramic machining will have to continue to be explored with new-sprung techniques before ceramic materials become commonplace. This numerical investigation results from the numerical simulations of the thermal and mechanical modeling of simultaneous material removal from hard-to-machine materials using both laser ablation and conventional tool cutting utilizing the finite element method. The model is formulated using a two dimensional, planar, computational domain. The process simulation acronymed, LAHM (Laser Ablation Hybrid Machining), uses laser energy for two purposes. The first purpose is to remove the material by ablation. The second purpose is to heat the unremoved material that lies below the ablated material in order to ``soften'' it. The softened material is then simultaneously removed by conventional machining processes. The complete solution determines the temperature distribution and stress contours within the material and tracks the moving boundary that occurs due to material ablation. The temperature distribution is used to determine the distance below the phase change surface where sufficient ``softening'' has occurred, so that a cutting tool may be used to remove additional material. The model incorporated for tracking the ablative surface does not assume an isothermal melt phase (e.g. Stefan problem) for laser ablation. Both surface absorption and volume absorption of laser energy as function of depth have been considered in the models. LAHM, from the thermal and mechanical point of view is a complex machining process involving large deformations at high strain rates, thermal effects of the laser, removal of

Sterility and hypermutability in the P-M system of hybrid dysgenesis in Drosophila melanogaster.

PubMed

Kocur, G J; Drier, E A; Simmons, M J

1986-12-01

Inbred wild strains of Drosophila melanogaster derived from the central and eastern United States were used to make dysgenic hybrids in the P-M system. These strains possessed P elements and the P cytotype, the condition that represses P element transposition. Their hybrids were studied for the mutability of the P element insertion mutation, snw, and for the incidence of gonadal dysgenesis (GD) sterility. All the strains tested were able to induce hybrid dysgenesis by one or both of these assays; however, high levels of dysgenesis were rare. Sets of X chromosomes and autosomes from the inbred wild strains were more effective at inducing GD sterility than were sets of Y chromosomes and autosomes. In two separate analyses, GD sterility was positively correlated with snw mutability, suggesting a linear relationship. However, one strain appeared to induce too much GD sterility for its level of snw destabilization, indicating an uncoupling of these two manifestations of hybrid dysgenesis.

Sterility and Hypermutability in the P-M System of Hybrid Dysgenesis in DROSOPHILA MELANOGASTER

PubMed Central

Kocur, Gordon J.; Drier, Eric A.; Simmons, Michael J.

1986-01-01

Inbred wild strains of Drosophila melanogaster derived from the central and eastern United States were used to make dysgenic hybrids in the P-M system. These strains possessed P elements and the P cytotype, the condition that represses P element transposition. Their hybrids were studied for the mutability of the P element insertion mutation, snw, and for the incidence of gonadal dysgenesis (GD) sterility. All the strains tested were able to induce hybrid dysgenesis by one or both of these assays; however, high levels of dysgenesis were rare. Sets of X chromosomes and autosomes from the inbred wild strains were more effective at inducing GD sterility than were sets of Y chromosomes and autosomes. In two separate analyses, GD sterility was positively correlated with snw mutability, suggesting a linear relationship. However, one strain appeared to induce too much GD sterility for its level of snw destabilization, indicating an uncoupling of these two manifestations of hybrid dysgenesis. PMID:3100389

Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Farahlina Johari, Nur; Zain, Azlan Mohd; Haszlinna Mustaffa, Noorfa; Udin, Amirmudin

2017-09-01

Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

NASA Astrophysics Data System (ADS)

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

2016-06-01

To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Molecular Machine Powered Surface Programmatic Chain Reaction for Highly Sensitive Electrochemical Detection of Protein.

PubMed

Zhu, Jing; Gan, Haiying; Wu, Jie; Ju, Huangxian

2018-04-17

A bipedal molecular machine powered surface programmatic chain reaction was designed for electrochemical signal amplification and highly sensitive electrochemical detection of protein. The bipedal molecular machine was built through aptamer-target specific recognition for the binding of one target protein with two DNA probes, which hybridized with surface-tethered hairpin DNA 1 (H1) via proximity effect to expose the prelocked toehold domain of H1 for the hybridization of ferrocene-labeled hairpin DNA 2 (H2-Fc). The toehold-mediated strand displacement reaction brought the electrochemical signal molecule Fc close to the electrode and meanwhile released the bipedal molecular machine to traverse the sensing surface by the surface programmatic chain reaction. Eventually, a large number of duplex structures of H1-H2 with ferrocene groups facing to the electrode were formed on the sensor surface to generate an amplified electrochemical signal. Using thrombin as a model target, this method showed a linear detection range from 2 pM to 20 nM with a detection limit of 0.76 pM. The proposed detection strategy was enzyme-free and allowed highly sensitive and selective detection of a variety of protein targets by using corresponding DNA-based affinity probes, showing potential application in bioanalysis.

Heuristic for Critical Machine Based a Lot Streaming for Two-Stage Hybrid Production Environment

NASA Astrophysics Data System (ADS)

Vivek, P.; Saravanan, R.; Chandrasekaran, M.; Pugazhenthi, R.

2017-03-01

Lot streaming in Hybrid flowshop [HFS] is encountered in many real world problems. This paper deals with a heuristic approach for Lot streaming based on critical machine consideration for a two stage Hybrid Flowshop. The first stage has two identical parallel machines and the second stage has only one machine. In the second stage machine is considered as a critical by valid reasons these kind of problems is known as NP hard. A mathematical model developed for the selected problem. The simulation modelling and analysis were carried out in Extend V6 software. The heuristic developed for obtaining optimal lot streaming schedule. The eleven cases of lot streaming were considered. The proposed heuristic was verified and validated by real time simulation experiments. All possible lot streaming strategies and possible sequence under each lot streaming strategy were simulated and examined. The heuristic consistently yielded optimal schedule consistently in all eleven cases. The identification procedure for select best lot streaming strategy was suggested.

Using Phun to Study ``Perpetual Motion'' Machines

NASA Astrophysics Data System (ADS)

Koreš, Jaroslav

2012-05-01

The concept of "perpetual motion" has a long history. The Indian astronomer and mathematician Bhaskara II (12th century) was the first person to describe a perpetual motion (PM) machine. An example of a 13th- century PM machine is shown in Fig. 1. Although the law of conservation of energy clearly implies the impossibility of PM construction, over the centuries numerous proposals for PM have been made, involving ever more elements of modern science in their construction. It is possible to test a variety of PM machines in the classroom using a program called Phun2 or its commercial version Algodoo.3 The programs are designed to simulate physical processes and we can easily simulate mechanical machines using them. They provide an intuitive graphical environment controlled with a mouse; a programming language is not needed. This paper describes simulations of four different (supposed) PM machines.4

Permanent magnet machine and method with reluctance poles and non-identical PM poles for high density operation

DOEpatents

Hsu, John S.

2010-05-18

A method and apparatus in which a stator (11) and a rotor (12) define a primary air gap (20) for receiving AC flux and at least one source (23, 40), and preferably two sources (23, 24, 40) of DC excitation are positioned for inducing DC flux at opposite ends of the rotor (12). Portions of PM material (17, 17a) are provided as boundaries separating PM rotor pole portions from each other and from reluctance poles. The PM poles (18) and the reluctance poles (19) can be formed with poles of one polarity having enlarged flux paths in relation to flux paths for pole portions of an opposite polarity, the enlarged flux paths communicating with a core of the rotor (12) so as to increase reluctance torque produced by the electric machine. Reluctance torque is increased by providing asymmetrical pole faces. The DC excitation can also use asymmetric poles and asymmetric excitation sources. Several embodiments are disclosed with additional variations.

Design Comparison of Inner and Outer Rotor of Permanent Magnet Flux Switching Machine for Electric Bicycle Application

NASA Astrophysics Data System (ADS)

Jusoh, L. I.; Sulaiman, E.; Bahrim, F. S.; Kumar, R.

2017-08-01

Recent advancements have led to the development of flux switching machines (FSMs) with flux sources within the stators. The advantage of being a single-piece machine with a robust rotor structure makes FSM an excellent choice for speed applications. There are three categories of FSM, namely, the permanent magnet (PM) FSM, the field excitation (FE) FSM, and the hybrid excitation (HE) FSM. The PMFSM and the FEFSM have their respective PM and field excitation coil (FEC) as their key flux sources. Meanwhile, as the name suggests, the HEFSM has a combination of PM and FECs as the flux sources. The PMFSM is a simple and cheap machine, and it has the ability to control variable flux, which would be suitable for an electric bicycle. Thus, this paper will present a design comparison between an inner rotor and an outer rotor for a single-phase permanent magnet flux switching machine with 8S-10P, designed specifically for an electric bicycle. The performance of this machine was validated using the 2D- FEA. As conclusion, the outer-rotor has much higher torque approximately at 54.2% of an innerrotor PMFSM. From the comprehensive analysis of both designs it can be conclude that output performance is lower than the SRM and IPMSM design machine. But, it shows that the possibility to increase the design performance by using “deterministic optimization method”.

Hybrid-secondary uncluttered permanent magnet machine and method

DOEpatents

Hsu, John S.

2005-12-20

An electric machine (40) has a stator (43), a permanent magnet rotor (38) with permanent magnets (39) and a magnetic coupling uncluttered rotor (46) for inducing a slip energy current in secondary coils (47). A dc flux can be produced in the uncluttered rotor when the secondary coils are fed with dc currents. The magnetic coupling uncluttered rotor (46) has magnetic brushes (A, B, C, D) which couple flux in through the rotor (46) to the secondary coils (47c, 47d) without inducing a current in the rotor (46) and without coupling a stator rotational energy component to the secondary coils (47c, 47d). The machine can be operated as a motor or a generator in multi-phase or single-phase embodiments and is applicable to the hybrid electric vehicle. A method of providing a slip energy controller is also disclosed.

Drilling of Hybrid Titanium Composite Laminate (HTCL) with Electrical Discharge Machining.

PubMed

Ramulu, M; Spaulding, Mathew

2016-09-01

An experimental investigation was conducted to determine the application of die sinker electrical discharge machining (EDM) as it applies to a hybrid titanium thermoplastic composite laminate material. Holes were drilled using a die sinker EDM. The effects of peak current, pulse time, and percent on-time on machinability of hybrid titanium composite material were evaluated in terms of material removal rate (MRR), tool wear rate, and cut quality. Experimental models relating each process response to the input parameters were developed and optimum operating conditions with a short cutting time, achieving the highest workpiece MRR, with very little tool wear were determined to occur at a peak current value of 8.60 A, a percent on-time of 36.12%, and a pulse time of 258 microseconds. After observing data acquired from experimentation, it was determined that while use of EDM is possible, for desirable quality it is not fast enough for industrial application.

Drilling of Hybrid Titanium Composite Laminate (HTCL) with Electrical Discharge Machining

PubMed Central

Ramulu, M.; Spaulding, Mathew

2016-01-01

An experimental investigation was conducted to determine the application of die sinker electrical discharge machining (EDM) as it applies to a hybrid titanium thermoplastic composite laminate material. Holes were drilled using a die sinker EDM. The effects of peak current, pulse time, and percent on-time on machinability of hybrid titanium composite material were evaluated in terms of material removal rate (MRR), tool wear rate, and cut quality. Experimental models relating each process response to the input parameters were developed and optimum operating conditions with a short cutting time, achieving the highest workpiece MRR, with very little tool wear were determined to occur at a peak current value of 8.60 A, a percent on-time of 36.12%, and a pulse time of 258 microseconds. After observing data acquired from experimentation, it was determined that while use of EDM is possible, for desirable quality it is not fast enough for industrial application. PMID:28773866

Ion beam figuring of highly steep mirrors with a 5-axis hybrid machine tool

NASA Astrophysics Data System (ADS)

Yin, Xiaolin; Tang, Wa; Hu, Haixiang; Zeng, Xuefeng; Wang, Dekang; Xue, Donglin; Zhang, Feng; Deng, Weijie; Zhang, Xuejun

2018-02-01

Ion beam figuring (IBF) is an advanced and deterministic method for optical mirror surface processing. The removal function of IBF varies with the different incident angles of ion beam. Therefore, for the curved surface especially the highly steep one, the Ion Beam Source (IBS) should be equipped with 5-axis machining capability to remove the material along the normal direction of the mirror surface, so as to ensure the stability of the removal function. Based on the 3-RPS parallel mechanism and two dimensional displacement platform, a new type of 5-axis hybrid machine tool for IBF is presented. With the hybrid machine tool, the figuring process of a highly steep fused silica spherical mirror is introduced. The R/# of the mirror is 0.96 and the aperture is 104mm. The figuring result shows that, PV value of the mirror surface error is converged from 121.1nm to32.3nm, and RMS value 23.6nm to 3.4nm.

Design study and performance analysis of 12S-14P field excitation flux switching motor for hybrid electric vehicle

NASA Astrophysics Data System (ADS)

Husin, Zhafir Aizat; Sulaiman, Erwan; Khan, Faisal; Mazlan, Mohamed Mubin Aizat; Othman, Syed Muhammad Naufal Syed

2015-05-01

This paper presents a new structure of 12slot-14pole field excitation flux switching motor (FEFSM) as an alternative candidate of non-Permanent Magnet (PM) machine for HEV drives. Design study, performance analysis and optimization of field excitation flux switching machine with non-rare-earth magnet for hybrid electric vehicle drive applications is done. The stator of projected machine consists of iron core made of electromagnetic steels, armature coils and field excitation coils as the only field mmf source. The rotor is consisted of only stack of iron and hence, it is reliable and appropriate for high speed operation. The design target is a machine with the maximum torque, power and power density, more than 210Nm, 123kW and 3.5kW/kg, respectively, which competes with interior permanent magnet synchronous machine used in existing hybrid electric vehicle. Some design feasibility studies on FEFSM based on 2D-FEA and deterministic optimization method will be applied to design the proposed machine.

Machine vision system for inspecting characteristics of hybrid rice seed

NASA Astrophysics Data System (ADS)

Cheng, Fang; Ying, Yibin

2004-03-01

Obtaining clear images advantaged of improving the classification accuracy involves many factors, light source, lens extender and background were discussed in this paper. The analysis of rice seed reflectance curves showed that the wavelength of light source for discrimination of the diseased seeds from normal rice seeds in the monochromic image recognition mode was about 815nm for jinyou402 and shanyou10. To determine optimizing conditions for acquiring digital images of rice seed using a computer vision system, an adjustable color machine vision system was developed. The machine vision system with 20mm to 25mm lens extender produce close-up images which made it easy to object recognition of characteristics in hybrid rice seeds. White background was proved to be better than black background for inspecting rice seeds infected by disease and using the algorithms based on shape. Experimental results indicated good classification for most of the characteristics with the machine vision system. The same algorithm yielded better results in optimizing condition for quality inspection of rice seed. Specifically, the image processing can correct for details such as fine fissure with the machine vision system.

Using Phun to Study "Perpetual Motion" Machines

ERIC Educational Resources Information Center

Kores, Jaroslav

2012-01-01

The concept of "perpetual motion" has a long history. The Indian astronomer and mathematician Bhaskara II (12th century) was the first person to describe a perpetual motion (PM) machine. An example of a 13th-century PM machine is shown in Fig. 1. Although the law of conservation of energy clearly implies the impossibility of PM construction, over…

A hybrid flowshop scheduling model considering dedicated machines and lot-splitting for the solar cell industry

NASA Astrophysics Data System (ADS)

Wang, Li-Chih; Chen, Yin-Yann; Chen, Tzu-Li; Cheng, Chen-Yang; Chang, Chin-Wei

2014-10-01

This paper studies a solar cell industry scheduling problem, which is similar to traditional hybrid flowshop scheduling (HFS). In a typical HFS problem, the allocation of machine resources for each order should be scheduled in advance. However, the challenge in solar cell manufacturing is the number of machines that can be adjusted dynamically to complete the job. An optimal production scheduling model is developed to explore these issues, considering the practical characteristics, such as hybrid flowshop, parallel machine system, dedicated machines, sequence independent job setup times and sequence dependent job setup times. The objective of this model is to minimise the makespan and to decide the processing sequence of the orders/lots in each stage, lot-splitting decisions for the orders and the number of machines used to satisfy the demands in each stage. From the experimental results, lot-splitting has significant effect on shortening the makespan, and the improvement effect is influenced by the processing time and the setup time of orders. Therefore, the threshold point to improve the makespan can be identified. In addition, the model also indicates that more lot-splitting approaches, that is, the flexibility of allocating orders/lots to machines is larger, will result in a better scheduling performance.

Hybrid approach of selecting hyperparameters of support vector machine for regression.

PubMed

Jeng, Jin-Tsong

2006-06-01

To select the hyperparameters of the support vector machine for regression (SVR), a hybrid approach is proposed to determine the kernel parameter of the Gaussian kernel function and the epsilon value of Vapnik's epsilon-insensitive loss function. The proposed hybrid approach includes a competitive agglomeration (CA) clustering algorithm and a repeated SVR (RSVR) approach. Since the CA clustering algorithm is used to find the nearly "optimal" number of clusters and the centers of clusters in the clustering process, the CA clustering algorithm is applied to select the Gaussian kernel parameter. Additionally, an RSVR approach that relies on the standard deviation of a training error is proposed to obtain an epsilon in the loss function. Finally, two functions, one real data set (i.e., a time series of quarterly unemployment rate for West Germany) and an identification of nonlinear plant are used to verify the usefulness of the hybrid approach.

Manufacturing and Machining Challenges of Hybrid Aluminium Metal Matix Composites

NASA Astrophysics Data System (ADS)

Baburaja, Kammuluri; Sainadh Teja, S.; Karthik Sri, D.; Kuldeep, J.; Gowtham, V.

2017-08-01

Manufacturing which involves material removal processes or material addition processes or material transformation processes. One or all the processes to obtain the final desired properties for a material with desired shape which meets the required precision and accuracy values for the expected service life of a material in working conditions. Researchers found the utility of aluminium to be the second largest after steel. Aluminium and its metal matrix composite possess wide applications in various applications in aerospace industry, automobile industry, Constructions and even in kitchen utensils. Hybrid Al-MMCconsist of two different materials, and one will be from organic origin along with the base material. In this paper an attempt is made to bring out the importance of utilization of aluminium and the challenges concerned in manufacturing and machining of hybrid aluminium MMC.

A hybrid analytical model for open-circuit field calculation of multilayer interior permanent magnet machines

NASA Astrophysics Data System (ADS)

Zhang, Zhen; Xia, Changliang; Yan, Yan; Geng, Qiang; Shi, Tingna

2017-08-01

Due to the complicated rotor structure and nonlinear saturation of rotor bridges, it is difficult to build a fast and accurate analytical field calculation model for multilayer interior permanent magnet (IPM) machines. In this paper, a hybrid analytical model suitable for the open-circuit field calculation of multilayer IPM machines is proposed by coupling the magnetic equivalent circuit (MEC) method and the subdomain technique. In the proposed analytical model, the rotor magnetic field is calculated by the MEC method based on the Kirchhoff's law, while the field in the stator slot, slot opening and air-gap is calculated by subdomain technique based on the Maxwell's equation. To solve the whole field distribution of the multilayer IPM machines, the coupled boundary conditions on the rotor surface are deduced for the coupling of the rotor MEC and the analytical field distribution of the stator slot, slot opening and air-gap. The hybrid analytical model can be used to calculate the open-circuit air-gap field distribution, back electromotive force (EMF) and cogging torque of multilayer IPM machines. Compared with finite element analysis (FEA), it has the advantages of faster modeling, less computation source occupying and shorter time consuming, and meanwhile achieves the approximate accuracy. The analytical model is helpful and applicable for the open-circuit field calculation of multilayer IPM machines with any size and pole/slot number combination.

Magnetic Flux Distribution of Linear Machines with Novel Three-Dimensional Hybrid Magnet Arrays

PubMed Central

Yao, Nan; Yan, Liang; Wang, Tianyi; Wang, Shaoping

2017-01-01

The objective of this paper is to propose a novel tubular linear machine with hybrid permanent magnet arrays and multiple movers, which could be employed for either actuation or sensing technology. The hybrid magnet array produces flux distribution on both sides of windings, and thus helps to increase the signal strength in the windings. The multiple movers are important for airspace technology, because they can improve the system’s redundancy and reliability. The proposed design concept is presented, and the governing equations are obtained based on source free property and Maxwell equations. The magnetic field distribution in the linear machine is thus analytically formulated by using Bessel functions and harmonic expansion of magnetization vector. Numerical simulation is then conducted to validate the analytical solutions of the magnetic flux field. It is proved that the analytical model agrees with the numerical results well. Therefore, it can be utilized for the formulation of signal or force output subsequently, depending on its particular implementation. PMID:29156577

Magnetic Flux Distribution of Linear Machines with Novel Three-Dimensional Hybrid Magnet Arrays.

PubMed

Yao, Nan; Yan, Liang; Wang, Tianyi; Wang, Shaoping

2017-11-18

The objective of this paper is to propose a novel tubular linear machine with hybrid permanent magnet arrays and multiple movers, which could be employed for either actuation or sensing technology. The hybrid magnet array produces flux distribution on both sides of windings, and thus helps to increase the signal strength in the windings. The multiple movers are important for airspace technology, because they can improve the system's redundancy and reliability. The proposed design concept is presented, and the governing equations are obtained based on source free property and Maxwell equations. The magnetic field distribution in the linear machine is thus analytically formulated by using Bessel functions and harmonic expansion of magnetization vector. Numerical simulation is then conducted to validate the analytical solutions of the magnetic flux field. It is proved that the analytical model agrees with the numerical results well. Therefore, it can be utilized for the formulation of signal or force output subsequently, depending on its particular implementation.

Data on Support Vector Machines (SVM) model to forecast photovoltaic power.

PubMed

Malvoni, M; De Giorgi, M G; Congedo, P M

2016-12-01

The data concern the photovoltaic (PV) power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled "Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data" (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015) [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA) are applied to the Least Squares Support Vector Machines (LS-SVM) to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

A single-phase axially-magnetized permanent-magnet oscillating machine for miniature aerospace power sources

NASA Astrophysics Data System (ADS)

Sui, Yi; Zheng, Ping; Cheng, Luming; Wang, Weinan; Liu, Jiaqi

2017-05-01

A single-phase axially-magnetized permanent-magnet (PM) oscillating machine which can be integrated with a free-piston Stirling engine to generate electric power, is investigated for miniature aerospace power sources. Machine structure, operating principle and detent force characteristic are elaborately studied. With the sinusoidal speed characteristic of the mover considered, the proposed machine is designed by 2D finite-element analysis (FEA), and some main structural parameters such as air gap diameter, dimensions of PMs, pole pitches of both stator and mover, and the pole-pitch combinations, etc., are optimized to improve both the power density and force capability. Compared with the three-phase PM linear machines, the proposed single-phase machine features less PM use, simple control and low controller cost. The power density of the proposed machine is higher than that of the three-phase radially-magnetized PM linear machine, but lower than the three-phase axially-magnetized PM linear machine.

High Strength P/M Gears for Vehicle Transmissions - Phase 2

DTIC Science & Technology

2008-08-15

and while it was considered amenable to standard work material transfer ("blue steel" chutes for example) from other P/M processing equipment, no...depend of the machine design but should be kept to a minimum in order to minimize part transfer times. Position control of the linear axis is...Establish design of ausform gear finishing machine for P/M gears: The "Focus" part identified in phase I (New Process Planet gear P/N 17864, component

Hybrid excited claw pole generator with skewed and non-skewed permanent magnets

NASA Astrophysics Data System (ADS)

Wardach, Marcin

2017-12-01

This article contains simulation results of the Hybrid Excited Claw Pole Generator with skewed and non-skewed permanent magnets on rotor. The experimental machine has claw poles on two rotor sections, between which an excitation control coil is located. The novelty of this machine is existence of non-skewed permanent magnets on claws of one part of the rotor and skewed permanent magnets on the second one. The paper presents the construction of the machine and analysis of the influence of the PM skewing on the cogging torque and back-emf. Simulation studies enabled the determination of the cogging torque and the back-emf rms for both: the strengthening and the weakening of magnetic field. The influence of the magnets skewing on the cogging torque and the back-emf rms have also been analyzed.

A variable-mode stator consequent pole memory machine

NASA Astrophysics Data System (ADS)

Yang, Hui; Lyu, Shukang; Lin, Heyun; Zhu, Z. Q.

2018-05-01

In this paper, a variable-mode concept is proposed for the speed range extension of a stator-consequent-pole memory machine (SCPMM). An integrated permanent magnet (PM) and electrically excited control scheme is utilized to simplify the flux-weakening control instead of relatively complicated continuous PM magnetization control. Due to the nature of memory machine, the magnetization state of low coercive force (LCF) magnets can be easily changed by applying either a positive or negative current pulse. Therefore, the number of PM poles may be changed to satisfy the specific performance requirement under different speed ranges, i.e. the machine with all PM poles can offer high torque output while that with half PM poles provides wide constant power range. In addition, the SCPMM with non-magnetized PMs can be considered as a dual-three phase electrically excited reluctance machine, which can be fed by an open-winding based dual inverters that provide direct current (DC) bias excitation to further extend the speed range. The effectiveness of the proposed variable-mode operation for extending its operating region and improving the system reliability is verified by both finite element analysis (FEA) and experiments.

A hybrid algorithm optimization approach for machine loading problem in flexible manufacturing system

NASA Astrophysics Data System (ADS)

Kumar, Vijay M.; Murthy, ANN; Chandrashekara, K.

2012-05-01

The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made. Such problems are not only combinatorial optimization problems, but also happen to be non-deterministic polynomial-time-hard, making it difficult to obtain satisfactory solutions using traditional optimization techniques. In this paper, an attempt has been made to address the machine loading problem with objectives of minimization of system unbalance and maximization of throughput simultaneously while satisfying the system constraints related to available machining time and tool slot designing and using a meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as throughput and system utilization.

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

PubMed

Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

PubMed Central

Ozmutlu, H. Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

Machine Learning

DTIC Science & Technology

1990-04-01

DTIC i.LE COPY RADC-TR-90-25 Final Technical Report April 1990 MACHINE LEARNING The MITRE Corporation Melissa P. Chase Cs) CTIC ’- CT E 71 IN 2 11990...S. FUNDING NUMBERS MACHINE LEARNING C - F19628-89-C-0001 PE - 62702F PR - MOlE S. AUTHO(S) TA - 79 Melissa P. Chase WUT - 80 S. PERFORMING...341.280.5500 pm I " Aw Sig rill Ia 2110-01 SECTION 1 INTRODUCTION 1.1 BACKGROUND Research in machine learning has taken two directions in the problem of

High speed operation of permanent magnet machines

NASA Astrophysics Data System (ADS)

El-Refaie, Ayman M.

This work proposes methods to extend the high-speed operating capabilities of both the interior PM (IPM) and surface PM (SPM) machines. For interior PM machines, this research has developed and presented the first thorough analysis of how a new bi-state magnetic material can be usefully applied to the design of IPM machines. Key elements of this contribution include identifying how the unique properties of the bi-state magnetic material can be applied most effectively in the rotor design of an IPM machine by "unmagnetizing" the magnet cavity center posts rather than the outer bridges. The importance of elevated rotor speed in making the best use of the bi-state magnetic material while recognizing its limitations has been identified. For surface PM machines, this research has provided, for the first time, a clear explanation of how fractional-slot concentrated windings can be applied to SPM machines in order to achieve the necessary conditions for optimal flux weakening. A closed-form analytical procedure for analyzing SPM machines designed with concentrated windings has been developed. Guidelines for designing SPM machines using concentrated windings in order to achieve optimum flux weakening are provided. Analytical and numerical finite element analysis (FEA) results have provided promising evidence of the scalability of the concentrated winding technique with respect to the number of poles, machine aspect ratio, and output power rating. Useful comparisons between the predicted performance characteristics of SPM machines equipped with concentrated windings and both SPM and IPM machines designed with distributed windings are included. Analytical techniques have been used to evaluate the impact of the high pole number on various converter performance metrics. Both analytical techniques and FEA have been used for evaluating the eddy-current losses in the surface magnets due to the stator winding subharmonics. Techniques for reducing these losses have been

Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

PubMed

Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

2014-01-01

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

Accurate modeling of switched reluctance machine based on hybrid trained WNN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Shoujun, E-mail: sunnyway@nwpu.edu.cn; Ge, Lefei; Ma, Shaojie

2014-04-15

According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, themore » nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.« less

Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

PubMed Central

Banik, Shipra; Khodadad Khan, A. F. M.; Anwer, Mohammad

2014-01-01

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange. PMID:24701205

A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China.

PubMed

Li, Weide; Kong, Demeng; Wu, Jinran

2017-01-01

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality.

Machinability of Green Powder Metallurgy Components: Part I. Characterization of the Influence of Tool Wear

NASA Astrophysics Data System (ADS)

Robert-Perron, Etienne; Blais, Carl; Pelletier, Sylvain; Thomas, Yannig

2007-06-01

The green machining process is an interesting approach for solving the mediocre machining behavior of high-performance powder metallurgy (PM) steels. This process appears as a promising method for extending tool life and reducing machining costs. Recent improvements in binder/lubricant technologies have led to high green strength systems that enable green machining. So far, tool wear has been considered negligible when characterizing the machinability of green PM specimens. This inaccurate assumption may lead to the selection of suboptimum cutting conditions. The first part of this study involves the optimization of the machining parameters to minimize the effects of tool wear on the machinability in turning of green PM components. The second part of our work compares the sintered mechanical properties of components machined in green state with other machined after sintering.

Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces.

PubMed

Herbert, Robert; Kim, Jong-Hoon; Kim, Yun Soung; Lee, Hye Moon; Yeo, Woon-Hong

2018-01-24

Flexible hybrid electronics (FHE), designed in wearable and implantable configurations, have enormous applications in advanced healthcare, rapid disease diagnostics, and persistent human-machine interfaces. Soft, contoured geometries and time-dynamic deformation of the targeted tissues require high flexibility and stretchability of the integrated bioelectronics. Recent progress in developing and engineering soft materials has provided a unique opportunity to design various types of mechanically compliant and deformable systems. Here, we summarize the required properties of soft materials and their characteristics for configuring sensing and substrate components in wearable and implantable devices and systems. Details of functionality and sensitivity of the recently developed FHE are discussed with the application areas in medicine, healthcare, and machine interactions. This review concludes with a discussion on limitations of current materials, key requirements for next generation materials, and new application areas.

Diversity of P-element piRNA production among M' and Q strains and its association with P-M hybrid dysgenesis in Drosophila melanogaster.

PubMed

Wakisaka, Keiko Tsuji; Ichiyanagi, Kenji; Ohno, Seiko; Itoh, Masanobu

2017-01-01

Transposition of P elements in the genome causes P-M hybrid dysgenesis in Drosophila melanogaster . For the P strain, the P-M phenotypes are associated with the ability to express a class of small RNAs, called piwi-interacting small RNAs (piRNAs), that suppress the P elements in female gonads. However, little is known about the extent to which piRNAs are involved in the P-M hybrid dysgenesis in M' and Q strains, which show different abilities to regulate the P elements from P strains. To elucidate the molecular basis of the suppression of paternally inherited P elements, we analyzed the mRNA and piRNA levels of P elements in the F1 progeny between males of a P strain and nine-line females of M' or Q strains (M' or Q progenies). M' progenies showed the hybrid dysgenesis phenotype, while Q progenies did not. Consistently, the levels of P -element mRNA in both the ovaries and F1 embryos were higher in M' progenies than in Q progenies, indicating that the M' progenies have a weaker ability to suppress P -element expression. The level of P -element mRNA was inversely correlated to the level of piRNAs in F1 embryos. Importantly, the M' progenies were characterized by a lower abundance of P -element piRNAs in both young ovaries and F1 embryonic bodies. The Q progenies showed various levels of piRNAs in both young ovaries and F1 embryonic bodies despite all of the Q progenies suppressing P -element transposition in their gonad. Our results are consistent with an idea that the level of P -element piRNAs is a determinant for dividing strain types between M' and Q and that the suppression mechanisms of transposable elements, including piRNAs, are varied between natural populations.

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2005-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Hybrid Neural Network and Support Vector Machine Method for Optimization

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2007-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction

NASA Astrophysics Data System (ADS)

Benvenuto, Federico; Piana, Michele; Campi, Cristina; Massone, Anna Maria

2018-01-01

This paper introduces a novel method for flare forecasting, combining prediction accuracy with the ability to identify the most relevant predictive variables. This result is obtained by means of a two-step approach: first, a supervised regularization method for regression, namely, LASSO is applied, where a sparsity-enhancing penalty term allows the identification of the significance with which each data feature contributes to the prediction; then, an unsupervised fuzzy clustering technique for classification, namely, Fuzzy C-Means, is applied, where the regression outcome is partitioned through the minimization of a cost function and without focusing on the optimization of a specific skill score. This approach is therefore hybrid, since it combines supervised and unsupervised learning; realizes classification in an automatic, skill-score-independent way; and provides effective prediction performances even in the case of imbalanced data sets. Its prediction power is verified against NOAA Space Weather Prediction Center data, using as a test set, data in the range between 1996 August and 2010 December and as training set, data in the range between 1988 December and 1996 June. To validate the method, we computed several skill scores typically utilized in flare prediction and compared the values provided by the hybrid approach with the ones provided by several standard (non-hybrid) machine learning methods. The results showed that the hybrid approach performs classification better than all other supervised methods and with an effectiveness comparable to the one of clustering methods; but, in addition, it provides a reliable ranking of the weights with which the data properties contribute to the forecast.

Electrical test prediction using hybrid metrology and machine learning

NASA Astrophysics Data System (ADS)

Breton, Mary; Chao, Robin; Muthinti, Gangadhara Raja; de la Peña, Abraham A.; Simon, Jacques; Cepler, Aron J.; Sendelbach, Matthew; Gaudiello, John; Emans, Susan; Shifrin, Michael; Etzioni, Yoav; Urenski, Ronen; Lee, Wei Ti

2017-03-01

Electrical test measurement in the back-end of line (BEOL) is crucial for wafer and die sorting as well as comparing intended process splits. Any in-line, nondestructive technique in the process flow to accurately predict these measurements can significantly improve mean-time-to-detect (MTTD) of defects and improve cycle times for yield and process learning. Measuring after BEOL metallization is commonly done for process control and learning, particularly with scatterometry (also called OCD (Optical Critical Dimension)), which can solve for multiple profile parameters such as metal line height or sidewall angle and does so within patterned regions. This gives scatterometry an advantage over inline microscopy-based techniques, which provide top-down information, since such techniques can be insensitive to sidewall variations hidden under the metal fill of the trench. But when faced with correlation to electrical test measurements that are specific to the BEOL processing, both techniques face the additional challenge of sampling. Microscopy-based techniques are sampling-limited by their small probe size, while scatterometry is traditionally limited (for microprocessors) to scribe targets that mimic device ground rules but are not necessarily designed to be electrically testable. A solution to this sampling challenge lies in a fast reference-based machine learning capability that allows for OCD measurement directly of the electrically-testable structures, even when they are not OCD-compatible. By incorporating such direct OCD measurements, correlation to, and therefore prediction of, resistance of BEOL electrical test structures is significantly improved. Improvements in prediction capability for multiple types of in-die electrically-testable device structures is demonstrated. To further improve the quality of the prediction of the electrical resistance measurements, hybrid metrology using the OCD measurements as well as X-ray metrology (XRF) is used. Hybrid metrology

Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces

PubMed Central

Herbert, Robert; Kim, Jong-Hoon; Kim, Yun Soung; Lee, Hye Moon

2018-01-01

Flexible hybrid electronics (FHE), designed in wearable and implantable configurations, have enormous applications in advanced healthcare, rapid disease diagnostics, and persistent human-machine interfaces. Soft, contoured geometries and time-dynamic deformation of the targeted tissues require high flexibility and stretchability of the integrated bioelectronics. Recent progress in developing and engineering soft materials has provided a unique opportunity to design various types of mechanically compliant and deformable systems. Here, we summarize the required properties of soft materials and their characteristics for configuring sensing and substrate components in wearable and implantable devices and systems. Details of functionality and sensitivity of the recently developed FHE are discussed with the application areas in medicine, healthcare, and machine interactions. This review concludes with a discussion on limitations of current materials, key requirements for next generation materials, and new application areas. PMID:29364861

Position error compensation via a variable reluctance sensor applied to a Hybrid Vehicle Electric machine.

PubMed

Bucak, Ihsan Ömür

2010-01-01

In the automotive industry, electromagnetic variable reluctance (VR) sensors have been extensively used to measure engine position and speed through a toothed wheel mounted on the crankshaft. In this work, an application that already uses the VR sensing unit for engine and/or transmission has been chosen to infer, this time, the indirect position of the electric machine in a parallel Hybrid Electric Vehicle (HEV) system. A VR sensor has been chosen to correct the position of the electric machine, mainly because it may still become critical in the operation of HEVs to avoid possible vehicle failures during the start-up and on-the-road, especially when the machine is used with an internal combustion engine. The proposed method uses Chi-square test and is adaptive in a sense that it derives the compensation factors during the shaft operation and updates them in a timely fashion.

Position Error Compensation via a Variable Reluctance Sensor Applied to a Hybrid Vehicle Electric Machine

PubMed Central

Bucak, İhsan Ömür

2010-01-01

In the automotive industry, electromagnetic variable reluctance (VR) sensors have been extensively used to measure engine position and speed through a toothed wheel mounted on the crankshaft. In this work, an application that already uses the VR sensing unit for engine and/or transmission has been chosen to infer, this time, the indirect position of the electric machine in a parallel Hybrid Electric Vehicle (HEV) system. A VR sensor has been chosen to correct the position of the electric machine, mainly because it may still become critical in the operation of HEVs to avoid possible vehicle failures during the start-up and on-the-road, especially when the machine is used with an internal combustion engine. The proposed method uses Chi-square test and is adaptive in a sense that it derives the compensation factors during the shaft operation and updates them in a timely fashion. PMID:22294906

A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China

PubMed Central

Wu, Jinran

2017-01-01

Air pollution in China is becoming more serious especially for the particular matter (PM) because of rapid economic growth and fast expansion of urbanization. To solve the growing environment problems, daily PM2.5 and PM10 concentration data form January 1, 2015, to August 23, 2016, in Kunming and Yuxi (two important cities in Yunnan Province, China) are used to present a new hybrid model CI-FPA-SVM to forecast air PM2.5 and PM10 concentration in this paper. The proposed model involves two parts. Firstly, due to its deficiency to assess the possible correlation between different variables, the cointegration theory is introduced to get the input-output relationship and then obtain the nonlinear dynamical system with support vector machine (SVM), in which the parameters c and g are optimized by flower pollination algorithm (FPA). Six benchmark models, including FPA-SVM, CI-SVM, CI-GA-SVM, CI-PSO-SVM, CI-FPA-NN, and multiple linear regression model, are considered to verify the superiority of the proposed hybrid model. The empirical study results demonstrate that the proposed model CI-FPA-SVM is remarkably superior to all considered benchmark models for its high prediction accuracy, and the application of the model for forecasting can give effective monitoring and management of further air quality. PMID:28932237

Tribological and microstructural comparison of HIPped PM212 and PM212/Au self-lubricating composites

NASA Technical Reports Server (NTRS)

Bogdanski, Michael S.; Sliney, Harold E.; Dellacorte, Christopher

1992-01-01

The feasibility of replacing the silver with the volumetric equivalent of gold in the chromium carbide-based self-lubricating composite PM212 (70 wt. percent NiCo-Cr3C2, 15 percent BaF2/CaF2 eutectic) was studied. The new composite, PM212/Au has the following composition: 62 wt. percent NiCo-Cr3C2, 25 percent Au, 13 percent BaF2/CaF2 eutectic. The silver was replaced with gold to minimize the potential reactivity of the composite with possible environmental contaminants such as sulfur. The composites were fabricated by hot isostatic pressing (HIPping) and machined into pin specimens. The pins were slid against nickel-based superalloy disks. Sliding velocities ranged from 0.27 to 10.0 m/s and temperatures from 25 to 900 C. Friction coefficients ranged from 0.25 to 0.40 and wear factors for the pin and disk were typically low 10(exp -5) cu mm/N-m. HIPped PM212 measured fully dense, whereas PM212/Au had 15 percent residual porosity. Examination of the microstructures with optical and scanning electron microscopy revealed the presence of pores in PM212/Au that were not present in PM212. Though the exact reason for the residual porosity in PM212/Au was not determined, it may be due to particle morphology differences between the gold and silver and their effect on powder metallurgy processing.

A dual-channel flux-switching permanent magnet motor for hybrid electric vehicles

NASA Astrophysics Data System (ADS)

Hua, Wei; Wu, Zhongze; Cheng, Ming; Wang, Baoan; Zhang, Jianzhong; Zhou, Shigui

2012-04-01

The flux-switching permanent magnet (FSPM) motor is a relatively novel brushless machine having both magnets and concentrated windings in the stator, which exhibits inherently sinusoidal PM flux-linkage, back-EMF waveforms, and high torque capability. However, in the application of hybrid electric vehicles, it is essential to prevent magnets and armature windings moving in radial direction due to the possible vibration during operation, and to ensure fault-tolerant capability. Hence, in this paper based on an original FSPM motor, a dual-channel FSPM (DC-FSPM) motor with modified structure to fix both armature windings and magnets and improved reliability is proposed for a practical 10 kW integral starter/generator (ISG) in hybrid electric vehicles. The influences of different solutions and the end-effect on the static characteristics, are evaluated based on the 2D and 3D finite element analysis, respectively. Finally, both the predicted and experimental results, compared with a prototype DC-FSPM motor and an interior PM motor used in Honda Civic, confirm that the more sinusoidal back-EMF waveform and lower torque ripple can be achieved in the DC-FSPM motor, whereas the torque is smaller under the same coil current.

A hybrid genetic algorithm-extreme learning machine approach for accurate significant wave height reconstruction

NASA Astrophysics Data System (ADS)

Alexandre, E.; Cuadra, L.; Nieto-Borge, J. C.; Candil-García, G.; del Pino, M.; Salcedo-Sanz, S.

2015-08-01

Wave parameters computed from time series measured by buoys (significant wave height Hs, mean wave period, etc.) play a key role in coastal engineering and in the design and operation of wave energy converters. Storms or navigation accidents can make measuring buoys break down, leading to missing data gaps. In this paper we tackle the problem of locally reconstructing Hs at out-of-operation buoys by using wave parameters from nearby buoys, based on the spatial correlation among values at neighboring buoy locations. The novelty of our approach for its potential application to problems in coastal engineering is twofold. On one hand, we propose a genetic algorithm hybridized with an extreme learning machine that selects, among the available wave parameters from the nearby buoys, a subset FnSP with nSP parameters that minimizes the Hs reconstruction error. On the other hand, we evaluate to what extent the selected parameters in subset FnSP are good enough in assisting other machine learning (ML) regressors (extreme learning machines, support vector machines and gaussian process regression) to reconstruct Hs. The results show that all the ML method explored achieve a good Hs reconstruction in the two different locations studied (Caribbean Sea and West Atlantic).

Source apportionment of PM10 and PM2.5 in major urban Greek agglomerations using a hybrid source-receptor modeling process.

PubMed

Argyropoulos, G; Samara, C; Diapouli, E; Eleftheriadis, K; Papaoikonomou, K; Kungolos, A

2017-12-01

A hybrid source-receptor modeling process was assembled, to apportion and infer source locations of PM 10 and PM 2.5 in three heavily-impacted urban areas of Greece, during the warm period of 2011, and the cold period of 2012. The assembled process involved application of an advanced computational procedure, the so-called Robotic Chemical Mass Balance (RCMB) model. Source locations were inferred using two well-established probability functions: (a) the Conditional Probability Function (CPF), to correlate the output of RCMB with local wind directional data, and (b) the Potential Source Contribution Function (PSCF), to correlate the output of RCMB with 72h air-mass back-trajectories, arriving at the receptor sites, during sampling. Regarding CPF, a higher-level conditional probability function was defined as well, from the common locus of CPF sectors derived for neighboring receptor sites. With respect to PSCF, a non-parametric bootstrapping method was applied to discriminate the statistically significant values. RCMB modeling showed that resuspended dust is actually one of the main barriers for attaining the European Union (EU) limit values in Mediterranean urban agglomerations, where the drier climate favors build-up. The shift in the energy mix of Greece (caused by the economic recession) was also evidenced, since biomass burning was found to contribute more significantly to the sampling sites belonging to the coldest climatic zone, particularly during the cold period. The CPF analysis showed that short-range transport of anthropogenic emissions from urban traffic to urban background sites was very likely to have occurred, within all the examined urban agglomerations. The PSCF analysis confirmed that long-range transport of primary and/or secondary aerosols may indeed be possible, even from distances over 1000km away from study areas. Copyright © 2017 Elsevier B.V. All rights reserved.

Research on a new magnetic-field-modulated brushless double-rotor machine with sinusoidal-permeance modulating ring

NASA Astrophysics Data System (ADS)

Zheng, Ping; Liu, Jiaqi; Bai, Jingang; Song, Zhiyi; Liu, Yong

2017-05-01

The magnetic-field-modulated brushless double-rotor machine (MFM-BDRM), composed of a stator, a modulating ring rotor, and a PM rotor, is a kind of power-split device for hybrid electric vehicles (HEVs). In this paper, a new MFM-BDRM with sinusoidal-permeance modulating ring named Sinusoidal-Permeance-Modulating-Ring Brushless Double-Rotor Machine (SPMR-BDRM) is proposed to solve the problem of poor mechanical strength and large iron loss. The structure and the operating principle of the MFM-BDRM are introduced. The design principle of the sinusoidal-permeance modulating ring is analyzed and derived. The main idea of that is to minimize the harmonic permeance of air gap, thereby the harmonic magnetic fields can be restrained. There are comparisons between a MFM-BDRM with sinusoidal-permeance modulating ring and a same size MFM-BDRM with traditional modulating ring, including magnetic field distributions and electromagnetic performances. Most importantly, the iron losses are compared under six different conditions. The result indicates that the harmonic magnetic fields in the air gap are restrained; the electromagnetic torque and power factor are almost the same with same armature current; the torque ripples of the modulating ring rotor and the PM rotor are reduced; the stator loss is reduced by 13% at least and the PM loss is reduced by 20% at least compared with the same size traditional MFM-BDRM under the same operating conditions.

Electric machine for hybrid motor vehicle

DOEpatents

Hsu, John Sheungchun

2007-09-18

A power system for a motor vehicle having an internal combustion engine and an electric machine is disclosed. The electric machine has a stator, a permanent magnet rotor, an uncluttered rotor spaced from the permanent magnet rotor, and at least one secondary core assembly. The power system also has a gearing arrangement for coupling the internal combustion engine to wheels on the vehicle thereby providing a means for the electric machine to both power assist and brake in relation to the output of the internal combustion engine.

Estimating Ground-Level Particulate Matter (PM) Concentration using Satellite-derived Aerosol Optical Depth (AOD)

NASA Astrophysics Data System (ADS)

Park, Seohui; Im, Jungho

2017-04-01

Atmospheric aerosols are strongly associated with adverse human health effects. In particular, particulate matter less than 10 micrometers and 2.5 micrometers (i.e., PM10 and PM2.5, respectively) can cause cardiovascular and lung diseases such as asthma and chronic obstructive pulmonary disease (COPD). Air quality including PM has typically been monitored using station-based in-situ measurements over the world. However, in situ measurements do not provide spatial continuity over large areas. An alternative approach is to use satellite remote sensing as it provides data over vast areas at high temporal resolution. The literature shows that PM concentrations are related with Aerosol Optical Depth (AOD) that is derived from satellite observations, but it is still difficult to identify PM concentrations directly from AOD. Some studies used statistical approaches for estimating PM concentrations from AOD while some others combined numerical models and satellite-derived AOD. In this study, satellite-derived products were used to estimate ground PM concentrations based on machine learning over South Korea. Satellite-derived products include AOD from Geostationary Ocean Color Imager (GOCI), precipitation from Tropical Rainfall Measuring Mission (TRMM), soil moisture from AMSR-2, elevation from Shuttle Radar Topography Mission (SRTM), and land cover, land surface temperature and normalized difference vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS). PM concentrations data were collected from 318 stations. A statistical ordinary least squares (OLS) approach was also tested and compared with the machine learning approach (i.e., random forest). PM concentration was estimated during spring season (from March to May) in 2015 that typically shows high concentration of PM. The randomly selected 80% of data were used for model calibration and the remaining 20% were used for validation. The developed models were further tested for prediction of PM

Satellite-based PM concentrations and their application to COPD in Cleveland, OH

PubMed Central

Kumar, Naresh; Liang, Dong; Comellas, Alejandro; Chu, Allen D.; Abrams, Thad

2014-01-01

A hybrid approach is proposed to estimate exposure to fine particulate matter (PM2.5) at a given location and time. This approach builds on satellite-based aerosol optical depth (AOD), air pollution data from sparsely distributed Environmental Protection Agency (EPA) sites and local time–space Kriging, an optimal interpolation technique. Given the daily global coverage of AOD data, we can develop daily estimate of air quality at any given location and time. This can assure unprecedented spatial coverage, needed for air quality surveillance and management and epidemiological studies. In this paper, we developed an empirical relationship between the 2 km AOD and PM2.5 data from EPA sites. Extrapolating this relationship to the study domain resulted in 2.3 million predictions of PM2.5 between 2000 and 2009 in Cleveland Metropolitan Statistical Area (MSA). We have developed local time–space Kriging to compute exposure at a given location and time using the predicted PM2.5. Daily estimates of PM2.5 were developed for Cleveland MSA between 2000 and 2009 at 2.5 km spatial resolution; 1.7 million (~79.8%) of 2.13 million predictions required for multiyear and geographic domain were robust. In the epidemiological application of the hybrid approach, admissions for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) was examined with respect to time–space lagged PM2.5 exposure. Our analysis suggests that the risk of AECOPD increases 2.3% with a unit increase in PM2.5 exposure within 9 days and 0.05° (~5 km) distance lags. In the aggregated analysis, the exposed groups (who experienced exposure to PM2.5 >15.4 μg/m3) were 54% more likely to be admitted for AECOPD than the reference group. The hybrid approach offers greater spatiotemporal coverage and reliable characterization of ambient concentration than conventional in situ monitoring-based approaches. Thus, this approach can potentially reduce exposure misclassification errors in the conventional

Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina.

PubMed

Chang, Shih Ying; Vizuete, William; Serre, Marc; Vennam, Lakshmi Pradeepa; Omary, Mohammad; Isakov, Vlad; Breen, Michael; Arunachalam, Saravanan

2017-12-01

To quantify the on-road PM 2.5 -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM 2.5 -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM 2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM 2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM 2.5 where the hybrid approach estimated 2.5 times more primary on-road PM 2.5 -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM 2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM 2.5 and suggesting that previous studies may have underestimated premature mortality due to PM 2.5 from traffic-related emissions. © 2017 Society for Risk Analysis.

A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

NASA Astrophysics Data System (ADS)

Wang, Aimeng; Guo, Jiayu

2017-12-01

A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

Channel Efficiency with Security Enhancement for Remote Condition Monitoring of Multi Machine System Using Hybrid Huffman Coding

NASA Astrophysics Data System (ADS)

Datta, Jinia; Chowdhuri, Sumana; Bera, Jitendranath

2016-12-01

This paper presents a novel scheme of remote condition monitoring of multi machine system where a secured and coded data of induction machine with different parameters is communicated between a state-of-the-art dedicated hardware Units (DHU) installed at the machine terminal and a centralized PC based machine data management (MDM) software. The DHUs are built for acquisition of different parameters from the respective machines, and hence are placed at their nearby panels in order to acquire different parameters cost effectively during their running condition. The MDM software collects these data through a communication channel where all the DHUs are networked using RS485 protocol. Before transmitting, the parameter's related data is modified with the adoption of differential pulse coded modulation (DPCM) and Huffman coding technique. It is further encrypted with a private key where different keys are used for different DHUs. In this way a data security scheme is adopted during its passage through the communication channel in order to avoid any third party attack into the channel. The hybrid mode of DPCM and Huffman coding is chosen to reduce the data packet length. A MATLAB based simulation and its practical implementation using DHUs at three machine terminals (one healthy three phase, one healthy single phase and one faulty three phase machine) proves its efficacy and usefulness for condition based maintenance of multi machine system. The data at the central control room are decrypted and decoded using MDM software. In this work it is observed that Chanel efficiency with respect to different parameter measurements has been increased very much.

Two-machine flow shop scheduling integrated with preventive maintenance planning

NASA Astrophysics Data System (ADS)

Wang, Shijin; Liu, Ming

2016-02-01

This paper investigates an integrated optimisation problem of production scheduling and preventive maintenance (PM) in a two-machine flow shop with time to failure of each machine subject to a Weibull probability distribution. The objective is to find the optimal job sequence and the optimal PM decisions before each job such that the expected makespan is minimised. To investigate the value of integrated scheduling solution, computational experiments on small-scale problems with different configurations are conducted with total enumeration method, and the results are compared with those of scheduling without maintenance but with machine degradation, and individual job scheduling combined with independent PM planning. Then, for large-scale problems, four genetic algorithm (GA) based heuristics are proposed. The numerical results with several large problem sizes and different configurations indicate the potential benefits of integrated scheduling solution and the results also show that proposed GA-based heuristics are efficient for the integrated problem.

A hybrid least squares support vector machines and GMDH approach for river flow forecasting

NASA Astrophysics Data System (ADS)

Samsudin, R.; Saad, P.; Shabri, A.

2010-06-01

This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.

Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

PubMed Central

Yang, Zhutian; Wu, Zhilu; Yin, Zhendong; Quan, Taifan; Sun, Hongjian

2013-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches. PMID:23344380

Hybrid-secondary uncluttered induction machine

DOEpatents

Hsu, John S.

2001-01-01

An uncluttered secondary induction machine (100) includes an uncluttered rotating transformer (66) which is mounted on the same shaft as the rotor (73) of the induction machine. Current in the rotor (73) is electrically connected to current in the rotor winding (67) of the transformer, which is not electrically connected to, but is magnetically coupled to, a stator secondary winding (40). The stator secondary winding (40) is alternately connected to an effective resistance (41), an AC source inverter (42) or a magnetic switch (43) to provide a cost effective slip-energy-controlled, adjustable speed, induction motor that operates over a wide speed range from below synchronous speed to above synchronous speed based on the AC line frequency fed to the stator.

Application of XGBoost algorithm in hourly PM2.5 concentration prediction

NASA Astrophysics Data System (ADS)

Pan, Bingyue

2018-02-01

In view of prediction techniques of hourly PM2.5 concentration in China, this paper applied the XGBoost(Extreme Gradient Boosting) algorithm to predict hourly PM2.5 concentration. The monitoring data of air quality in Tianjin city was analyzed by using XGBoost algorithm. The prediction performance of the XGBoost method is evaluated by comparing observed and predicted PM2.5 concentration using three measures of forecast accuracy. The XGBoost method is also compared with the random forest algorithm, multiple linear regression, decision tree regression and support vector machines for regression models using computational results. The results demonstrate that the XGBoost algorithm outperforms other data mining methods.

Controlling corrosion rate of Magnesium alloy using powder mixed electrical discharge machining

NASA Astrophysics Data System (ADS)

Razak, M. A.; Rani, A. M. A.; Saad, N. M.; Littlefair, G.; Aliyu, A. A.

2018-04-01

Biomedical implant can be divided into permanent and temporary employment. The duration of a temporary implant applied to children and adult is different due to different bone healing rate among the children and adult. Magnesium and its alloys are compatible for the biodegradable implanting application. Nevertheless, it is difficult to control the degradation rate of magnesium alloy to suit the application on both the children and adult. Powder mixed electrical discharge machining (PM-EDM) method, a modified EDM process, has high capability to improve the EDM process efficiency and machined surface quality. The objective of this paper is to establish a formula to control the degradation rate of magnesium alloy using the PM-EDM method. The different corrosion rate of machined surface is hypothesized to be obtained by having different combinations of PM-EDM operation inputs. PM-EDM experiments are conducted using an opened-loop PM-EDM system and the in-vitro corrosion tests are carried out on the machined surface of each specimen. There are four operation inputs investigated in this study which are zinc powder concentration, peak current, pulse on-time and pulse off-time. The results indicate that zinc powder concentration is significantly affecting the response with 2 g/l of zinc powder concentration obtaining the lowest corrosion rate. The high localized temperature at the cutting zone in spark erosion process causes some of the zinc particles get deposited on the machined surface, hence improving the surface characteristics. The suspended zinc particles in the dielectric fluid have also improve the sparking efficiency and the uniformity of sparks distribution. From the statistical analysis, a formula was developed to control the corrosion rate of magnesium alloy within the range from 0.000183 mm/year to 0.001528 mm/year.

Daily River Flow Forecasting with Hybrid Support Vector Machine – Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Zaini, N.; Malek, M. A.; Yusoff, M.; Mardi, N. H.; Norhisham, S.

2018-04-01

The application of artificial intelligence techniques for river flow forecasting can further improve the management of water resources and flood prevention. This study concerns the development of support vector machine (SVM) based model and its hybridization with particle swarm optimization (PSO) to forecast short term daily river flow at Upper Bertam Catchment located in Cameron Highland, Malaysia. Ten years duration of historical rainfall, antecedent river flow data and various meteorology parameters data from 2003 to 2012 are used in this study. Four SVM based models are proposed which are SVM1, SVM2, SVM-PSO1 and SVM-PSO2 to forecast 1 to 7 day ahead of river flow. SVM1 and SVM-PSO1 are the models with historical rainfall and antecedent river flow as its input, while SVM2 and SVM-PSO2 are the models with historical rainfall, antecedent river flow data and additional meteorological parameters as input. The performances of the proposed model are measured in term of RMSE and R2 . It is found that, SVM2 outperformed SVM1 and SVM-PSO2 outperformed SVM-PSO1 which meant the additional meteorology parameters used as input to the proposed models significantly affect the model performances. Hybrid models SVM-PSO1 and SVM-PSO2 yield higher performances as compared to SVM1 and SVM2. It is found that hybrid models are more effective in forecasting river flow at 1 to 7 day ahead at the study area.

Dynamism in a Semiconductor Industrial Machine Allocation Problem using a Hybrid of the Bio-inspired and Musical-Harmony Approach

NASA Astrophysics Data System (ADS)

Kalsom Yusof, Umi; Nor Akmal Khalid, Mohd

2015-05-01

Semiconductor industries need to constantly adjust to the rapid pace of change in the market. Most manufactured products usually have a very short life cycle. These scenarios imply the need to improve the efficiency of capacity planning, an important aspect of the machine allocation plan known for its complexity. Various studies have been performed to balance productivity and flexibility in the flexible manufacturing system (FMS). Many approaches have been developed by the researchers to determine the suitable balance between exploration (global improvement) and exploitation (local improvement). However, not much work has been focused on the domain of machine allocation problem that considers the effects of machine breakdowns. This paper develops a model to minimize the effect of machine breakdowns, thus increasing the productivity. The objectives are to minimize system unbalance and makespan as well as increase throughput while satisfying the technological constraints such as machine time availability. To examine the effectiveness of the proposed model, results for throughput, system unbalance and makespan on real industrial datasets were performed with applications of intelligence techniques, that is, a hybrid of genetic algorithm and harmony search. The result aims to obtain a feasible solution to the domain problem.

PmRunt regulated by Pm-miR-183 participates in nacre formation possibly through promoting the expression of collagen VI-like and Nacrein in pearl oyster Pinctada martensii.

PubMed

Zheng, Zhe; Du, Xiaodong; Xiong, Xinwei; Jiao, Yu; Deng, Yuewen; Wang, Qingheng; Huang, Ronglian

2017-01-01

Heterodimeric PEBP2/CBFs are key regulators in diverse biological processes, such as haematopoietic stem-cell generation, bone formation and cancers. In this work, we cloned runt-like transcriptional factor (designated as PmRunt) and CBF β (designated as PmCBF) gene, which comprise the heterodimeric transcriptional factor in Pinctada martensii. PmRunt was identified with an open reading frame that encodes 545 amino acids and has typical Runt domain. Phylogenetic analysis results speculated that runt-like transcriptional factors (RDs) in vertebrates and invertebrates are separated into two branches. In molluscs, PmRunt and other RDs are clustered in one of these branches. Direct interaction between PmRunt and PmCBF was evidenced by yeast two-hybrid assay results. Gene repression by RNA interference decreased the expression level of PmRunt, and subsequent observation of the inner surface of the nacre by scanning electron microscopy demonstrated disordered growth. The luciferase activities of reporters that contain promoter regions of Collagen VI-like (PmColVI) and PmNacrein were enhanced by PmRunt. Meanwhile, Pm-miR-183 apparently inhibited the relative luciferase activity of reporters containing the 3'-UTR of PmRunt. The expression level of PmRunt was repressed after Pm-miR-183 was overexpressed in the mantle tissue. Therefore, we proposed that PmRunt could be targeted by Pm-miR-183 and regulate the transcription of PmColVI and PmNacrein by increasing their transcriptional activity, thereby governing nacre formation.

A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

NASA Astrophysics Data System (ADS)

Aziz, Nur Liyana Afiqah Abdul; Siah Yap, Keem; Afif Bunyamin, Muhammad

2013-06-01

This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of "computing the word". The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.

Flexible and freestanding supercapacitor based on nanostructured poly(m-aminophenol)/carbon nanofiber hybrid mats with high energy and power densities

NASA Astrophysics Data System (ADS)

Choudhury, Arup; Dey, Baban; Sinha Mahapatra, Susanta; Kim, Doo-Won; Yang, Kap-Seung; Yang, Duck-Joo

2018-04-01

Nanostructured poly(m-aminophenol) (PmAP) coated freestanding carbon nanofiber (CNF) mats were fabricated through simple in situ rapid-mixing polymerization of m-aminophenol in the presence of a CNF mat for flexible solid-state supercapacitors. The surface compositions, morphology and pore structure of the hybrid mats were characterized by using various techniques, e.g., FTIR, Raman, XRD, FE-SEM, TEM, and N2 absorption. The results show that the PmAP nanoparticles were homogeneously deposited on CNF surfaces and formed a thin flexible hybrid mat, which were directly used to made electrodes for electrochemical analysis without using any binders or conductive additives. The electrochemical performances of the hybrid mats were easily tailored by varying the PmAP loading on a hybrid electrode. The PmAP/CNF-10 hybrid electrode with a relatively low PmAP loading (> 42 wt%) showed a high specific capacitance of 325.8 F g-1 and a volumetric capacitance of 273.6 F cm-3 at a current density of 0.5 A g-1, together with a specific capacitance retention of 196.2 F g-1 at 20 A g-1. The PmAP/CNF-10 hybrid electrode showed good cycling stability with 88.2% capacitance retention after 5000 cycles. A maximum energy density of 45.2 Wh kg-1 and power density of 20.4 kW kg-1 were achieved for the PmAP/CNF-10 hybrid electrode. This facile and cost-effective synthesis of a flexible binder-free PmAP/CNF hybrid mat with excellent capacitive performances encourages its possible commercial exploitation.

Hybrid Metaheuristics for Solving a Fuzzy Single Batch-Processing Machine Scheduling Problem

PubMed Central

Molla-Alizadeh-Zavardehi, S.; Tavakkoli-Moghaddam, R.; Lotfi, F. Hosseinzadeh

2014-01-01

This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA), variable neighborhood search (VNS), and simulated annealing (SA) frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms. PMID:24883359

A hybrid dynamic harmony search algorithm for identical parallel machines scheduling

NASA Astrophysics Data System (ADS)

Chen, Jing; Pan, Quan-Ke; Wang, Ling; Li, Jun-Qing

2012-02-01

In this article, a dynamic harmony search (DHS) algorithm is proposed for the identical parallel machines scheduling problem with the objective to minimize makespan. First, an encoding scheme based on a list scheduling rule is developed to convert the continuous harmony vectors to discrete job assignments. Second, the whole harmony memory (HM) is divided into multiple small-sized sub-HMs, and each sub-HM performs evolution independently and exchanges information with others periodically by using a regrouping schedule. Third, a novel improvisation process is applied to generate a new harmony by making use of the information of harmony vectors in each sub-HM. Moreover, a local search strategy is presented and incorporated into the DHS algorithm to find promising solutions. Simulation results show that the hybrid DHS (DHS_LS) is very competitive in comparison to its competitors in terms of mean performance and average computational time.

Hybrid polylingual object model: an efficient and seamless integration of Java and native components on the Dalvik virtual machine.

PubMed

Huang, Yukun; Chen, Rong; Wei, Jingbo; Pei, Xilong; Cao, Jing; Prakash Jayaraman, Prem; Ranjan, Rajiv

2014-01-01

JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded.

Hybrid micromachining using a nanosecond pulsed laser and micro EDM

NASA Astrophysics Data System (ADS)

Kim, Sanha; Kim, Bo Hyun; Chung, Do Kwan; Shin, Hong Shik; Chu, Chong Nam

2010-01-01

Micro electrical discharge machining (micro EDM) is a well-known precise machining process that achieves micro structures of excellent quality for any conductive material. However, the slow machining speed and high tool wear are main drawbacks of this process. Though the use of deionized water instead of kerosene as a dielectric fluid can reduce the tool wear and increase the machine speed, the material removal rate (MRR) is still low. In contrast, laser ablation using a nanosecond pulsed laser is a fast and non-wear machining process but achieves micro figures of rather low quality. Therefore, the integration of these two processes can overcome the respective disadvantages. This paper reports a hybrid process of a nanosecond pulsed laser and micro EDM for micromachining. A novel hybrid micromachining system that combines the two discrete machining processes is introduced. Then, the feasibility and characteristics of the hybrid machining process are investigated compared to conventional EDM and laser ablation. It is verified experimentally that the machining time can be effectively reduced in both EDM drilling and milling by rapid laser pre-machining prior to micro EDM. Finally, some examples of complicated 3D micro structures fabricated by the hybrid process are shown.

A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning

NASA Astrophysics Data System (ADS)

Houborg, Rasmus; McCabe, Matthew F.

2018-01-01

With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory 'predictor' variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association with

Physical properties of particulate matter (PM) from late model heavy-duty diesel vehicles operating with advanced PM and NO x emission control technologies

NASA Astrophysics Data System (ADS)

Biswas, Subhasis; Hu, Shaohua; Verma, Vishal; Herner, Jorn D.; Robertson, William H.; Ayala, Alberto; Sioutas, Constantinos

Emission control technologies designed to meet the 2007 and 2010 emission standards for heavy-duty diesel vehicles (HDDV) remove effectively the non-volatile fraction of particles, but are comparatively less efficient at controlling the semi-volatile components. A collaborative study between the California Air Resources Board (CARB) and the University of Southern California was initiated to investigate the physicochemical and toxicological characteristics of the semi-volatile and non-volatile particulate matter (PM) fractions from HDDV emissions. This paper reports the physical properties, including size distribution, volatility (in terms of number and mass), surface diameter, and agglomeration of particles emitted from HDDV retrofitted with advanced emission control devices. Four vehicles in combination with six after-treatment devices (V-SCRT ®, Z-SCRT ®, CRT ®, DPX, Hybrid-CCRT ®, EPF) were tested under three driving cycles: steady state (cruise), transient (urban dynamometer driving schedule, UDDS), and idle. An HDDV without any control device is served as the baseline vehicle. Substantial reduction of PM mass emissions (>90%) was accomplished for the HDDV operating with advanced emission control technologies. This reduction was not observed for particle number concentrations under cruise conditions, with the exceptions of the Hybrid-CCRT ® and EPF vehicles, which were efficient in controlling both—mass and number emissions. In general, significant nucleation mode particles (<50 nm) were formed during cruise cycles in comparison with the UDDS cycles, which emit higher PM mass in the accumulation mode. The nucleation mode particles (<50 nm) were mainly internally mixed, and evaporated considerably between 150 and 230 °C. Compared to the baseline vehicle, particles from vehicles with controls (except of the Hybrid-CCRT ®) had a higher mass specific surface area.

Indoor PM1, PM2.5, PM10 and outdoor PM2.5 concentrations in primary schools in Sari, Iran.

PubMed

Mohammadyan, Mahmoud; Shabankhani, Bijan

2013-09-01

This study was carried out to determine the distribution of particles in classrooms in primary schools located in the centre of the city of Sari, Iran and identify the relationship between indoor classroom particle levels and outdoor PM2.5 concentrations. Outdoor PM2.5 and indoor PM1, PM2.5, and PM10 were monitored using a real-time Micro Dust Pro monitor and a GRIMM monitor, respectively. Both monitors were calibrated by gravimetric method using filters. The Kolmogorov-Smirnov test showed that all indoor and outdoor data fitted normal distribution. Mean indoor PM1, PM2.5, PM10 and outdoor PM2.5 concentrations for all of the classrooms were 17.6 μg m(-3), 46.6 μg m(-3), 400.9 μg m(-3), and 36.9 μg m(-3), respectively. The highest levels of indoor and outdoor PM2.5 concentrations were measured at the Shahed Boys School (69.1 μg m(-3) and 115.8 μg m(-3), respectively). The Kazemi school had the lowest levels of indoor and outdoor PM2.5 (29.1 μg m(-3) and 15.5 μg m(-3), respectively). In schools located near both main and small roads, the association between indoor fine particle (PM2.5 and PM1) and outdoor PM2.5 levels was stronger than that between indoor PM10 and outdoor PM2.5 levels. Mean indoor PM2.5 and PM10 and outdoor PM2.5 were higher than the standards for PM2.5 and PM10, and there was a good correlation between indoor and outdoor fine particle concentrations.

Repurposing mainstream CNC machine tools for laser-based additive manufacturing

NASA Astrophysics Data System (ADS)

Jones, Jason B.

2016-04-01

The advent of laser technology has been a key enabler for industrial 3D printing, known as Additive Manufacturing (AM). Despite its commercial success and unique technical capabilities, laser-based AM systems are not yet able to produce parts with the same accuracy and surface finish as CNC machining. To enable the geometry and material freedoms afforded by AM, yet achieve the precision and productivity of CNC machining, hybrid combinations of these two processes have started to gain traction. To achieve the benefits of combined processing, laser technology has been integrated into mainstream CNC machines - effectively repurposing them as hybrid manufacturing platforms. This paper reviews how this engineering challenge has prompted beam delivery innovations to allow automated changeover between laser processing and machining, using standard CNC tool changers. Handling laser-processing heads using the tool changer also enables automated change over between different types of laser processing heads, further expanding the breadth of laser processing flexibility in a hybrid CNC. This paper highlights the development, challenges and future impact of hybrid CNCs on laser processing.

Hybrid PolyLingual Object Model: An Efficient and Seamless Integration of Java and Native Components on the Dalvik Virtual Machine

PubMed Central

Huang, Yukun; Chen, Rong; Wei, Jingbo; Pei, Xilong; Cao, Jing; Prakash Jayaraman, Prem; Ranjan, Rajiv

2014-01-01

JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded. PMID:25110745

Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient.

PubMed

Resquin, F; Ibañez, J; Gonzalez-Vargas, J; Brunetti, F; Dimbwadyo, I; Alves, S; Carrasco, L; Torres, L; Pons, Jose Luis

2016-08-01

Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user's movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

NASA Astrophysics Data System (ADS)

Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

2012-08-01

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

Insect-machine interface based neurocybernetics.

PubMed

Bozkurt, Alper; Gilmour, Robert F; Sinha, Ayesa; Stern, David; Lal, Amit

2009-06-01

We present details of a novel bioelectric interface formed by placing microfabricated probes into insect during metamorphic growth cycles. The inserted microprobes emerge with the insect where the development of tissue around the electronics during the pupal development allows mechanically stable and electrically reliable structures coupled to the insect. Remarkably, the insects do not react adversely or otherwise to the inserted electronics in the pupae stage, as is true when the electrodes are inserted in adult stages. We report on the electrical and mechanical characteristics of this novel bioelectronic interface, which we believe would be adopted by many investigators trying to investigate biological behavior in insects with negligible or minimal traumatic effect encountered when probes are inserted in adult stages. This novel insect-machine interface also allows for hybrid insect-machine platforms for further studies. As an application, we demonstrate our first results toward navigation of flight in moths. When instrumented with equipment to gather information for environmental sensing, such insects potentially can assist man to monitor the ecosystems that we share with them for sustainability. The simplicity of the optimized surgical procedure we invented allows for batch insertions to the insect for automatic and mass production of such hybrid insect-machine platforms. Therefore, our bioelectronic interface and hybrid insect-machine platform enables multidisciplinary scientific and engineering studies not only to investigate the details of insect behavioral physiology but also to control it.

Energy harvesting using AC machines with high effective pole count

NASA Astrophysics Data System (ADS)

Geiger, Richard Theodore

In this thesis, ways to improve the power conversion of rotating generators at low rotor speeds in energy harvesting applications were investigated. One method is to increase the pole count, which increases the generator back-emf without also increasing the I2R losses, thereby increasing both torque density and conversion efficiency. One machine topology that has a high effective pole count is a hybrid "stepper" machine. However, the large self inductance of these machines decreases their power factor and hence the maximum power that can be delivered to a load. This effect can be cancelled by the addition of capacitors in series with the stepper windings. A circuit was designed and implemented to automatically vary the series capacitance over the entire speed range investigated. The addition of the series capacitors improved the power output of the stepper machine by up to 700%. At low rotor speeds, with the addition of series capacitance, the power output of the hybrid "stepper" was more than 200% that of a similarly sized PMDC brushed motor. Finally, in this thesis a hybrid lumped parameter / finite element model was used to investigate the impact of number, shape and size of the rotor and stator teeth on machine performance. A typical off-the-shelf hybrid stepper machine has significant cogging torque by design. This cogging torque is a major problem in most small energy harvesting applications. In this thesis it was shown that the cogging and ripple torque can be dramatically reduced. These findings confirm that high-pole-count topologies, and specifically the hybrid stepper configuration, are an attractive choice for energy harvesting applications.

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

PubMed Central

Murat, Miraemiliana; Abu, Arpah; Yap, Hwa Jen; Yong, Kien-Thai

2017-01-01

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson’s coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach.

PubMed

Murat, Miraemiliana; Chang, Siow-Wee; Abu, Arpah; Yap, Hwa Jen; Yong, Kien-Thai

2017-01-01

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99

Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

PubMed Central

2013-01-01

Background Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers. Results In the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3-input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81%; AUC = 0.90) for the oral cancer prognosis. Conclusions The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies. PMID:23725313

An experimental investigation on orthogonal cutting of hybrid CFRP/Ti stacks

NASA Astrophysics Data System (ADS)

Xu, Jinyang; El Mansori, Mohamed

2016-10-01

Hybrid CFRP/Ti stack has been widely used in the modern aerospace industry owing to its superior mechanical/physical properties and excellent structural functions. Several applications require mechanical machining of these hybrid composite stacks in order to achieve dimensional accuracy and assembly performance. However, machining of such composite-to-metal alliance is usually an extremely challenging task in the manufacturing sectors due to the disparate natures of each stacked constituent and their respective poor machinability. Special issues may arise from the high force/heat generation, severe subsurface damage and rapid tool wear. To study the fundamental mechanisms controlling the bi-material machining, this paper presented an experimental study on orthogonal cutting of hybrid CFRP/Ti stack by using superior polycrystalline diamond (PCD) tipped tools. The utilized cutting parameters for hybrid CFRP/Ti machining were rigorously adopted through a compromise selection due to the disparate machinability behaviors of the CFRP laminate and Ti alloy. The key cutting responses in terms of cutting force generation, machined surface quality and tool wear mechanism were precisely addressed. The experimental results highlighted the involved five stages of CFRP/Ti cutting and the predominant crater wear and edge fracture failure governing the PCD cutting process.

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

PubMed

Shang, Qiang; Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang

2016-01-01

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine

PubMed Central

Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang

2016-01-01

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust. PMID:27551829

Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5

NASA Astrophysics Data System (ADS)

Ausati, Shadi; Amanollahi, Jamil

2016-10-01

Since Sanandaj is considered one of polluted cities of Iran, prediction of any type of pollution especially prediction of suspended particles of PM2.5, which are the cause of many diseases, could contribute to health of society by timely announcements and prior to increase of PM2.5. In order to predict PM2.5 concentration in the Sanandaj air the hybrid models consisting of an ensemble empirical mode decomposition and general regression neural network (EEMD-GRNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), principal component regression (PCR), and linear model such as multiple liner regression (MLR) model were used. In these models the data of suspended particles of PM2.5 were the dependent variable and the data related to air quality including PM2.5, PM10, SO2, NO2, CO, O3 and meteorological data including average minimum temperature (Min T), average maximum temperature (Max T), average atmospheric pressure (AP), daily total precipitation (TP), daily relative humidity level of the air (RH) and daily wind speed (WS) for the year 2014 in Sanandaj were the independent variables. Among the used models, EEMD-GRNN model with values of R2 = 0.90, root mean square error (RMSE) = 4.9218 and mean absolute error (MAE) = 3.4644 in the training phase and with values of R2 = 0.79, RMSE = 5.0324 and MAE = 3.2565 in the testing phase, exhibited the best function in predicting this phenomenon. It can be concluded that hybrid models have accurate results to predict PM2.5 concentration compared with linear model.

Advanced Propulsion Power Distribution System for Next Generation Electric/Hybrid Vehicle. Phase 1; Preliminary System Studies

NASA Technical Reports Server (NTRS)

Bose, Bimal K.; Kim, Min-Huei

1995-01-01

The report essentially summarizes the work performed in order to satisfy the above project objective. In the beginning, different energy storage devices, such as battery, flywheel and ultra capacitor are reviewed and compared, establishing the superiority of the battery. Then, the possible power sources, such as IC engine, diesel engine, gas turbine and fuel cell are reviewed and compared, and the superiority of IC engine has been established. Different types of machines for drive motor/engine generator, such as induction machine, PM synchronous machine and switched reluctance machine are compared, and the induction machine is established as the superior candidate. Similar discussion was made for power converters and devices. The Insulated Gate Bipolar Transistor (IGBT) appears to be the most superior device although Mercury Cadmium Telluride (MCT) shows future promise. Different types of candidate distribution systems with the possible combinations of power and energy sources have been discussed and the most viable system consisting of battery, IC engine and induction machine has been identified. Then, HFAC system has been compared with the DC system establishing the superiority of the former. The detailed component sizing calculations of HFAC and DC systems reinforce the superiority of the former. A preliminary control strategy has been developed for the candidate HFAC system. Finally, modeling and simulation study have been made to validate the system performance. The study in the report demonstrates the superiority of HFAC distribution system for next generation electric/hybrid vehicle.

Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland.

PubMed

de Hoogh, Kees; Héritier, Harris; Stafoggia, Massimo; Künzli, Nino; Kloog, Itai

2018-02-01

Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM 2.5 ) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM 2.5 monitoring data was supplemented by imputing PM 2.5 concentrations at PM 10 sites, using PM 2.5 /PM 10 ratios at co-located sites. Daily PM 2.5 concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM 2.5 in cells with AOD but without PM 2.5 measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM 2.5 predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM 2.5 concentrations. Copyright © 2017 Elsevier Ltd. All rights reserved.

PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors

NASA Astrophysics Data System (ADS)

Zhu, Suling; Lian, Xiuyuan; Wei, Lin; Che, Jinxing; Shen, Xiping; Yang, Ling; Qiu, Xuanlin; Liu, Xiaoning; Gao, Wenlong; Ren, Xiaowei; Li, Juansheng

2018-06-01

The PM2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for PM2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and

Direct control of air gap flux in permanent magnet machines

DOEpatents

Hsu, John S.

2000-01-01

A method and apparatus for field weakening in PM machines uses field weakening coils (35, 44, 45, 71, 72) to produce flux in one or more stators (34, 49, 63, 64), including a flux which counters flux normally produced in air gaps between the stator(s) (34, 49, 63, 64) and the rotor (20, 21, 41, 61) which carries the PM poles. Several modes of operation are introduced depending on the magnitude and polarity of current in the field weakening coils (35, 44, 45, 71, 72). The invention is particularly useful for, but not limited to, the electric vehicle drives and PM generators.

Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines

PubMed Central

Teodoro, George; Pan, Tony; Kurc, Tahsin; Kong, Jun; Cooper, Lee; Saltz, Joel

2013-01-01

We address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50× and 85× with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively. PMID:23908562

Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate.

PubMed

Zeynoddin, Mohammad; Bonakdari, Hossein; Azari, Arash; Ebtehaj, Isa; Gharabaghi, Bahram; Riahi Madavar, Hossein

2018-09-15

A novel hybrid approach is presented that can more accurately predict monthly rainfall in a tropical climate by integrating a linear stochastic model with a powerful non-linear extreme learning machine method. This new hybrid method was then evaluated by considering four general scenarios. In the first scenario, the modeling process is initiated without preprocessing input data as a base case. While in other three scenarios, the one-step and two-step procedures are utilized to make the model predictions more precise. The mentioned scenarios are based on a combination of stationarization techniques (i.e., differencing, seasonal and non-seasonal standardization and spectral analysis), and normality transforms (i.e., Box-Cox, John and Draper, Yeo and Johnson, Johnson, Box-Cox-Mod, log, log standard, and Manly). In scenario 2, which is a one-step scenario, the stationarization methods are employed as preprocessing approaches. In scenario 3 and 4, different combinations of normality transform, and stationarization methods are considered as preprocessing techniques. In total, 61 sub-scenarios are evaluated resulting 11013 models (10785 linear methods, 4 nonlinear models, and 224 hybrid models are evaluated). The uncertainty of the linear, nonlinear and hybrid models are examined by Monte Carlo technique. The best preprocessing technique is the utilization of Johnson normality transform and seasonal standardization (respectively) (R 2  = 0.99; RMSE = 0.6; MAE = 0.38; RMSRE = 0.1, MARE = 0.06, UI = 0.03 &UII = 0.05). The results of uncertainty analysis indicated the good performance of proposed technique (d-factor = 0.27; 95PPU = 83.57). Moreover, the results of the proposed methodology in this study were compared with an evolutionary hybrid of adaptive neuro fuzzy inference system (ANFIS) with firefly algorithm (ANFIS-FFA) demonstrating that the new hybrid methods outperformed ANFIS-FFA method. Copyright © 2018 Elsevier Ltd. All rights

Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients.

PubMed

Liu, Nehemiah T; Holcomb, John B; Wade, Charles E; Batchinsky, Andriy I; Cancio, Leopoldo C; Darrah, Mark I; Salinas, José

2014-02-01

Accurate and effective diagnosis of actual injury severity can be problematic in trauma patients. Inherent physiologic compensatory mechanisms may prevent accurate diagnosis and mask true severity in many circumstances. The objective of this project was the development and validation of a multiparameter machine learning algorithm and system capable of predicting the need for life-saving interventions (LSIs) in trauma patients. Statistics based on means, slopes, and maxima of various vital sign measurements corresponding to 79 trauma patient records generated over 110,000 feature sets, which were used to develop, train, and implement the system. Comparisons among several machine learning models proved that a multilayer perceptron would best implement the algorithm in a hybrid system consisting of a machine learning component and basic detection rules. Additionally, 295,994 feature sets from 82 h of trauma patient data showed that the system can obtain 89.8 % accuracy within 5 min of recorded LSIs. Use of machine learning technologies combined with basic detection rules provides a potential approach for accurately assessing the need for LSIs in trauma patients. The performance of this system demonstrates that machine learning technology can be implemented in a real-time fashion and potentially used in a critical care environment.

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

PubMed Central

Filho, Geraldo P. R.; Ueyama, Jó; Villas, Leandro A.; Pinto, Alex R.; Gonçalves, Vinícius P.; Pessin, Gustavo; Pazzi, Richard W.; Braun, Torsten

2014-01-01

In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out. PMID:24399157

Study on emission characteristics of hybrid bus under driving cycles in typical Chinese city

NASA Astrophysics Data System (ADS)

Xie, Yongdong; Xu, Guangju

2017-09-01

In this study, hybrid city bus was taken as the research object, through the vehicle drum test, the vehicle emissions of hybrid bus, the transient emissions of gas pollutants, as well as the particle size and number distribution were surveyed. The results of the studies are listed as follows: First, compared to traditional fuel bus, hybrid bus could reduce about 44% of the NOx emissions, 33% of the total hydrocarbon emissions, and 51% of the particles emissions. Furthermore, the distribution of particles number concentration of test vehicle became high in middle and low in both sides. More specifically, the particle number concentration was mainly concentrated in the range from 0.021 to 0.755μm, the maximum was 0.2μm, and particle size of particulate matter (PM) less than 1.2μm accounted for 95% of the total number concentration. Particulate mass concentration was increased with increment of particle size, and the maximum of particulate mass (PM) concentration was 6.2μm. On average, whether traditional fuel bus or hybrid bus, the particle size of particulate matter(PM) less than 2.5μm accounted for more than 98% in the particles emission. It is found that the particles are more likely to deposit to the lung, respiratory bronchioles and alveoli, causing respiratory and lung diseases. Therefore, how to control the PM emissions of hybrid bus is the key factor of the study.

A Hybrid Method for Opinion Finding Task (KUNLP at TREC 2008 Blog Track)

DTIC Science & Technology

2008-11-01

retrieve relevant documents. For the Opinion Retrieval subtask, we propose a hybrid model of lexicon-based approach and machine learning approach for...estimating and ranking the opinionated documents. For the Polarized Opinion Retrieval subtask, we employ machine learning for predicting the polarity...and linear combination technique for ranking polar documents. The hybrid model which utilize both lexicon-based approach and machine learning approach

A Double-Sided Linear Primary Permanent Magnet Vernier Machine

PubMed Central

2015-01-01

The purpose of this paper is to present a new double-sided linear primary permanent magnet (PM) vernier (DSLPPMV) machine, which can offer high thrust force, low detent force, and improved power factor. Both PMs and windings of the proposed machine are on the short translator, while the long stator is designed as a double-sided simple iron core with salient teeth so that it is very robust to transmit high thrust force. The key of this new machine is the introduction of double stator and the elimination of translator yoke, so that the inductance and the volume of the machine can be reduced. Hence, the proposed machine offers improved power factor and thrust force density. The electromagnetic performances of the proposed machine are analyzed including flux, no-load EMF, thrust force density, and inductance. Based on using the finite element analysis, the characteristics and performances of the proposed machine are assessed. PMID:25874250

A double-sided linear primary permanent magnet vernier machine.

PubMed

Du, Yi; Zou, Chunhua; Liu, Xianxing

2015-01-01

The purpose of this paper is to present a new double-sided linear primary permanent magnet (PM) vernier (DSLPPMV) machine, which can offer high thrust force, low detent force, and improved power factor. Both PMs and windings of the proposed machine are on the short translator, while the long stator is designed as a double-sided simple iron core with salient teeth so that it is very robust to transmit high thrust force. The key of this new machine is the introduction of double stator and the elimination of translator yoke, so that the inductance and the volume of the machine can be reduced. Hence, the proposed machine offers improved power factor and thrust force density. The electromagnetic performances of the proposed machine are analyzed including flux, no-load EMF, thrust force density, and inductance. Based on using the finite element analysis, the characteristics and performances of the proposed machine are assessed.

Simplified Hybrid-Secondary Uncluttered Machine And Method

DOEpatents

Hsu, John S [Oak Ridge, TN

2005-05-10

An electric machine (40, 40') has a stator (43) and a rotor (46) and a primary air gap (48) has secondary coils (47c, 47d) separated from the rotor (46) by a secondary air gap (49) so as to induce a slip current in the secondary coils (47c, 47d). The rotor (46, 76) has magnetic brushes (A, B, C, D) or wires (80) which couple flux in through the rotor (46) to the secondary coils (47c, 47d) without inducing a current in the rotor (46) and without coupling a stator rotational energy component to the secondary coils (47c, 47d). The machine can be operated as a motor or a generator in multi-phase or single-phase embodiments. A method of providing a slip energy controller is also disclosed.

Spatiotemporal modeling of PM2.5 concentrations at the national scale combining land use regression and Bayesian maximum entropy in China.

PubMed

Chen, Li; Gao, Shuang; Zhang, Hui; Sun, Yanling; Ma, Zhenxing; Vedal, Sverre; Mao, Jian; Bai, Zhipeng

2018-05-03

Concentrations of particulate matter with aerodynamic diameter <2.5 μm (PM 2.5 ) are relatively high in China. Estimation of PM 2.5 exposure is complex because PM 2.5 exhibits complex spatiotemporal patterns. To improve the validity of exposure predictions, several methods have been developed and applied worldwide. A hybrid approach combining a land use regression (LUR) model and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals were developed to estimate the PM 2.5 concentrations on a national scale in China. This hybrid model could potentially provide more valid predictions than a commonly-used LUR model. The LUR/BME model had good performance characteristics, with R 2  = 0.82 and root mean square error (RMSE) of 4.6 μg/m 3 . Prediction errors of the LUR/BME model were reduced by incorporating soft data accounting for data uncertainty, with the R 2 increasing by 6%. The performance of LUR/BME is better than OK/BME. The LUR/BME model is the most accurate fine spatial scale PM 2.5 model developed to date for China. Copyright © 2018. Published by Elsevier Ltd.

Spatial and temporal variations of the concentrations of PM10, PM2.5 and PM1 in China

NASA Astrophysics Data System (ADS)

Wang, Y. Q.; Zhang, X. Y.; Sun, J. Y.; Zhang, X. C.; Che, H. Z.; Li, Y.

2015-06-01

Concentrations of PM10, PM2.5 and PM1 were monitored at 24 stations of CAWNET (China Atmosphere Watch Network) from 2006 to 2014 using GRIMM 180 dust monitors. The highest particulate matter (PM) concentrations were observed at the stations of Xian, Zhengzhou and Gucheng, in Guanzhong and the Hua Bei Plain (HBP). The second highest PM concentrations were observed in northeast China, followed by southern China. According to the latest air quality standards of China, 14 stations reached the PM10 standard and only 7 stations, mainly rural and remote stations, reached the PM2.5 standard. The PM2.5 and PM10 ratios showed a clear increasing trend from northern to southern China, because of the substantial contribution of coarse mineral aerosol in northern China. The PM1 and PM2.5 ratios were higher than 80% at most stations. PM concentrations tended to be highest in winter and lowest in summer at most stations, and mineral dust impacts influenced the results in spring. A decreasing interannual trend was observed in the HBP and southern China from 2006 to 2014, but an increasing trend occurred at some stations in northeast China. Also diurnal variations of PM concentrations and meteorological factors effects were investigated.

Estimating hourly PM1 concentrations from Himawari-8 aerosol optical depth in China.

PubMed

Zang, Lin; Mao, Feiyue; Guo, Jianping; Gong, Wei; Wang, Wei; Pan, Zengxin

2018-06-11

Particulate matter with diameter less than 1 μm (PM 1 ) has been found to be closely associated with air quality, climate changes, and even adverse human health. However, a large gap in our knowledge concerning the large-scale distribution and variability of PM 1 remains, which is expected to be bridged with advanced remote-sensing techniques. In this study, a hybrid model called principal component analysis-general regression neural network (PCA-GRNN) is developed to estimate hourly PM 1 concentrations from Himawari-8 aerosol optical depth in combination with coincident ground-based PM 1 measurements in China. Results indicate that the hourly estimated PM 1 concentrations from satellite agree well with the measured values at national scale, with R 2 of 0.65, root-mean-square error (RMSE) of 22.0 μg/m 3 and mean absolute error (MAE) of 13.8 μg/m 3 . On daily and monthly time scales, R 2 increases to 0.70 and 0.81, respectively. Spatially, highly polluted regions of PM 1 are largely located in the North China Plain and Northeast China, in accordance with the distribution of industrialisation and urbanisation. In terms of diurnal variability, PM 1 concentration tends to peak in rush hours during the daytime. PM 1 exhibits distinct seasonality with winter having the largest concentration (31.5±3.5 μg/m 3 ), largely due to peak combustion emissions. We further attempt to estimate PM 2.5 and PM 10 with the proposed method and find that the accuracies of the proposed model for PM 1 and PM 2.5 estimation are significantly higher than that of PM 10 . Our findings suggest that geostationary data is one of the promising data to estimate fine particle concentration on large spatial scale. Copyright © 2018 Elsevier Ltd. All rights reserved.

Hybrid wavelet-support vector machine approach for modelling rainfall-runoff process.

PubMed

Komasi, Mehdi; Sharghi, Soroush

2016-01-01

Because of the importance of water resources management, the need for accurate modeling of the rainfall-runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall-runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall-runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process.

Random forest meteorological normalisation models for Swiss PM10 trend analysis

NASA Astrophysics Data System (ADS)

Grange, Stuart K.; Carslaw, David C.; Lewis, Alastair C.; Boleti, Eirini; Hueglin, Christoph

2018-05-01

Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM10 trends ranged between -0.09 and -1.16 µg m-3 yr-1 with urban traffic sites experiencing the greatest mean decrease in PM10 concentrations at -0.77 µg m-3 yr-1. Similar magnitudes have been reported for normalised PM10 trends for earlier time periods in Switzerland which indicates PM10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations.

Implementing the PM Programming Language using MPI and OpenMP - a New Tool for Programming Geophysical Models on Parallel Systems

NASA Astrophysics Data System (ADS)

Bellerby, Tim

2015-04-01

) or tasks are divided out among the available processors (number of tasks > number of processors). Nested parallel statements may further subdivide the processor set owned by a given task. Tasks or processors are distributed evenly by default, but uneven distributions are possible under programmer control. It is also possible to explicitly enable child tasks to migrate within the processor set owned by their parent task, reducing load unbalancing at the potential cost of increased inter-processor message traffic. PM incorporates some programming structures from the earlier MIST language presented at a previous EGU General Assembly, while adopting a significantly different underlying parallelisation model and type system. PM code is available at www.pm-lang.org under an unrestrictive MIT license. Reference Ruymán Reyes, Antonio J. Dorta, Francisco Almeida, Francisco de Sande, 2009. Automatic Hybrid MPI+OpenMP Code Generation with llc, Recent Advances in Parallel Virtual Machine and Message Passing Interface, Lecture Notes in Computer Science Volume 5759, 185-195

Design and analysis of linear fault-tolerant permanent-magnet vernier machines.

PubMed

Xu, Liang; Ji, Jinghua; Liu, Guohai; Du, Yi; Liu, Hu

2014-01-01

This paper proposes a new linear fault-tolerant permanent-magnet (PM) vernier (LFTPMV) machine, which can offer high thrust by using the magnetic gear effect. Both PMs and windings of the proposed machine are on short mover, while the long stator is only manufactured from iron. Hence, the proposed machine is very suitable for long stroke system applications. The key of this machine is that the magnetizer splits the two movers with modular and complementary structures. Hence, the proposed machine offers improved symmetrical and sinusoidal back electromotive force waveform and reduced detent force. Furthermore, owing to the complementary structure, the proposed machine possesses favorable fault-tolerant capability, namely, independent phases. In particular, differing from the existing fault-tolerant machines, the proposed machine offers fault tolerance without sacrificing thrust density. This is because neither fault-tolerant teeth nor the flux-barriers are adopted. The electromagnetic characteristics of the proposed machine are analyzed using the time-stepping finite-element method, which verifies the effectiveness of the theoretical analysis.

Hybrid Power Management for Office Equipment

NASA Astrophysics Data System (ADS)

Gingade, Ganesh P.

Office machines (such as printers, scanners, fax, and copiers) can consume significant amounts of power. Few studies have been devoted to power management of office equipment. Most office machines have sleep modes to save power. Power management of these machines are usually timeout-based: a machine sleeps after being idle long enough. Setting the timeout duration can be difficult: if it is too long, the machine wastes power during idleness. If it is too short, the machine sleeps too soon and too often--the wakeup delay can significantly degrade productivity. Thus, power management is a tradeoff between saving energy and keeping short response time. Many power management policies have been published and one policy may outperform another in some scenarios. There is no definite conclusion which policy is always better. This thesis describes two methods for office equipment power management. The first method adaptively reduces power based on a constraint of the wakeup delay. The second method is a hybrid with multiple candidate policies and it selects the most appropriate power management policy. Using six months of request traces from 18 different offices, we demonstrate that the hybrid policy outperforms individual policies. We also discover that power management based on business hours does not produce consistent energy savings.

Influences of wind and precipitation on different-sized particulate matter concentrations (PM2.5, PM10, PM2.5-10)

NASA Astrophysics Data System (ADS)

Zhang, Boen; Jiao, Limin; Xu, Gang; Zhao, Suli; Tang, Xin; Zhou, Yue; Gong, Chen

2018-06-01

Though it is recognized that meteorology has a great impact on the diffusion, accumulation and transport of air pollutants, few studies have investigated the impacts on different-sized particulate matter concentrations. We conducted a systematic comparative analysis and used the framework of generalized additive models (GAMs) to explore the influences of critical meteorological parameters, wind and precipitation, on PM2.5, PM10 and PM2.5-10 concentrations in Wuhan during 2013-2016. Overall, results showed that the impacts of wind and precipitation on different-sized PM concentrations are significantly different. The fine PM concentrations decreased gradually with the increase of wind speed, while coarse PM concentrations would increase due to dust resuspension under strong wind. Wind direction exerts limited influence on coarse PM concentrations. Wind speed was linearly correlated with log-transformed PM2.5 concentrations, but nonlinearly correlated with log-transformed PM10 and PM2.5-10 concentrations. We also found the PM2.5 and PM2.5-10 concentrations decreased by nearly 60 and 15% when the wind speed was up to 6 m/s, respectively, indicating a stronger negative impact of wind-speed on fine PM than coarse PM. The scavenging efficiency of precipitation on PM2.5-10 was over twice as high as on PM2.5. Our findings may help to understand the impacts of meteorology on different PM concentrations as well as discriminate and forecast variation in particulate matter concentrations.

Finite element analysis when orthogonal cutting of hybrid composite CFRP/Ti

NASA Astrophysics Data System (ADS)

Xu, Jinyang; El Mansori, Mohamed

2015-07-01

Hybrid composite, especially CFRP/Ti stack, is usually considered as an innovative structural configuration for manufacturing the key load-bearing components in modern aerospace industry. This paper originally proposed an FE model to simulate the total chip formation process dominated the hybrid cutting operation. The hybrid composite model was established based on three physical constituents, i.e., Ti constituent, interface and CFRP constituent. Different constitutive models and damage criteria were introduced to replicate the interrelated cutting behaviour of the stack material. The CFRP/Ti interface was modelled as a third phase through the concept of cohesive zone (CZ). Particular attention was made on the comparative studies of the influence of different cutting-sequence strategies on the machining responses induced in hybrid stack cutting. The numerical results emphasized the pivotal role of cutting-sequence strategy on the various machining induced responses including cutting-force generation, machined surface quality and induced interface damage.

PM RESEARCH

EPA Science Inventory

Activity Area (F03): PM Implementation NRMRL conducts research to improve the techniques used to quantify PM and PM precursor emissions from stationary, mobile, and fugitive sources and investigates the performance and cost of innovative control technology systems. The emission...

A Hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morton, April M; Nagle, Nicholas N; Piburn, Jesse O

As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for detailed information regarding residential energy consumption patterns has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy consumption, the majority of techniques are highly dependent on region-specific data sources and often require building- or dwelling-level details that are not publicly available for many regions in the United States. Furthermore, many existing methods do not account for errors in input data sources and may not accurately reflect inherent uncertainties in modelmore » outputs. We propose an alternative and more general hybrid approach to high-resolution residential electricity consumption modeling by merging a dasymetric model with a complementary machine learning algorithm. The method s flexible data requirement and statistical framework ensure that the model both is applicable to a wide range of regions and considers errors in input data sources.« less

Case study of PM pollution in playgrounds in Istanbul

NASA Astrophysics Data System (ADS)

Ozdemir, Huseyin; Mertoglu, Bulent; Demir, Goksel; Deniz, Ali; Toros, Hüseyin

2012-05-01

In a world where at least 50% of the population is living in urban environments, air pollution and specifically particulate matter (PM) have become one of the most critical issues for human health. Children are more susceptible than adults to air pollution and its adverse effects because they inhale and retain larger amounts of air pollutants per unit of body weight. In this study, PM pollution, particularly PM10 and PM2.5, at selected playgrounds were investigated in Istanbul city. Istanbul is a megacity of over 15 million inhabitants, and on-road traffic is increasing rapidly (over 3 million vehicles on the road). To estimate the effect of traffic emissions on children, the location of the playgrounds were selected according to traffic density. Measurements were carried out at five different playgrounds throughout the city in 2009. Field results show that the values of PM10 and PM2.5 have reached critical limits at the playgrounds close to the main roads, especially at P-1. Thus, we focused on this location and investigated a source other than traffic emissions. One of the episode days has been observed on 5-7 March 2009. Evaluations of meteorological events are very important to determine air pollution sources and their long-range transport. Therefore, the Weather Research and Forecasting model (WRF) was used to simulate and forecast meteorological parameters and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) applied to investigate long-range transport. According to the WRF model outputs, there was a low-pressure system over Geneva gulf on the 500-hPa level, and its core had been located over Britain on 5 March 2009 00UTC. The system had been sweeping dust from the Sahara Desert and carrying the air particles over Istanbul. Similarly, backward HYSPLIT analysis showed that air particles had moved through Istanbul from Northern Africa.

Emissions from Plug-in Hybrid Electric Vehicle (PHEV) During Real World Driving Under Various Weather Conditions

DOT National Transportation Integrated Search

2018-02-02

Exposure to particulate matter (PM) and pollutant gas (NOx) is associated with increased cardiopulmonary morbidity and mortality. Mobile source emissions contribute to PM and NOx emissions significantly in urban areas. Hybrid Electric Vehicles (HEVs)...

PREDICTING POPULATION EXPOSURES TO PM10 AND PM 2.5

EPA Science Inventory

An improved model for human exposure to particulate matter (PM), specifically PM10 and PM2.5 is under development by the U.S. EPA/NERL. This model will incorporate data from new PM exposure measurement and exposure factors research. It is intended to be used to predict exposure...

Interior Permanent Magnet Reluctance Machine with Brushless Field Excitation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wiles, R.H.

2005-10-07

In a conventional permanent magnet (PM) machine, the air-gap flux produced by the PM is fixed. It is difficult to enhance the air-gap flux density due to limitations of the PM in a series-magnetic circuit. However, the air-gap flux density can be weakened by using power electronic field weakening to the limit of demagnetization of the PMs. This paper presents the test results of controlling the PM air-gap flux density through the use of a stationary brushless excitation coil in a reluctance interior permanent magnet with brushless field excitation (RIPM-BFE) motor. Through the use of this technology the air-gap fluxmore » density can be either enhanced or weakened. There is no concern with demagnetizing the PMs during field weakening. The leakage flux of the excitation coil through the PMs is blocked. The prototype motor built on this principle confirms the concept of flux enhancement and weakening through the use of excitation coils.« less

Ex-vivo perfusion machines in kidney transplantation. The significance of the resistivity index.

PubMed

Elec, Florin Ioan; Lucan, Ciprian; Ghervan, Liviu; Munteanu, Valentin; Moga, Silviu; Suciu, Mihai; Enache, Dan; Elec, Alina; Munteanu, Adriana; Barbos, Adrian; Iacob, Gheorghita; Lucan, Mihai

2014-01-01

With a growing shortage of organs for transplantation, finding ways of increasing the donor organ pool remains of utmost importance. Perfusion machines (PM) have been proven to enhance the potential for kidney transplants to function sooner, last longer, giving patients the opportunity for a better life quality. The aim of this study is to evaluate the relation between the resistance index provided by the PM, the postoperative resistance index measured by Doppler ultrasound and the initial graft outcome. Between January 2012-December 2012, clinical data obtained from 82 consecutive renal transplants from brain death donors (BDD) which underwent PM maintenance were analyzed in a transversal study. Prior transplantation we recorded the solution temperature, filtration rate and the resistance index provided by PM. After the surgical intervention, each patient had standard follow-up. Doppler ultrasound resistivity index (RI) was recorded on the first postoperative day. Out of 115 renal transplants, 98 (85.21%) were performed with grafts from BDD. The PM was used for 82 renal grafts. The Doppler resistance index in relation to the resistance index shows a highly statistical correlation by linear regression (R=0.813, p<0.0001). Primary graft function was recorded in 74 patients (90.24%) and it was highly statistically significant correlated with the resistance index measured by PM. Out of 8 patients with primary non-function, 6 patients recovered with normal graft function at one year. The resistivity index recorded by the life-port machine is correlated with the vascular resistivity index measured by Doppler ultrasound and thus it may predicts the primary graft outcome.

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

NASA Astrophysics Data System (ADS)

Mozaffari, Ahmad; Vajedi, Mahyar; Chehresaz, Maryyeh; Azad, Nasser L.

2016-03-01

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.

Prediction of hourly PM2.5 using a space-time support vector regression model

NASA Astrophysics Data System (ADS)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Identification of PM10 air pollution origins at a rural background site

NASA Astrophysics Data System (ADS)

Reizer, Magdalena; Orza, José A. G.

2018-01-01

Trajectory cluster analysis and concentration weighted trajectory (CWT) approach have been applied to investigate the origins of PM10 air pollution recorded at a rural background site in North-eastern Poland (Diabla Góra). Air mass back-trajectories used in this study have been computed with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for a 10-year period of 2006-2015. A cluster analysis grouped back-trajectories into 7 clusters. Most of the trajectories correspond to fast and moderately moving westerly and northerly flows (45% and 25% of the cases, respectively). However, significantly higher PM10 concentrations were observed for slow moving easterly (11%) and southerly (20%) air masses. The CWT analysis shows that high PM10 levels are observed at Diabla Góra site when air masses are originated and passed over the heavily industrialized areas in Central-Eastern Europe located to the south and south-east of the site.

Simulation of hybrid solar power plants

NASA Astrophysics Data System (ADS)

Dieckmann, Simon; Dersch, Jürgen

2017-06-01

Hybrid solar power plants have the potential to combine advantages of two different technologies at the cost of increased complexity. The present paper shows the potential of the software greenius for the techno-economic evaluation of hybrid solar power plants and discusses two exemplary scenarios. Depreciated Concentrated Solar Power (CSP) plants based on trough technology can be retrofitted with solar towers in order to reach higher steam cycle temperatures and hence efficiencies. Compared to a newly built tower plant the hybridization of a depreciated trough plant causes about 30% lower LCOE reaching 104 /MWh. The second hybrid scenario combines cost-efficient photovoltaics with dispatchable CSP technology. This hybrid plant offers very high capacity factors up to 69% based on 100% load from 8am to 11pm. The LCOE of the hybrid plant are only slightly lower (174 vs. 186 /MWh) compared to the pure CSP plant because the capital expenditure for thermal storage and power block remains the same while the electricity output is much lower.

Scalable hybrid computation with spikes.

PubMed

Sarpeshkar, Rahul; O'Halloran, Micah

2002-09-01

We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity: First, like digital computation, which uses several one-bit precise logical units to collectively compute a precise answer to a computation, the hybrid scheme uses several moderate-precision analog units to collectively compute a precise answer to a computation. Second, frequent discrete signal restoration of the analog information prevents analog noise and offset from degrading the computation. And, third, a state machine enables complex computations to be created using a sequence of elementary computations. A natural choice for implementing this hybrid scheme is one based on spikes because spike-count codes are digital, while spike-time codes are analog. We illustrate how spikes afford easy ways to implement all three components of scalable hybrid computation. First, as an important example of distributed analog computation, we show how spikes can create a distributed modular representation of an analog number by implementing digital carry interactions between spiking analog neurons. Second, we show how signal restoration may be performed by recursive spike-count quantization of spike-time codes. And, third, we use spikes from an analog dynamical system to trigger state transitions in a digital dynamical system, which reconfigures the analog dynamical system using a binary control vector; such feedback interactions between analog and digital dynamical systems create a hybrid state machine (HSM). The HSM extends and expands the concept of a digital finite-state-machine to the hybrid domain. We present experimental data from a two-neuron HSM on a chip that implements error-correcting analog-to-digital conversion with the concurrent use of spike-time and spike-count codes. We also present experimental data from silicon circuits that implement HSM-based pattern recognition using spike-time synchrony. We outline how HSMs may be

Design and Analysis of Linear Fault-Tolerant Permanent-Magnet Vernier Machines

PubMed Central

Xu, Liang; Liu, Guohai; Du, Yi; Liu, Hu

2014-01-01

This paper proposes a new linear fault-tolerant permanent-magnet (PM) vernier (LFTPMV) machine, which can offer high thrust by using the magnetic gear effect. Both PMs and windings of the proposed machine are on short mover, while the long stator is only manufactured from iron. Hence, the proposed machine is very suitable for long stroke system applications. The key of this machine is that the magnetizer splits the two movers with modular and complementary structures. Hence, the proposed machine offers improved symmetrical and sinusoidal back electromotive force waveform and reduced detent force. Furthermore, owing to the complementary structure, the proposed machine possesses favorable fault-tolerant capability, namely, independent phases. In particular, differing from the existing fault-tolerant machines, the proposed machine offers fault tolerance without sacrificing thrust density. This is because neither fault-tolerant teeth nor the flux-barriers are adopted. The electromagnetic characteristics of the proposed machine are analyzed using the time-stepping finite-element method, which verifies the effectiveness of the theoretical analysis. PMID:24982959

[Research on Kalman interpolation prediction model based on micro-region PM2.5 concentration].

PubMed

Wang, Wei; Zheng, Bin; Chen, Binlin; An, Yaoming; Jiang, Xiaoming; Li, Zhangyong

2018-02-01

In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people's attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level α = 0.05. The mean absolute error (MEA) of Kalman prediction model was 1.8 μg/m 3 , the average relative error (MER) was 6%, and the correlation coefficient R was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation (BP) prediction and support vector machine (SVM) prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.

A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERPs: application to robot control.

PubMed

Ma, Jiaxin; Zhang, Yu; Cichocki, Andrzej; Matsuno, Fumitoshi

2015-03-01

This study presents a novel human-machine interface (HMI) based on both electrooculography (EOG) and electroencephalography (EEG). This hybrid interface works in two modes: an EOG mode recognizes eye movements such as blinks, and an EEG mode detects event related potentials (ERPs) like P300. While both eye movements and ERPs have been separately used for implementing assistive interfaces, which help patients with motor disabilities in performing daily tasks, the proposed hybrid interface integrates them together. In this way, both the eye movements and ERPs complement each other. Therefore, it can provide a better efficiency and a wider scope of application. In this study, we design a threshold algorithm that can recognize four kinds of eye movements including blink, wink, gaze, and frown. In addition, an oddball paradigm with stimuli of inverted faces is used to evoke multiple ERP components including P300, N170, and VPP. To verify the effectiveness of the proposed system, two different online experiments are carried out. One is to control a multifunctional humanoid robot, and the other is to control four mobile robots. In both experiments, the subjects can complete tasks effectively by using the proposed interface, whereas the best completion time is relatively short and very close to the one operated by hand.

Ex-vivo perfusion machines in kidney transplantation. The significance of the resistivity index

PubMed Central

ELEC, FLORIN IOAN; LUCAN, CIPRIAN; GHERVAN, LIVIU; MUNTEANU, VALENTIN; MOGA, SILVIU; SUCIU, MIHAI; ENACHE, DAN; ELEC, ALINA; MUNTEANU, ADRIANA; BARBOS, ADRIAN; IACOB, GHEORGHITA; LUCAN, MIHAI

2014-01-01

Introduction With a growing shortage of organs for transplantation, finding ways of increasing the donor organ pool remains of utmost importance. Perfusion machines (PM) have been proven to enhance the potential for kidney transplants to function sooner, last longer, giving patients the opportunity for a better life quality. Objective The aim of this study is to evaluate the relation between the resistance index provided by the PM, the postoperative resistance index measured by Doppler ultrasound and the initial graft outcome. Material and method Between January 2012-December 2012, clinical data obtained from 82 consecutive renal transplants from brain death donors (BDD) which underwent PM maintenance were analyzed in a transversal study. Prior transplantation we recorded the solution temperature, filtration rate and the resistance index provided by PM. After the surgical intervention, each patient had standard follow-up. Doppler ultrasound resistivity index (RI) was recorded on the first postoperative day. Results Out of 115 renal transplants, 98 (85.21%) were performed with grafts from BDD. The PM was used for 82 renal grafts. The Doppler resistance index in relation to the resistance index shows a highly statistical correlation by linear regression (R=0.813, p<0.0001). Primary graft function was recorded in 74 patients (90.24%) and it was highly statistically significant correlated with the resistance index measured by PM. Out of 8 patients with primary non-function, 6 patients recovered with normal graft function at one year. Conclusion The resistivity index recorded by the life-port machine is correlated with the vascular resistivity index measured by Doppler ultrasound and thus it may predicts the primary graft outcome. PMID:26527992

Hybrid Cloud Computing Environment for EarthCube and Geoscience Community

NASA Astrophysics Data System (ADS)

Yang, C. P.; Qin, H.

2016-12-01

The NSF EarthCube Integration and Test Environment (ECITE) has built a hybrid cloud computing environment to provides cloud resources from private cloud environments by using cloud system software - OpenStack and Eucalyptus, and also manages public cloud - Amazon Web Service that allow resource synchronizing and bursting between private and public cloud. On ECITE hybrid cloud platform, EarthCube and geoscience community can deploy and manage the applications by using base virtual machine images or customized virtual machines, analyze big datasets by using virtual clusters, and real-time monitor the virtual resource usage on the cloud. Currently, a number of EarthCube projects have deployed or started migrating their projects to this platform, such as CHORDS, BCube, CINERGI, OntoSoft, and some other EarthCube building blocks. To accomplish the deployment or migration, administrator of ECITE hybrid cloud platform prepares the specific needs (e.g. images, port numbers, usable cloud capacity, etc.) of each project in advance base on the communications between ECITE and participant projects, and then the scientists or IT technicians in those projects launch one or multiple virtual machines, access the virtual machine(s) to set up computing environment if need be, and migrate their codes, documents or data without caring about the heterogeneity in structure and operations among different cloud platforms.

Where is PM gone? Trends and variability of atmospheric PM10, PM2.5 and PM10-2.5 in the Po valley over the last decade (and more).

NASA Astrophysics Data System (ADS)

Bigi, Alessandro; Ghermandi, Grazia

2017-04-01

The Po Valley is one of the largest European regions with a remarkably high concentration level of atmospheric pollutants, both for particulate and gaseous compounds. In the last decade stringent regulations on air quality standards and on anthropogenic emissions have been set by the European Commission, leading to an overall improvement in air quality across Europe. In order to assess the decadal pattern and variability in PM across the Po valley we thoroughly investigated the time series of PM10, PM2.5 and PM10-2.5 from 41, 44 and 15 sites respectively (Bigi & Ghermandi 2014, 2016). PM2.5 and PM10-2.5 (PM10) series with a 7 (10) year or longer record have been analysed for long term trend in deseasonalized monthly means, annual quantiles and in monthly frequency distribution by robust statistical methods. A widespread and significant decreasing trend was observed at several sites for all size fractions, with the drop, up to a few percent per year, occurring mainly in winter for PM2.5 and throughout the year for PM10. All series were tested for a significant weekly periodicity (a proxy to estimate the impact of primary anthropogenic emissions) by 3 different statistical methods, yielding positive results for summer PM2.5 and PM10, and for both summer and winter PM10-2.5. Hierarchical cluster analysis showed larger variability for PM10 than for PM2.5. The former was split in five clusters: two encompassing the metropolitan areas of Turin and Milan and their respective nearby sites and the other three clusters gathering northeast, northwest and central Po Valley sites respectively. PM2.5 clusters divide the valley in western, eastern and southern/Apennines foothill sectors. The trend in atmospheric concentration was compared with the time series of local primary and precursor emissions, vehicular fleet details and fuel sales. A significant basin-wide drop in emissions occurred for gaseous pollutants, contrarily to primary emissions of PM10 and PM2.5, whose drop was

Experimental Characterization of Aluminum-Based Hybrid Composites Obtained Through Powder Metallurgy

NASA Astrophysics Data System (ADS)

Marcu, D. F.; Buzatu, M.; Ghica, V. G.; Petrescu, M. I.; Popescu, G.; Niculescu, F.; Iacob, G.

2018-06-01

The paper presents some experimental results concerning fabrication through powder metallurgy (P/M) of aluminum-based hybrid composites - Al/Al2O3/Gr. In order to understand the mechanisms that occur during the P/M processes of obtaining Al/Al2O3/Gr composite, we correlated the physical characteristics with their micro-structural characteristics. The characterization was performed using analysis techniques specific for P/M process, SEM-EDS and XRD analyses. Micro-structural characterization of the composites has revealed fairly uniform distribution this resulting in good properties of the final composite material.

Analysis of PM10, PM2.5, and PM2 5-10 concentrations in Santiago, Chile, from 1989 to 2001.

PubMed

Koutrakis, Petros; Sax, Sonja N; Sarnat, Jeremy A; Coull, Brent; Demokritou, Phil; Oyola, Pedro; Garcia, Javier; Gramsch, Ernesto

2005-03-01

Daily particle samples were collected in Santiago, Chile, at four urban locations from January 1, 1989, through December 31, 2001. Both fine PM with da < 2.5 microm (PM2.5) and coarse PM with 2.5 < da < 10 microm (PM2.5-10) were collected using dichotomous samplers. The inhalable particle fraction, PM10, was determined as the sum of fine and coarse concentrations. Wind speed, temperature and relative humidity (RH) were also measured continuously. Average concentrations of PM2.5 for the 1989-2001 period ranged from 38.5 microg/m3 to 53 microg/m3. For PM2.5-10 levels ranged from 35.8-48.2 microg/m3 and for PM10 results were 74.4-101.2 microg/m3 across the four sites. Both annual and daily PM2.5 and PM10 concentration levels exceeded the U.S. National Ambient Air Quality Standards and the European Union concentration limits. Mean PM2.5 levels during the cold season (April through September) were more than twice as high as those observed in the warm season (October through March); whereas coarse particle levels were similar in both seasons. PM concentration trends were investigated using regression models, controlling for site, weekday, month, wind speed, temperature, and RH. Results showed that PM2.5 concentrations decreased substantially, 52% over the 12-year period (1989-2000), whereas PM2.5-10 concentrations increased by approximately 50% in the first 5 years and then decreased by a similar percentage over the following 7 years. These decreases were evident even after controlling for significant climatic effects. These results suggest that the pollution reduction programs developed and implemented by the Comisión Nacional del Medio Ambiente (CONAMA) have been effective in reducing particle levels in the Santiago Metropolitan region. However, particle levels remain high and it is thus imperative that efforts to improve air quality continue.

Identifying PM2.5 and PM0.1 sources for epidemiological studies in California.

PubMed

Hu, Jianlin; Zhang, Hongliang; Chen, Shuhua; Ying, Qi; Wiedinmyer, Christine; Vandenberghe, Francois; Kleeman, Michael J

2014-05-06

The University of California-Davis_Primary (UCD_P) model was applied to simultaneously track ∼ 900 source contributions to primary particulate matter (PM) in California for seven continuous years (January 1st, 2000 to December 31st, 2006). Predicted source contributions to primary PM2.5 mass, PM1.8 elemental carbon (EC), PM1.8 organic carbon (OC), PM0.1 EC, and PM0.1 OC were in general agreement with the results from previous source apportionment studies using receptor-based techniques. All sources were further subjected to a constraint check based on model performance for PM trace elemental composition. A total of 151 PM2.5 sources and 71 PM0.1 sources contained PM elements that were predicted at concentrations in general agreement with measured values at nearby monitoring sites. Significant spatial heterogeneity was predicted among the 151 PM2.5 and 71 PM0.1 source concentrations, and significantly different seasonal profiles were predicted for PM2.5 and PM0.1 in central California vs southern California. Population-weighted concentrations of PM emitted from various sources calculated using the UCD_P model spatial information differed from the central monitor estimates by up to 77% for primary PM2.5 mass and 148% for PM2.5 EC because the central monitor concentration is not representative of exposure for nearby population. The results from the UCD_P model provide enhanced source apportionment information for epidemiological studies to examine the relationship between health effects and concentrations of primary PM from individual sources.

High accuracy demodulation for twin-grating based sensor network with hybrid TDM/FDM

NASA Astrophysics Data System (ADS)

Ai, Fan; Sun, Qizhen; Cheng, Jianwei; Luo, Yiyang; Yan, Zhijun; Liu, Deming

2017-04-01

We demonstrate a high accuracy demodulation platform with a tunable Fabry-Perot filter (TFF) for twin-grating based fiber optic sensing network with hybrid TDM/FDM. The hybrid TDM/FDM scheme can improve the spatial resolution to centimeter but increases the requirement of high spectrum resolution. To realize the demodulation of the complex twin-grating spectrum, we adopt the TFF demodulation method and compensate the environmental temperature change and nonlinear effect through calibration FBGs. The performance of the demodulation module is tested by a temperature experiment. Spectrum resolution of 1pm is realized with precision of 2.5pm while the environmental temperature of TFF changes 9.3°C.

Novel Transverse Flux Machine for Vehicle Traction Applications: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wan, Z.; Ahmed, A.; Husain, I.

2015-04-02

A novel transverse flux machine topology for electric vehicle traction applications using ferrite magnets is presented in this paper. The proposed transverse flux topology utilizes novel magnet arrangements in the rotor that are similar to the Halbach array to boost flux linkage; on the stator side, cores are alternately arranged around a pair of ring windings in each phase to make use of the entire rotor flux that eliminates end windings. Analytical design considerations and finite-element methods are used for an optimized design of a scooter in-wheel motor. Simulation results from finite element analysis (FEA) show that the motor achievedmore » comparable torque density to conventional rare-earth permanent magnet (PM) machines. This machine is a viable candidate for direct-drive applications with low cost and high torque density.« less

Effect of the Machining Processes on Low Cycle Fatigue Behavior of a Powder Metallurgy Disk

NASA Technical Reports Server (NTRS)

Telesman, J.; Kantzos, P.; Gabb, T. P.; Ghosn, L. J.

2010-01-01

A study has been performed to investigate the effect of various machining processes on fatigue life of configured low cycle fatigue specimens machined out of a NASA developed LSHR P/M nickel based disk alloy. Two types of configured specimen geometries were employed in the study. To evaluate a broach machining processes a double notch geometry was used with both notches machined using broach tooling. EDM machined notched specimens of the same configuration were tested for comparison purposes. Honing finishing process was evaluated by using a center hole specimen geometry. Comparison testing was again done using EDM machined specimens of the same geometry. The effect of these machining processes on the resulting surface roughness, residual stress distribution and microstructural damage were characterized and used in attempt to explain the low cycle fatigue results.

Design and analysis of a 3D-flux flux-switching permanent magnet machine with SMC cores and ferrite magnets

NASA Astrophysics Data System (ADS)

Liu, Chengcheng; Wang, Youhua; Lei, Gang; Guo, Youguang; Zhu, Jianguo

2017-05-01

Since permanent magnets (PM) are stacked between the adjacent stator teeth and there are no windings or PMs on the rotor, flux-switching permanent magnet machine (FSPMM) owns the merits of good flux concentrating and robust rotor structure. Compared with the traditional PM machines, FSPMM can provide higher torque density and better thermal dissipation ability. Combined with the soft magnetic composite (SMC) material and ferrite magnets, this paper proposes a new 3D-flux FSPMM (3DFFSPMM). The topology and operation principle are introduced. It can be found that the designed new 3DFFSPMM has many merits over than the traditional FSPMM for it can utilize the advantages of SMC material. Moreover, the PM flux of this new motor can be regulated by using the mechanical method. 3D finite element method (FEM) is used to calculate the magnetic field and parameters of the motor, such as flux density, inductance, PM flux linkage and efficiency map. The demagnetization analysis of the ferrite magnet is also addressed to ensure the safety operation of the proposed motor.

Assessment of annual air pollution levels with PM1, PM2.5, PM10 and associated heavy metals in Algiers, Algeria.

PubMed

Talbi, Abdelhamid; Kerchich, Yacine; Kerbachi, Rabah; Boughedaoui, Ménouèr

2018-01-01

Concentrations of particulate matter less than 1  μm, 2.5  μm, 10 μm and their contents of heavy metals were investigated in two different stations, urban and roadside at Algiers (Algeria). Sampling was conducted during two years by a high volume samplers (HVS) equipped with a cascade impactor at four levels stage, for one year sampling. The characterization of the heavy metals associated to the particulate matter (PM) was carried out by X-Ray Fluorescence analysis (XRF). The annual average concentration of PM 1 , PM 2.5 and PM 10 in both stations were 18.24, 32.23 and 60.01 μg m -3 respectively. The PM 1 , PM 2.5 and PM 10 concentrations in roadside varied from 13.46 to 25.59 μg m -3 , 20.82-49.85 μg m -3 and 45.90-77.23 μg m -3 respectively. However in the urban station, the PM 1 , PM 2.5 and PM 10 concentrations varied from 10.45 to 26.24 μg m -3 , 18.53-47.58 μg m -3 and 43.8-91.62 μg m -3 . The heavy metals associated to the PM were confirmed by Scanning Electron Microscopy-Energy Dispersive X-Ray analyses (SEM-EDX). The different spots of PM 2.5 analysis by SEM-EDX shows the presence of nineteen elements with anthropogenic and natural origins, within the heavy metal detected, the lead was found with maximum of 5% (weight percent). In order to determine the source contributions of PM levels at the two sampling sites sampling, principal compound analysis (PCA) was applied to the collected data. Statistical analysis confirmed anthropogenic source with traffic being a significant source and high contribution of natural emissions. At both sites, the PM 2.5 /PM 10 ratio is lower than that usually recorded in developed countries. The study of the back-trajectories of the air masses starting from Sahara shows that desert dust influences the concentration and the composition of the PM measured in Algiers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hybrid MPI+OpenMP Programming of an Overset CFD Solver and Performance Investigations

NASA Technical Reports Server (NTRS)

Djomehri, M. Jahed; Jin, Haoqiang H.; Biegel, Bryan (Technical Monitor)

2002-01-01

This report describes a two level parallelization of a Computational Fluid Dynamic (CFD) solver with multi-zone overset structured grids. The approach is based on a hybrid MPI+OpenMP programming model suitable for shared memory and clusters of shared memory machines. The performance investigations of the hybrid application on an SGI Origin2000 (O2K) machine is reported using medium and large scale test problems.

PM SUPERSITES PROGRAM

EPA Science Inventory

In 1997, the EPA administrator published National Ambient Air Quality Standards (NAAQS) for Particulate Matter (PM) that included new standards for PM2.5 (PM with diameters less than 2.5 um). These revised standards stimulated national concern over uncertainties regarding the ex...

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

PubMed Central

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site. PMID:29370230

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

PubMed

Illias, Hazlee Azil; Zhao Liang, Wee

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

A hybrid machine learning model to estimate nitrate contamination of production zone groundwater in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, K.; Nolan, B. T.; Faunt, C. C.; Bell, A.; Gronberg, J.; Traum, J.; Wheeler, D. C.; Rosecrans, C.; Belitz, K.; Eberts, S.; Harter, T.

2016-12-01

A hybrid, non-linear, machine learning statistical model was developed within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface in the Central Valley, California. A database of 213 predictor variables representing well characteristics, historical and current field and county scale nitrogen mass balance, historical and current landuse, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6,000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The machine learning method, gradient boosting machine (GBM) was used to screen predictor variables and rank them in order of importance in relation to the groundwater nitrate measurements. The top five most important predictor variables included oxidation/reduction characteristics, historical field scale nitrogen mass balance, climate, and depth to 60 year old water. Twenty-two variables were selected for the final model and final model errors for log-transformed hold-out data were R squared of 0.45 and root mean square error (RMSE) of 1.124. Modeled mean groundwater age was tested separately for error improvement in the model and when included decreased model RMSE by 0.5% compared to the same model without age and by 0.20% compared to the model with all 213 variables. 1D and 2D partial plots were examined to determine how variables behave individually and interact in the model. Some variables behaved as expected: log nitrate decreased with increasing probability of anoxic conditions and depth to 60 year old water, generally decreased with increasing natural landuse surrounding wells and increasing mean groundwater age, generally increased with increased minimum depth to high water table and with increased base flow index value. Other variables exhibited much more erratic or noisy behavior in

Hybrid soft-lithography/laser machined microchips for the parallel generation of droplets†

PubMed Central

Muluneh, M.

2015-01-01

Microfluidic chips have been developed to generate droplets and microparticles with control over size, shape, and composition not possible using conventional methods. However, it has remained a challenge to scale-up production for practical applications due to the inherently limited throughput of micro-scale devices. To address this problem, we have developed a self-contained microchip that integrates many (N = 512) micro-scale droplet makers. This 3 × 3 cm2 PDMS microchip consists of a two-dimensional array of 32 × 16 flow-focusing droplet makers, a network of flow channels that connect them, and only two inputs and one output. The key innovation of this technology is the hybrid use of both soft-lithography and direct laser-micromachining. The microscale resolution of soft lithography is used to fabricate flow-focusing droplet makers that can produce small and precisely defined droplets. Deeply engraved (h ≈ 500 μm) laser-machined channels are utilized to supply each of the droplet makers with its oil phase, aqueous phase, and access to an output channel. The engraved channels' low hydrodynamic resistance ensures that each droplet maker is driven with the same flow rates for highly uniform droplet formation.To demonstrate the utility of this approach, water droplets (d ≈ 80 μm) were generated in hexadecane on both 8 × 1 and 32 × 16 geometries. PMID:24166156

Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Huang, Bo; Lin, Chuan-Yao

2015-02-01

This paper explores the spatio-temporal patterns of particulate matter (PM) in Taiwan based on a series of methods. Using fuzzy c-means clustering first, the spatial heterogeneity (six clusters) in the PM data collected between 2005 and 2009 in Taiwan are identified and the industrial and urban areas of Taiwan (southwestern, west central, northwestern, and northern Taiwan) are found to have high PM concentrations. The PM10-PM2.5 relationship is then modeled with global ordinary least squares regression, geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR). The GTWR and GWR produce consistent results; however, GTWR provides more detailed information of spatio-temporal variations of the PM10-PM2.5 relationship. The results also show that GTWR provides a relatively high goodness of fit and sufficient space-time explanatory power. In particular, the PM2.5 or PM10 varies with time and space, depending on weather conditions and the spatial distribution of land use and emission patterns in local areas. Such information can be used to determine patterns of spatio-temporal heterogeneity in PM that will allow the control of pollutants and the reduction of public exposure.

Diagnosis of Dust- and Pollution- Impacted PM10, PM2.5, and PM1 Aerosols Observed at Gosan Climate Observatory

NASA Astrophysics Data System (ADS)

Shang, X.; Lee, M.; LIM, S.; Gustafsson, O.; Lee, G.; Chang, L.

2017-12-01

In East Asia, dust is prevalent and used to be mixed with various pollutants during transportation, causing a large uncertainty in estimating the climate forcing of aerosol and difficulty in making environmental policy. In order to diagnose the influence of dust particles on aerosol, we conducted a long-term measurement of PM10, PM2.5 and PM1 for mass, water-soluble ions, and carbonaceous compounds at Gosan Climate Observatory, South Korea from August 2007 to February 2012. The result of principle component analysis reveals that anthropogenic, typical soil dust, and saline dust impact explain 46 %, 16 %, and 9 % of the total variance for all samples, respectively. The mode analysis of mass distributions provides the criteria to distinguish these principle factors. The anthropogenic impact was most pronounced in PM1 and diagnosed by the PM1 mass higher than mean+σ. If PM10 mass was greater than mean+σ, it was highly likely to be affected by typical soil dust. This criterion is also applicable for PM2.5 mass, which was enhanced by both haze and dust particles, though. In the present study, saline dust was recognized by relatively high concentrations of Na and Cl ions in PM1.0. However, their existence was not manifested by increased mass in any of three PM types.

Spatiotemporal patterns of particulate matter (PM) and associations between PM and mortality in Shenzhen, China.

PubMed

Zhang, Fengying; Liu, Xiaojian; Zhou, Lei; Yu, Yong; Wang, Li; Lu, Jinmei; Wang, Wuyi; Krafft, Thomas

2016-03-02

Most studies on air pollution exposure and its associations with human health in China have focused on the heavily polluted industrial areas and/or mega-cities, and studies on cities with comparatively low air pollutant concentrations are still rare. Only a few studies have attempted to analyse particulate matter (PM) for the vibrant economic centre Shenzhen in the Pearl River Delta. So far no systematic investigation of PM spatiotemporal patterns in Shenzhen has been undertaken and the understanding of pollution exposure in urban agglomerations with comparatively low pollution is still limited. We analyze daily and hourly particulate matter concentrations and all-cause mortality during 2013 in Shenzhen, China. Temporal patterns of PM (PM2.5 and PM10) with aerodynamic diameters of 2.5 (10) μm or less (or less (including particles with a diameter that equals to 2.5 (10) μm) are studied, along with the ratio of PM2.5 to PM10. Spatial distributions of PM10 and PM2.5 are addressed and associations of PM10 or PM2.5 and all-cause mortality are analyzed. Annual average PM10 and PM2.5 concentrations were 61.3 and 39.6 μg/m(3) in 2013. PM2.5 failed to meet the Class 2 annual limit of the National Ambient Air Quality Standard. PM2.5 was the primary air pollutant, with 8.8 % of days having heavy PM2.5 pollution. The daily PM2.5/PM10 ratios were high. Hourly PM2.5 concentrations in the tourist area were lower than downtown throughout the day. PM10 and PM2.5 concentrations were higher in western parts of Shenzhen than in eastern parts. Excess risks in the number of all-cause mortality with a 10 μg/m(3) increase of PM were 0.61 % (95 % confidence interval [CI]: 0.50-0.72) for PM10, and 0.69 % (95 % CI: 0.55-0.83) for PM2.5, respectively. The greatest ERs of PM10 and PM2.5 were in 2-day cumulative measures for the all-cause mortality, 2-day lag for females and the young (0-65 years), and L02 for males and the elder (>65 years). PM2.5 had higher risks on all

Partitioning of magnetic particles in PM10, PM2.5 and PM1 aerosols in the urban atmosphere of Barcelona (Spain).

PubMed

Revuelta, María Aránzazu; McIntosh, Gregg; Pey, Jorge; Pérez, Noemi; Querol, Xavier; Alastuey, Andrés

2014-05-01

A combined magnetic-chemical study of 15 daily, simultaneous PM10-PM2.5-PM1 urban background aerosol samples has been carried out. The magnetic properties are dominated by non-stoichiometric magnetite, with highest concentrations seen in PM10. Low temperature magnetic analyses showed that the superparamagnetic fraction is more abundant when coarse, multidomain particles are present, confirming that they may occur as an oxidized outer shell around coarser grains. A strong association of the magnetic parameters with a vehicular PM10 source has been identified. Strong correlations found with Cu and Sb suggests that this association is related to brake abrasion emissions rather than exhaust emissions. For PM1 the magnetic remanence parameters are more strongly associated with crustal sources. Two crustal sources are identified in PM1, one of which is of North African origin. The magnetic particles are related to this source and so may be used to distinguish North African dust from other sources in PM1. Copyright © 2014 Elsevier Ltd. All rights reserved.

COMPARISON OF PM 2.5 AND PM 10 MONITORS

EPA Science Inventory

An extensive PM monitoring study was conducted during the 1998 Baltimore PM Epidemiology-Exposure Study of the Elderly. One goal was to investigate the mass concentration comparability between various monitoring instrumentation located across residential indoor, residential out...

Improved equivalent magnetic network modeling for analyzing working points of PMs in interior permanent magnet machine

NASA Astrophysics Data System (ADS)

Guo, Liyan; Xia, Changliang; Wang, Huimin; Wang, Zhiqiang; Shi, Tingna

2018-05-01

As is well known, the armature current will be ahead of the back electromotive force (back-EMF) under load condition of the interior permanent magnet (PM) machine. This kind of advanced armature current will produce a demagnetizing field, which may make irreversible demagnetization appeared in PMs easily. To estimate the working points of PMs more accurately and take demagnetization under consideration in the early design stage of a machine, an improved equivalent magnetic network model is established in this paper. Each PM under each magnetic pole is segmented, and the networks in the rotor pole shoe are refined, which makes a more precise model of the flux path in the rotor pole shoe possible. The working point of each PM under each magnetic pole can be calculated accurately by the established improved equivalent magnetic network model. Meanwhile, the calculated results are compared with those calculated by FEM. And the effects of d-axis component and q-axis component of armature current, air-gap length and flux barrier size on working points of PMs are analyzed by the improved equivalent magnetic network model.

Characterizing metal(loid) solubility in airborne PM10, PM2.5 and PM1 in Frankfurt, Germany using simulated lung fluids

NASA Astrophysics Data System (ADS)

Wiseman, Clare L. S.; Zereini, Fathi

2014-06-01

The purpose of this study is to assess the solubility of traffic-related metal(loid)s associated with airborne PM of human health concern, employing a physiologically-based extraction test with simulated lung fluids (artificial lysosomal fluid (ALF) and Gamble's solution). Airborne PM (PM10, PM2.5 and PM1) samples were collected in Frankfurt am Main, Germany, using a high volume sampler. Following extraction of the soluble metal(loid) fractions, sample filters were digested with a high pressure asher. Metal(loid) concentrations (As, Ce, Co, Cr, Cu, Mn, Ni, Pb, Sb, Ti and V) were determined in extracts and digests per ICP-Q-MS. All metal(loid)s occurred at detectable concentrations in the three airborne PM fractions. Copper was the most abundant element in mass terms, with mean concentrations of 105 and 53 ng/m3 in PM10 and PM2.5, respectively. Many of the metal(loid)s were observed to be soluble in simulated lung fluids, with Cu, As, V and Sb demonstrating the highest overall mobility in airborne PM. For instance, all four elements associated with PM10 had a solubility of >80% in ALF (24 h). Clearly, solubility is strongly pH dependent, as reflected by the higher relative mobility of samples extracted with the acidic ALF. Given their demonstrated solubility, this study provides indirect evidence that a number of toxic metal(loid)s are likely to possess an enhanced pulmonary toxic potential upon their inhalation. The co-presence of many toxic elements of concern in airborne PM suggests an assessment of health risk must consider the possible interactive impacts of multi-element exposures.

Chromosome segregation in Archaea mediated by a hybrid DNA partition machine

PubMed Central

Kalliomaa-Sanford, Anne K.; Rodriguez-Castañeda, Fernando A.; McLeod, Brett N.; Latorre-Roselló, Victor; Smith, Jasmine H.; Reimann, Julia; Albers, Sonja V.; Barillà, Daniela

2012-01-01

Eukarya and, more recently, some bacteria have been shown to rely on a cytoskeleton-based apparatus to drive chromosome segregation. In contrast, the factors and mechanisms underpinning this fundamental process are underexplored in archaea, the third domain of life. Here we establish that the archaeon Sulfolobus solfataricus harbors a hybrid segrosome consisting of two interacting proteins, SegA and SegB, that play a key role in genome segregation in this organism. SegA is an ortholog of bacterial, Walker-type ParA proteins, whereas SegB is an archaea-specific factor lacking sequence identity to either eukaryotic or bacterial proteins, but sharing homology with a cluster of uncharacterized factors conserved in both crenarchaea and euryarchaea, the two major archaeal sub-phyla. We show that SegA is an ATPase that polymerizes in vitro and that SegB is a site-specific DNA-binding protein contacting palindromic sequences located upstream of the segAB cassette. SegB interacts with SegA in the presence of nucleotides and dramatically affects its polymerization dynamics. Our data demonstrate that SegB strongly stimulates SegA polymerization, possibly by promoting SegA nucleation and accelerating polymer growth. Increased expression levels of segAB resulted in severe growth and chromosome segregation defects, including formation of anucleate cells, compact nucleoids confined to one half of the cell compartment and fragmented nucleoids. The overall picture emerging from our findings indicates that the SegAB complex fulfills a crucial function in chromosome segregation and is the prototype of a DNA partition machine widespread across archaea. PMID:22355141

Chromosome segregation in Archaea mediated by a hybrid DNA partition machine.

PubMed

Kalliomaa-Sanford, Anne K; Rodriguez-Castañeda, Fernando A; McLeod, Brett N; Latorre-Roselló, Victor; Smith, Jasmine H; Reimann, Julia; Albers, Sonja V; Barillà, Daniela

2012-03-06

Eukarya and, more recently, some bacteria have been shown to rely on a cytoskeleton-based apparatus to drive chromosome segregation. In contrast, the factors and mechanisms underpinning this fundamental process are underexplored in archaea, the third domain of life. Here we establish that the archaeon Sulfolobus solfataricus harbors a hybrid segrosome consisting of two interacting proteins, SegA and SegB, that play a key role in genome segregation in this organism. SegA is an ortholog of bacterial, Walker-type ParA proteins, whereas SegB is an archaea-specific factor lacking sequence identity to either eukaryotic or bacterial proteins, but sharing homology with a cluster of uncharacterized factors conserved in both crenarchaea and euryarchaea, the two major archaeal sub-phyla. We show that SegA is an ATPase that polymerizes in vitro and that SegB is a site-specific DNA-binding protein contacting palindromic sequences located upstream of the segAB cassette. SegB interacts with SegA in the presence of nucleotides and dramatically affects its polymerization dynamics. Our data demonstrate that SegB strongly stimulates SegA polymerization, possibly by promoting SegA nucleation and accelerating polymer growth. Increased expression levels of segAB resulted in severe growth and chromosome segregation defects, including formation of anucleate cells, compact nucleoids confined to one half of the cell compartment and fragmented nucleoids. The overall picture emerging from our findings indicates that the SegAB complex fulfills a crucial function in chromosome segregation and is the prototype of a DNA partition machine widespread across archaea.

Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.

PubMed

McMullen, David P; Hotson, Guy; Katyal, Kapil D; Wester, Brock A; Fifer, Matthew S; McGee, Timothy G; Harris, Andrew; Johannes, Matthew S; Vogelstein, R Jacob; Ravitz, Alan D; Anderson, William S; Thakor, Nitish V; Crone, Nathan E

2014-07-01

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.

A MOOC on Approaches to Machine Translation

ERIC Educational Resources Information Center

Costa-jussà, Mart R.; Formiga, Lluís; Torrillas, Oriol; Petit, Jordi; Fonollosa, José A. R.

2015-01-01

This paper describes the design, development, and analysis of a MOOC entitled "Approaches to Machine Translation: Rule-based, statistical and hybrid", and provides lessons learned and conclusions to be taken into account in the future. The course was developed within the Canvas platform, used by recognized European universities. It…

Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback

PubMed Central

Zeng, Hong; Wang, Yanxin; Wu, Changcheng; Song, Aiguo; Liu, Jia; Ji, Peng; Xu, Baoguo; Zhu, Lifeng; Li, Huijun; Wen, Pengcheng

2017-01-01

Brain-machine interface (BMI) can be used to control the robotic arm to assist paralysis people for performing activities of daily living. However, it is still a complex task for the BMI users to control the process of objects grasping and lifting with the robotic arm. It is hard to achieve high efficiency and accuracy even after extensive trainings. One important reason is lacking of sufficient feedback information for the user to perform the closed-loop control. In this study, we proposed a method of augmented reality (AR) guiding assistance to provide the enhanced visual feedback to the user for a closed-loop control with a hybrid Gaze-BMI, which combines the electroencephalography (EEG) signals based BMI and the eye tracking for an intuitive and effective control of the robotic arm. Experiments for the objects manipulation tasks while avoiding the obstacle in the workspace are designed to evaluate the performance of our method for controlling the robotic arm. According to the experimental results obtained from eight subjects, the advantages of the proposed closed-loop system (with AR feedback) over the open-loop system (with visual inspection only) have been verified. The number of trigger commands used for controlling the robotic arm to grasp and lift the objects with AR feedback has reduced significantly and the height gaps of the gripper in the lifting process have decreased more than 50% compared to those trials with normal visual inspection only. The results reveal that the hybrid Gaze-BMI user can benefit from the information provided by the AR interface, improving the efficiency and reducing the cognitive load during the grasping and lifting processes. PMID:29163123

Variability of aerosols and chemical composition of PM10, PM2.5 and PM1 on a platform of the Prague underground metro

NASA Astrophysics Data System (ADS)

Cusack, M.; Talbot, N.; Ondráček, J.; Minguillón, M. C.; Martins, V.; Klouda, K.; Schwarz, J.; Ždímal, V.

2015-10-01

Measurements of PM10, PM2.5 and PM1 and particle number concentration and size distribution were measured for 24 h on a platform of the Prague underground metro in October 2013. The three PM fractions were analysed for major and minor elements, secondary inorganic aerosols (SIA) and total carbon (TC). Measurements were performed both when the metro was inoperative and closed to the public (referred to as background), and when the metro was in operation and open to passengers. PM concentrations were elevated during both periods, but were substantially increased in the coarse fraction during hours when the metro was in operation. Average PM concentrations were 214.8, 93.9 and 44.8 μg m-3 for PM10, PM2.5 and PM1, respectively (determined gravimetrically). Average particle number concentrations were 8.5 × 103 cm-3 for background hours and 11.5 × 103 cm-3 during operational hours. Particle number concentrations were found to not vary as significantly as PM concentrations throughout the day. Variations in PM were strongly governed by passing trains, with highest concentrations recorded during rush hour. When trains were less frequent, PM concentrations were shown to fluctuate in unison with the entrance and exit of trains (as shown by wind velocity measured on the platform). PM was found to be highly enriched with iron, especially in the coarse fraction, comprising 46% of PM10 (98.9 μg m-3). This reduces to 6.7 μg m-3 during background hours, proving that the trains themselves were the main source of iron, most probably from wheel-rail mechanical abrasion. Other enriched elements relative to background hours included Ba, Cu, Mn, Cr, Mo, Ni and Co, among others. Many of these elements exhibited a similar size distribution, further indicating their sources were common and were attributed to train operations.

Characterization of PAHs and metals in indoor/outdoor PM10/PM2.5/PM1 in a retirement home and a school dormitory.

PubMed

Hassanvand, Mohammad Sadegh; Naddafi, Kazem; Faridi, Sasan; Nabizadeh, Ramin; Sowlat, Mohammad Hossein; Momeniha, Fatemeh; Gholampour, Akbar; Arhami, Mohammad; Kashani, Homa; Zare, Ahad; Niazi, Sadegh; Rastkari, Noushin; Nazmara, Shahrokh; Ghani, Maryam; Yunesian, Masud

2015-09-15

In the present work, we investigated the characteristics of polycyclic aromatic hydrocarbons (PAHs) and metal(loid)s in indoor/outdoor PM10, PM2.5, and PM1 in a retirement home and a school dormitory in Tehran from May 2012 to May 2013. The results indicated that the annual levels of indoor and outdoor PM10 and PM2.5 were much higher than the guidelines issued by the World Health Organization (WHO). The most abundant detected metal(loid)s in PM were Si, Fe, Zn, Al, and Pb. We found higher percentages of metal(loid)s in smaller size fractions of PM. Additionally, the results showed that the total PAHs (ƩPAHs) bound to PM were predominantly (83-88%) found in PM2.5, which can penetrate deep into the alveolar regions of the lungs. In general, carcinogenic PAHs accounted for 40-47% of the total PAHs concentrations; furthermore, the smaller the particle size, the higher the percentage of carcinogenic PAHs. The percentages of trace metal(loid)s and carcinogenic PAHs in PM2.5 mass were almost twice as high as those in PM10. This can most likely be responsible for the fact that PM2.5 can cause more adverse health effects than PM10 can. The average BaP-equivalent carcinogenic (BaP-TEQ) levels both indoors and outdoors considerably exceeded the maximum permissible risk level of 1 ng/m(3) of BaP. The enrichment factors and diagnostic ratios indicated that combustion-related anthropogenic sources, such as gasoline- and diesel-fueled vehicles as well as natural gas combustion, were the major sources of PAHs and trace metal(loid)s bound to PM. Copyright © 2015 Elsevier B.V. All rights reserved.

Energy-efficient container handling using hybrid model predictive control

NASA Astrophysics Data System (ADS)

Xin, Jianbin; Negenborn, Rudy R.; Lodewijks, Gabriel

2015-11-01

The performance of container terminals needs to be improved to adapt the growth of containers while maintaining sustainability. This paper provides a methodology for determining the trajectory of three key interacting machines for carrying out the so-called bay handling task, involving transporting containers between a vessel and the stacking area in an automated container terminal. The behaviours of the interacting machines are modelled as a collection of interconnected hybrid systems. Hybrid model predictive control (MPC) is proposed to achieve optimal performance, balancing the handling capacity and energy consumption. The underlying control problem is hereby formulated as a mixed-integer linear programming problem. Simulation studies illustrate that a higher penalty on energy consumption indeed leads to improved sustainability using less energy. Moreover, simulations illustrate how the proposed energy-efficient hybrid MPC controller performs under different types of uncertainties.

Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees

NASA Astrophysics Data System (ADS)

Pham, Binh Thai; Prakash, Indra; Tien Bui, Dieu

2018-02-01

A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction.

The impact analysis of the connecting pipe length and diameter on the operation of a piston hybrid power machine of positive displacement with gas suction capacity

NASA Astrophysics Data System (ADS)

Shcherba, V. E.; Grigoriev, A. V.; Averyanov, G. S.; Surikov, V. I.; Vedruchenko, V. P.; Galdin, N. S.; Trukhanova, D. A.

2017-08-01

The article analyzes the impact of the connecting liquid pipe length and diameter on consumables and power characteristics of the piston hybrid power machine with gas suction capacity. The following operating characteristics of the machine were constructed and analyzed: the average height of the liquid column in the jacket space; instantaneous velocity and height of the liquid column in the jacket space; the relative height of the liquid column in the jacket space; volumetric efficiency; indicator isothermal efficiency; flowrate in the pump section; relative pressure losses during suction; relative flowrate. The dependence of the instantaneous pressure in the work space and the suction space of the compressor section on the rotation angle of the crankshaft is determined for different values of the length and diameter of the connecting pipeline.

Development of a continuous monitoring system for PM10 and components of PM2.5.

PubMed

Lippmann, M; Xiong, J Q; Li, W

2000-01-01

While particulate matter with aerodynamic diameters below 10 and 2.5 microns (PM10 and PM2.5) correlate with excess mortality and morbidity, there is evidence for still closer epidemiological associations with sulfate ion, and experimental exposure-response studies suggest that the hydrogen ion and ultrafine (PM0.15) concentrations may be important risk factors. Also, there are measurement artifacts in current methods used to measure ambient PM10 and PM2.5, including negative artifacts because of losses of sampled semivolatile components (ammonium nitrate and some organics) and positive artifacts due to particle-bound water. To study such issues, we are developing a semi-continuous monitoring system for PM10, PM2.5, semivolatiles (organic compounds and NH4NO3), particle-bound water, and other PM2.5 constituents that may be causal factors. PM10 is aerodynamically sorted into three size-fractions: (1) coarse (PM10-PM2.5); (2) accumulation mode (PM2.5-PM0.15); and (3) ultrafine (PM0.15). The mass concentration of each fraction is measured in terms of the linear relation between accumulated mass and pressure drop on polycarbonate pore filters. The PM0.15 mass, being highly correlated with the ultrafine number concentration, provides a good index of the total number concentration in ambient air. For the accumulation mode (PM2.5-PM0.15), which contains nearly all of the semivolatiles and particle-bound water by mass, aliquots of the aerosol stream flow into system components that continuously monitor sulfur (by flame photometry), ammonium and nitrate (by chemiluminescence following catalytic transformations to NO), organics (by thermal-optical analysis) and particle-bound water (by electrolytic hygrometer after vacuum evaporation of sampled particles). The concentration of H+ can be calculated (by ion balance using the monitoring data on NO3-, NH4+, and SO4=).

Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings

NASA Astrophysics Data System (ADS)

Elbayoumi, Maher; Ramli, Nor Azam; Md Yusof, Noor Faizah Fitri; Yahaya, Ahmad Shukri Bin; Al Madhoun, Wesam; Ul-Saufie, Ahmed Zia

2014-09-01

In this study the concentrations of PM10, PM2.5, CO and CO2 concentrations and meteorological variables (wind speed, air temperature, and relative humidity) were employed to predict the annual and seasonal indoor concentration of PM10 and PM2.5 using multivariate statistical methods. The data have been collected in twelve naturally ventilated schools in Gaza Strip (Palestine) from October 2011 to May 2012 (academic year). The bivariate correlation analysis showed that the indoor PM10 and PM2.5 were highly positive correlated with outdoor concentration of PM10 and PM2.5. Further, Multiple linear regression (MLR) was used for modelling and R2 values for indoor PM10 were determined as 0.62 and 0.84 for PM10 and PM2.5 respectively. The Performance indicators of MLR models indicated that the prediction for PM10 and PM2.5 annual models were better than seasonal models. In order to reduce the number of input variables, principal component analysis (PCA) and principal component regression (PCR) were applied by using annual data. The predicted R2 were 0.40 and 0.73 for PM10 and PM2.5, respectively. PM10 models (MLR and PCR) show the tendency to underestimate indoor PM10 concentrations as it does not take into account the occupant's activities which highly affect the indoor concentrations during the class hours.

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

PubMed Central

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

A PROBABILISTIC POPULATION EXPOSURE MODEL FOR PM10 AND PM 2.5

EPA Science Inventory

A first generation probabilistic population exposure model for Particulate Matter (PM), specifically for predicting PM10, and PM2.5, exposures of an urban, population has been developed. This model is intended to be used to predict exposure (magnitude, frequency, and duration) ...

Variations of PM2.5, PM10 mass concentration and health assessment in Islamabad, Pakistan

NASA Astrophysics Data System (ADS)

Memhood, Tariq; Tianle, Z.; Ahmad, I.; Li, X.; Shen, F.; Akram, W.; Dong, L.

2018-04-01

Sparse information appears in lack of awareness among the people regarding the linkage between particulate matter (PM) and mortality in Pakistan. The current study is aimed to investigate the seasonal mass concentration level of PM2.5 and PM10 in ambient air of Islamabad to assess the health risk of PM pollution. The sampling was carried out with two parallel medium volume air samplers on Whatman 47 mm quartz filter at a flow rate of 100L/min. Mass concentration was obtained by gravimetric analysis. A noticeable seasonal change in PM10 and PM2.5 mass concentration was observed. In case of PM2.5, the winter was a most polluted and spring was the cleanest season of 2017 in Islamabad with 69.97 and 40.44 μgm‑3 mean concentration. Contrary, highest (152.42 μgm‑3) and lowest (74.90 μgm‑3) PM10 mass concentration was observed in autumn and summer respectively. Air Quality index level for PM2.5 and PM10 was remained moderated to unhealthy and good to sensitive respectively. Regarding health risk assessment, using national data for mortality rates, the excess mortality due to PM2.5 and PM10 exposure has been calculated and amounts to over 198 and 98 deaths annually for Islamabad. Comparatively estimated lifetime risk for PM2.5 (1.16×10-6) was observed higher than PM10 (7.32×10-8).

Indoor/outdoor relationships of PM10, PM2.5, and PM1 mass concentrations and their water-soluble ions in a retirement home and a school dormitory

NASA Astrophysics Data System (ADS)

Hassanvand, Mohammad Sadegh; Naddafi, Kazem; Faridi, Sasan; Arhami, Mohammad; Nabizadeh, Ramin; Sowlat, Mohammad Hossein; Pourpak, Zahra; Rastkari, Noushin; Momeniha, Fatemeh; Kashani, Homa; Gholampour, Akbar; Nazmara, Shahrokh; Alimohammadi, Mahmood; Goudarzi, Gholamreza; Yunesian, Masud

2014-01-01

Indoor/outdoor particulate matter (PM10, PM2.5, and PM1) and their water-soluble ions were measured in a retirement home and a school dormitory in Tehran, from May 2012 to January 2013. Hourly indoor/outdoor PM concentrations were measured using GRIMM dust monitors and 24-h aerosol samples were collected by low-volume air samplers. Water-soluble ions were determined using an ion chromatography (IC) instrument. Although the mean outdoor PM concentrations in both sampling sites were almost equal, the mean indoor PM10 in the school dormitory was approximately 1.35 times higher than that in the retirement home. During a Middle Eastern dust storm, the 24-h average PM10, PM2.5, and PM1 concentrations were respectively 3.4, 2.9, and 1.9 times as high as those in normal days outdoors and 3.4, 2.8, and 1.6 times indoors. The results indicated that secondary inorganic aerosols were the dominant water-soluble ions of indoor and outdoor PM. We found that the smaller the particle, the higher the percentage of secondary inorganic aerosols. Except for PM10 in the school dormitory, strong correlations were found between indoor and outdoor PM. We estimated that nearly 45% of PM10, 67% of PM2.5, and 79% of PM1 in the retirement home, and 32% of PM10, 76% of PM2.5, and 83% of PM1 in the school dormitory originated from outdoor environment.

Analyses of 1999 PM Data for the PM NAAQS Review

EPA Pesticide Factsheets

These files document all analyses conducted in association with the EPA memorandum from Terence Fitz-Simons, Scott Mathias, and Mike Rizzo titled Analyses of 1999 PM Data for the PM NAAQS Review, November 17, 2000.

A multivariate study for characterizing particulate matter (PM(10), PM(2.5), and PM(1)) in Seoul metropolitan subway stations, Korea.

PubMed

Kwon, Soon-Bark; Jeong, Wootae; Park, Duckshin; Kim, Ki-Tae; Cho, Kyung Hwa

2015-10-30

Given that around eight million commuters use the Seoul Metropolitan Subway (SMS) each day, the indoor air quality (IAQ) of its stations has attracted much public attention. We have monitored the concentration of particulate matters (PMx) (i.e., PM10, PM2.5, and PM1) in six major transfer stations per minute for three weeks during the summer, autumn, and winter in 2014 and 2015. The data were analyzed to investigate the relationship between PMx concentration and multivariate environmental factors using statistical methods. The average PM concentration observed was approximately two or three times higher than outdoor PM10 concentration, showing similar temporal patterns at concourses and platforms. This implies that outdoor PM10 is the most significant factor in controlling indoor PM concentration. In addition, the station depth and number of trains passing through stations were found to be additional influences on PMx. Principal component analysis (PCA) and self-organizing map (SOM) were employed, through which we found that the number of trains influences PM concentration in the vicinity of platforms only, and PMx hotspots were determined. This study identifies the external and internal factors affecting PMx characteristics in six SMS stations, which can assist in the development of effective IAQ management plans to improve public health. Copyright © 2015 Elsevier B.V. All rights reserved.

Draft Analyses of PM Data for the PM NAAQS Review

EPA Pesticide Factsheets

These files document all analyses conducted in association with the EPA memorandum from Mark Schmidt, David Mintz, Tesh Rao, and Lance McCluney titled Draft Analyses of PM Data for the PM NAAQS Review, January 31, 2005.

Indoor air quality modeling for PM 10, PM 2.5, and PM 1.0 in naturally ventilated classrooms of an urban Indian school building.

PubMed

Goyal, Radha; Khare, Mukesh

2011-05-01

Assessment of indoor air quality (IAQ) in classrooms of school buildings is of prime concern due to its potential effects on student's health and performance as they spend a substantial amount of their time (6-7 h per day) in schools. A number of airborne contaminants may be present in urban school environment. However, respirable suspended particulate matter (RSPM) is of great significance as they may significantly affect occupants' health. The objectives of the present study are twofold, one, to measure the concentrations of PM(10) (<10 microm), PM(2.5) (<2.5 microm), and PM(1.0) (<1.0 microm) in naturally ventilated classrooms of a school building located near a heavy-traffic roadway (9,755 and 4,296 vehicles/hour during weekdays and weekends, respectively); and second, to develop single compartment mass balance-based IAQ models for PM(10) (NVIAQM(pm10)), PM(2.5) (NVIAQM(pm2.5)), and PM(1.0) (NVIAQM(pm1.0)) for predicting their indoor concentrations. Outdoor RSPM levels and classroom characteristics, such as size, occupancy level, temperature, relative humidity, and CO(2) concentrations have also been monitored during school hours. Predicted indoor PM(10) concentrations show poor correlations with observed indoor PM(10) concentrations (R (2) = 0.028 for weekdays, and 0.47 for weekends). However, a fair degree of agreement (d) has been found between observed and predicted concentrations, i.e., 0.42 for weekdays and 0.59 for weekends. Furthermore, NVIAQM(pm2.5) and NVIAQM(pm1.0) results show good correlations with observed concentrations of PM(2.5) (R(2) = 0.87 for weekdays and 0.9 for weekends) and PM(1.0) (R(2) = 0.86 for weekdays and 0.87 for weekends). NVIAQM(pm10) shows the tendency to underpredict indoor PM(10) concentrations during weekdays as it does not take into account the occupant's activities and its effects on the indoor concentrations during the class hours. Intense occupant's activities cause resuspension or delayed deposition of PM(10). The model

CARDIOVASCULAR MORTALITY IN PHOENIX: PM1 IS A BETTER INDICATOR THAN PM2.5.

EPA Science Inventory

EPA has obtained a 3-year database of particulate matter (PM) in Phoenix, AZ from 1995 - 1997 that includes elemental analysis by XRF of daily PM2.5. During this time period PM1 and PM2.5 TEOMs were run simultaneously for about 7 months during two periods of the year. Regressio...

Shrimp serine proteinase homologues PmMasSPH-1 and -2 play a role in the activation of the prophenoloxidase system.

PubMed

Jearaphunt, Miti; Amparyup, Piti; Sangsuriya, Pakkakul; Charoensapsri, Walaiporn; Senapin, Saengchan; Tassanakajon, Anchalee

2015-01-01

Melanization mediated by the prophenoloxidase (proPO) activating system is a rapid immune response used by invertebrates against intruding pathogens. Several masquerade-like and serine proteinase homologues (SPHs) have been demonstrated to play an essential role in proPO activation in insects and crustaceans. In a previous study, we characterized the masquerade-like SPH, PmMasSPH1, in the black tiger shrimp Penaeus monodon as a multifunctional immune protein based on its recognition and antimicrobial activity against the Gram-negative bacteria Vibrio harveyi. In the present study, we identify a novel SPH, known as PmMasSPH2, composed of an N-terminal clip domain and a C-terminal SP-like domain that share high similarity to those of other insect and crustacean SPHs. We demonstrate that gene silencing of PmMasSPH1 and PmMasSPH2 significantly reduces PO activity, resulting in a high number of V. harveyi in the hemolymph. Interestingly, knockdown of PmMasSPH1 suppressed not only its gene transcript but also other immune-related genes in the proPO system (e.g., PmPPAE2) and antimicrobial peptides (e.g., PenmonPEN3, PenmonPEN5, crustinPm1 and Crus-likePm). The PmMasSPH1 and PmMasSPH2 also show binding activity to peptidoglycan (PGN) of Gram-positive bacteria. Using a yeast two-hybrid analysis and co-immunoprecipitation, we demonstrate that PmMasSPH1 specifically interacted with the final proteinase of the proPO cascade, PmPPAE2. Furthermore, the presence of both PmMasSPH1 and PmPPAE2 enhances PGN-induced PO activity in vitro. Taken together, these results suggest the importance of PmMasSPHs in the activation of the shrimp proPO system.

Shrimp Serine Proteinase Homologues PmMasSPH-1 and -2 Play a Role in the Activation of the Prophenoloxidase System

PubMed Central

Jearaphunt, Miti; Amparyup, Piti; Sangsuriya, Pakkakul; Charoensapsri, Walaiporn; Senapin, Saengchan; Tassanakajon, Anchalee

2015-01-01

Melanization mediated by the prophenoloxidase (proPO) activating system is a rapid immune response used by invertebrates against intruding pathogens. Several masquerade-like and serine proteinase homologues (SPHs) have been demonstrated to play an essential role in proPO activation in insects and crustaceans. In a previous study, we characterized the masquerade-like SPH, PmMasSPH1, in the black tiger shrimp Penaeus monodon as a multifunctional immune protein based on its recognition and antimicrobial activity against the Gram-negative bacteria Vibrio harveyi. In the present study, we identify a novel SPH, known as PmMasSPH2, composed of an N-terminal clip domain and a C-terminal SP-like domain that share high similarity to those of other insect and crustacean SPHs. We demonstrate that gene silencing of PmMasSPH1 and PmMasSPH2 significantly reduces PO activity, resulting in a high number of V. harveyi in the hemolymph. Interestingly, knockdown of PmMasSPH1 suppressed not only its gene transcript but also other immune-related genes in the proPO system (e.g., PmPPAE2) and antimicrobial peptides (e.g., PenmonPEN3, PenmonPEN5, crustinPm1 and Crus-likePm). The PmMasSPH1 and PmMasSPH2 also show binding activity to peptidoglycan (PGN) of Gram-positive bacteria. Using a yeast two-hybrid analysis and co-immunoprecipitation, we demonstrate that PmMasSPH1 specifically interacted with the final proteinase of the proPO cascade, PmPPAE2. Furthermore, the presence of both PmMasSPH1 and PmPPAE2 enhances PGN-induced PO activity in vitro. Taken together, these results suggest the importance of PmMasSPHs in the activation of the shrimp proPO system. PMID:25803442

Demonstration of a Semi-Autonomous Hybrid Brain-Machine Interface using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic

PubMed Central

McMullen, David P.; Hotson, Guy; Katyal, Kapil D.; Wester, Brock A.; Fifer, Matthew S.; McGee, Timothy G.; Harris, Andrew; Johannes, Matthew S.; Vogelstein, R. Jacob; Ravitz, Alan D.; Anderson, William S.; Thakor, Nitish V.; Crone, Nathan E.

2014-01-01

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 seconds for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs. PMID:24760914

PM2.5 and Diabetes and Hypertension Incidence in the Black Women's Health Study.

PubMed

Coogan, Patricia F; White, Laura F; Yu, Jeffrey; Burnett, Richard T; Seto, Edmund; Brook, Robert D; Palmer, Julie R; Rosenberg, Lynn; Jerrett, Michael

2016-03-01

Clinical studies have shown that exposure to fine particulate matter (PM2.5) can increase insulin resistance and blood pressure. The epidemiologic evidence for an association of PM2.5 exposure with the incidence of type 2 diabetes or hypertension is inconsistent. Even a modest association would have great public health importance given the ubiquity of exposure and high prevalence of the conditions. We used Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident type 2 diabetes and hypertension associated with exposure to PM2.5 in a large cohort of African American women living in 56 metropolitan areas across the US, using data from the Black Women's Health Study. Pollutant levels were estimated at all residential locations over follow-up with a hybrid model incorporating land use regression and Bayesian Maximum Entropy techniques. During 1995 to 2011, 4,387 cases of diabetes and 9,570 cases of hypertension occurred. In models controlling for age, questionnaire cycle, and metro area, there were positive associations with diabetes (HR = 1.13, 95% CI = 1.04, 1.24) and hypertension (HR = 1.06, 95% CI = 1.00, 1.12) per interquartile range of PM2.5 (2.9 μg/m). Multivariable HRs, however, were 0.99 (95% CI = 0.90, 1.09) for diabetes and 0.99 (95% CI = 0.93, 1.06) for hypertension. Our results provide little support for an association of PM2.5 with diabetes or hypertension incidence.

Study of the Productivity and Surface Quality of Hybrid EDM

NASA Astrophysics Data System (ADS)

Wankhade, Sandeepkumar Haribhau; Sharma, Sunil Bansilal

2016-01-01

The development of new, advanced engineering materials and the need for precise prototypes and low-volume production have made the electric discharge machining (EDM), an important manufacturing process to meet such demands. It is capable of machining geometrically complex and hard material components, that are precise and difficult-to-machine such as heat treated tool steels, composites, super alloys, ceramics, carbides etc. Conversely the low MRR limits its productivity. Abrasive water jet machine (AJM) tools are quick to setup and offer quick turn-around on the machine and could make parts out of virtually any material. They do not heat the material hence no heat affected zone and can make any intricate shape easily. The main advantages are flexibility, low heat production and ability to machine hard and brittle materials. Main disadvantages comprise the process produces a tapered cut and health hazards due to dry abrasives. To overcome the limitations and exploit the best of each of above processes; an attempt has been made to hybridize the processes of AJM and EDM. The appropriate abrasives routed with compressed air through the hollow electrode to construct the hybrid process i.e., abrasive jet electric discharge machining (AJEDM), the high speed abrasives could impinge on the machined surface to remove the recast layer caused by EDM process. The main process parameters were varied to explore their effects and experimental results show that AJEDM enhances the machining efficiency with better surface finish hence can fit the requirements of modern manufacturing applications.

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation.

PubMed

Clark, Alex M; Bunin, Barry A; Litterman, Nadia K; Schürer, Stephan C; Visser, Ubbo

2014-01-01

Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers.

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

PubMed Central

Bunin, Barry A.; Litterman, Nadia K.; Schürer, Stephan C.; Visser, Ubbo

2014-01-01

Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers. PMID:25165633

PM 10, PM 2.5 and PM 1.0—Emissions from industrial plants—Results from measurement programmes in Germany

NASA Astrophysics Data System (ADS)

Ehrlich, C.; Noll, G.; Kalkoff, W.-D.; Baumbach, G.; Dreiseidler, A.

Emission measurement programmes were carried out at industrial plants in several regions of Germany to determine the fine dust in the waste gases; the PM 10, PM 2.5 and PM 1.0 fractions were sampled using a cascade impactor technique. The installations tested included plants used for: combustion (brown coal, heavy fuel oil, wood), cement production, glass production, asphalt mixing, and processing plants for natural stones and sand, ceramics, metallurgy, chemical production, spray painting, wood processing/chip drying, poultry farming and waste treatment. In addition waste gas samples were taken from small-scale combustion units, like domestic stoves, firing lignite briquettes or wood. In total 303 individual measurement results were obtained during 106 different measurement campaigns. In the study it was found that in more than 70% of the individual emission measurement results from industrial plants and domestic stoves the PM 10 portion amounted to more than 90% and the PM 2.5 portion between 50% and 90% of the total PM (particulate matter) emission. For thermal industrial processes the PM 1.0 portion constituted between 20% and 60% of the total PM emission. Typical particle size distributions for different processes were presented as cumulative frequency distributions and as frequency distributions. The particle size distributions determined for the different plant types show interesting similarities and differences depending on whether the processes are thermal, mechanical, chemical or mixed. Consequently, for the groups of plant investigated, a major finding of this study has been that the particle size distribution is a characteristic of the industrial process. Attempts to correlate particle size distributions of different plants to different gas cleaning technologies did not lead to usable results.

40 CFR Table C-4 to Subpart C of... - Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 5 2011-07-01 2011-07-01 false Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods C Table C-4 to Subpart C of Part 53 Protection of Environment... Pt. 53, Subpt. C, Table C-4 Table C-4 to Subpart C of Part 53—Test Specifications for PM10, PM2.5 and...

40 CFR Table C-4 to Subpart C of... - Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 6 2012-07-01 2012-07-01 false Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods C Table C-4 to Subpart C of Part 53 Protection of Environment... Pt. 53, Subpt. C, Table C-4 Table C-4 to Subpart C of Part 53—Test Specifications for PM10, PM2.5 and...

40 CFR Table C-4 to Subpart C of... - Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 5 2010-07-01 2010-07-01 false Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods C Table C-4 to Subpart C of Part 53 Protection of Environment... Pt. 53, Subpt. C, Table C-4 Table C-4 to Subpart C of Part 53—Test Specifications for PM10, PM2.5 and...

Weather forecasting based on hybrid neural model

NASA Astrophysics Data System (ADS)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data

NASA Astrophysics Data System (ADS)

Ni, X. Y.; Huang, H.; Du, W. P.

2017-02-01

The PM2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM2.5. In this paper, we attempted to realize the relevance analysis and short-term prediction of PM2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO2, SO2, PM10) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO2, PM10, and the daily number of microblog entries with key words 'Beijing; Air pollution' show high mathematical correlation with PM2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM2.5 and it also helps to broaden the application of big data and the multi-source data mining methods.

Process capability improvement through DMAIC for aluminum alloy wheel machining

NASA Astrophysics Data System (ADS)

Sharma, G. V. S. S.; Rao, P. Srinivasa; Babu, B. Surendra

2017-07-01

This paper first enlists the generic problems of alloy wheel machining and subsequently details on the process improvement of the identified critical-to-quality machining characteristic of A356 aluminum alloy wheel machining process. The causal factors are traced using the Ishikawa diagram and prioritization of corrective actions is done through process failure modes and effects analysis. Process monitoring charts are employed for improving the process capability index of the process, at the industrial benchmark of four sigma level, which is equal to the value of 1.33. The procedure adopted for improving the process capability levels is the define-measure-analyze-improve-control (DMAIC) approach. By following the DMAIC approach, the C p, C pk and C pm showed signs of improvement from an initial value of 0.66, -0.24 and 0.27, to a final value of 4.19, 3.24 and 1.41, respectively.

Shrimp hemocyte homeostasis-associated protein (PmHHAP) interacts with WSSV134 to control apoptosis in white spot syndrome virus infection.

PubMed

Apitanyasai, Kantamas; Amparyup, Piti; Charoensapsri, Walaiporn; Sangsuriya, Pakkakul; Tassanakajon, Anchalee

2018-05-01

Hemocyte homeostasis-associated protein (PmHHAP) was first identified as a viral-responsive gene, due to a high upregulation in transcription following white spot syndrome virus (WSSV) infection. Functional studies using RNA interference have suggested that PmHHAP is involved in hemocyte homeostasis by controlling apoptosis during WSSV infection. In this study, the role of PmHHAP in host-viral interactions was further investigated. Yeast two-hybrid assay and co-immunoprecipitation revealed that PmHHAP binds to an anti-apoptosis protein, WSSV134. The viral protein WSSV134 is a late protein of WSSV, expressed 24 h post infection (hpi). Gene silencing of WSSV134 in WSSV-infected shrimp resulted in a reduction of the expression level of the viral replication marker genes VP28, wsv477, and ie-1, which suggests that WSSV134 is likely involved in viral propagation. However, co-silencing of PmHHAP and WSSV134 counteracted the effects on WSSV infection, which implies the importance of the host-pathogen interaction between PmHHAP and WSSV134 in WSSV infection. In addition, caspase 3/7 activity was noticeably induced in the PmHHAP and WSSV134 co-silenced shrimp upon WSSV infection. Moreover, PmHHAP and WSSV134 inhibited caspase-induced activation of PmCasp in vitro in a non-competitive manner. Taken together, these results suggest that PmHHAP and WSSV134 play a role in the host-pathogen interaction and work concordantly to control apoptosis in WSSV infection. Copyright © 2018 Elsevier Ltd. All rights reserved.

Preliminary analysis of variability in concentration of fine particulate matter - PM1.0, PM2.5 and PM10 in area of Poznań city

NASA Astrophysics Data System (ADS)

Sówka, Izabela; Chlebowska-Styś, Anna; Mathews, Barbara

2018-01-01

It is commonly known, that suspended particulate matter pose a threat to human life and health, negatively influence the flora, climate and also materials. Especially dangerous is the presence of high concentration of particulate matter in the area of cities, where density of population is high. The research aimed at determining the variability of suspended particulate matter concentration (PM1.0, PM2.5 and PM10) in two different thermal seasons, in the area of Poznań city. As a part of carried out work we analyzed the variability of concentrations and also performed a preliminary analysis of their correlation. Measured concentrations of particulate matter were contained within following ranges: PM10 - 8.7-69.6 μg/m3, PM2.5 - 2.2-88.5 μg/m3, PM1.0 - 2.5-22.9 μg/m3 in the winter season and 1.0-42.8 μg/m3 (PM10), 1.2-40.3 μg/m3 (PM2.5) and 2.7-10.4 (PM1.0) in the summer season. Preliminary correlative analysis indicated interdependence between the temperature of air, the speed of wind and concentration of particulate matter in selected measurement points. The values of correlation coefficients between the air temperature, speed of wind and concentrations of particulate matter were respectively equal to: for PM10: -0.59 and -0.55 (Jana Pawła II Street), -0.53 and -0.53 (Szymanowskiego Street), for PM2.5: -0.60 and -0.53 (Jana Pawła II Street) and for PM1.0 -0.40 and -0.59 (Jana Pawła II Street).

Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

NASA Astrophysics Data System (ADS)

Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

2017-10-01

Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

Spatial and seasonal variation of particulate matter (PM10 and PM2.5) in Middle Eastern classrooms

NASA Astrophysics Data System (ADS)

Elbayoumi, Maher; Ramli, Nor Azam; Md Yusof, Noor Faizah Fitri; Al Madhoun, Wesam

2013-12-01

Monitoring of PM10 and PM2.5 particularly in school microenvironments is extremely important due to their impact on the global burden of disease. PM10 and PM2.5 levels were monitored inside and outside the classrooms of twelve naturally ventilated schools located in Gaza strip, Palestine. The measurements were carried out using hand held particulate matter instrument during fall, winter and spring seasons from October 2011 to May 2012. The average concentration of indoor PM10 was 349.49 (±196.57) μg m-3 and for PM2.5 was 103.96 (±84.96) μg m-3. The indoor/outdoor ratios for PM10 and PM2.5 were found to be much greater than 1.00 for all case study schools due to resuspension of deposited particles from the floors. Furthermore, strong correlations were found between indoor-outdoor PM10 and PM2.5. The variations of PM10 and PM2.5 concentrations were significant for the three seasons. During winter, the mean indoor PM10 was 1.30 and 2.50 times higher than fall and spring concentrations respectively. Meanwhile, PM2.5 concentration in winter was 3.00 times higher than fall and spring concentrations. In relation to spatial variation, the concentration of PM10 in the lower storey level was significantly higher than the classrooms located in the higher storey level.

Control system for a hybrid powertrain system

DOEpatents

Naqvi, Ali K.; Demirovic, Besim; Gupta, Pinaki; Kaminsky, Lawrence A.

2014-09-09

A vehicle includes a powertrain with an engine, first and second torque machines, and a hybrid transmission. A method for operating the vehicle includes operating the engine in an unfueled state, releasing an off-going clutch which when engaged effects operation of the hybrid transmission in a first continuously variable mode, and applying a friction braking torque to a wheel of the vehicle to compensate for an increase in an output torque of the hybrid transmission resulting from releasing the off-going clutch. Subsequent to releasing the off-going clutch, an oncoming clutch which when engaged effects operation of the hybrid transmission in a second continuously variable mode is synchronized. Subsequent to synchronization of the oncoming clutch, the oncoming clutch is engaged.

Spatial distribution of particulate matter (PM10 and PM2.5) in Seoul Metropolitan Subway stations.

PubMed

Kim, Ki Youn; Kim, Yoon Shin; Roh, Young Man; Lee, Cheol Min; Kim, Chi Nyon

2008-06-15

The aims of this study are to examine the concentrations of PM10 and PM2.5 in areas within the Seoul Metropolitan Subway network and to provide fundamental data in order to protect respiratory health of subway workers and passengers from air pollutants. A total of 22 subway stations located on lines 1-4 were selected based on subway official's guidance. At these stations both subway worker areas (station offices, rest areas, ticket offices and driver compartments) and passengers areas (station precincts, subway carriages and platforms) were the sites used for measuring the levels of PM. The mean concentrations of PM10 and PM2.5 were relatively higher on platforms, inside subway carriages and in driver compartments than in the other areas monitored. The levels of PM10 and PM2.5 for station precincts and platforms exceeded the 24-h acceptable threshold limits of 150 microg/m3 for PM10 and 35 microg/m3 for PM2.5, which are regulated by the U.S. Environmental Protection Agency (EPA). However, levels measured in station and ticket offices fell below the respective threshold. The mean PM10 and PM2.5 concentrations on platforms located underground were significantly higher than those at ground level (p<0.05).

Using human brain activity to guide machine learning.

PubMed

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Analyses of 2000-2002 PM Data for the PM NAAQS Review

EPA Pesticide Factsheets

These files document all analyses conducted in association with the EPA memorandum from Mark Schmidt, David Mintz, Tesh Rao, and Lance McCluney titled Analyses of 2000-2002 PM Data for the PM NAAQS Review, August 29, 2003.

PmVRP15, a novel viral responsive protein from the black tiger shrimp, Penaeus monodon, promoted white spot syndrome virus replication.

PubMed

Vatanavicharn, Tipachai; Prapavorarat, Adisak; Jaree, Phattarunda; Somboonwiwat, Kunlaya; Tassanakajon, Anchalee

2014-01-01

Suppression subtractive hybridization of Penaeus monodon hemocytes challenged with white spot syndrome virus (WSSV) has identified the viral responsive gene, PmVRP15, as the highest up-regulated gene ever reported in shrimps. Expression analysis by quantitative real time RT-PCR revealed 9410-fold up-regulated level at 48 h post WSSV injection. Tissue distribution analysis showed that PmVRP15 transcript was mainly expressed in the hemocytes of shrimp. The full-length cDNA of PmVRP15 transcript was obtained and showed no significant similarity to any known gene in the GenBank database. The predicted open reading frame of PmVRP15 encodes for a deduced 137 amino acid protein containing a putative transmembrane helix. Immunofluorescent localization of the PmVRP15 protein revealed it accumulated around the nuclear membrane in all three types of shrimp hemocytes and that the protein was highly up-regulated in WSSV-infected shrimps. Double-stranded RNA interference-mediated gene silencing of PmVRP15 in P. monodon significantly decreased WSSV propagation compared to the control shrimps (injected with GFP dsRNA). The significant decrease in cumulative mortality rate of WSSV-infected shrimp following PmVRP15 knockdown was observed. These results suggest that PmVRP15 is likely to be a nuclear membrane protein and that it acts as a part of WSSV propagation pathway.

PmVRP15, a Novel Viral Responsive Protein from the Black Tiger Shrimp, Penaeus monodon, Promoted White Spot Syndrome Virus Replication

PubMed Central

Vatanavicharn, Tipachai; Prapavorarat, Adisak; Jaree, Phattarunda; Somboonwiwat, Kunlaya; Tassanakajon, Anchalee

2014-01-01

Suppression subtractive hybridization of Penaeus monodon hemocytes challenged with white spot syndrome virus (WSSV) has identified the viral responsive gene, PmVRP15, as the highest up-regulated gene ever reported in shrimps. Expression analysis by quantitative real time RT-PCR revealed 9410–fold up-regulated level at 48 h post WSSV injection. Tissue distribution analysis showed that PmVRP15 transcript was mainly expressed in the hemocytes of shrimp. The full-length cDNA of PmVRP15 transcript was obtained and showed no significant similarity to any known gene in the GenBank database. The predicted open reading frame of PmVRP15 encodes for a deduced 137 amino acid protein containing a putative transmembrane helix. Immunofluorescent localization of the PmVRP15 protein revealed it accumulated around the nuclear membrane in all three types of shrimp hemocytes and that the protein was highly up-regulated in WSSV-infected shrimps. Double-stranded RNA interference-mediated gene silencing of PmVRP15 in P. monodon significantly decreased WSSV propagation compared to the control shrimps (injected with GFP dsRNA). The significant decrease in cumulative mortality rate of WSSV-infected shrimp following PmVRP15 knockdown was observed. These results suggest that PmVRP15 is likely to be a nuclear membrane protein and that it acts as a part of WSSV propagation pathway. PMID:24637711

Long-term Exposure to PM2.5 and Mortality Among Older Adults in the Southeastern US.

PubMed

Wang, Yan; Shi, Liuhua; Lee, Mihye; Liu, Pengfei; Di, Qian; Zanobetti, Antonella; Schwartz, Joel D

2017-03-01

Little is known about what factors modify the effect of long-term exposure to PM2.5 on mortality, in part because in most previous studies certain groups such as rural residents and individuals with lower socioeconomic status (SES) are under-represented. We studied 13.1 million Medicare beneficiaries (age ≥65) residing in seven southeastern US states during 2000-2013 with 95 million person-years of follow-up. We predicted annual average of PM2.5 in each zip code tabulation area (ZCTA) using a hybrid spatiotemporal model. We fit Cox proportional hazards models to estimate the association between long-term PM2.5 and mortality. We tested effect modification by individual-level covariates (race, sex, eligibility for both Medicare and Medicaid, and medical history), neighborhood-level covariates (urbanicity, percentage below poverty level, lower education, median income, and median home value), mean summer temperature, and mass fraction of 11 PM2.5 components. The hazard ratio (HR) for death was 1.021 (95% confidence interval: 1.019, 1.022) per 1 μg m increase in annual PM2.5. The HR decreased with age. It was higher among males, non-whites, dual-eligible individuals, and beneficiaries with previous hospital admissions. It was higher in neighborhoods with lower SES or higher urbanicity. The HR increased with mean summer temperature. The risk associated with PM2.5 increased with relative concentration of elemental carbon, vanadium, copper, calcium, and iron and decreased with nitrate, organic carbon, and sulfate. Associations between long-term PM2.5 exposure and death were modified by individual-level, neighborhood-level variables, temperature, and chemical compositions.

Modeling and Analysis of High Torque Density Transverse Flux Machines for Direct-Drive Applications

NASA Astrophysics Data System (ADS)

Hasan, Iftekhar

Commercially available permanent magnet synchronous machines (PMSM) typically use rare-earth-based permanent magnets (PM). However, volatility and uncertainty associated with the supply and cost of rare-earth magnets have caused a push for increased research into the development of non-rare-earth based PM machines and reluctance machines. Compared to other PMSM topologies, the Transverse Flux Machine (TFM) is a promising candidate to get higher torque densities at low speed for direct-drive applications, using non-rare-earth based PMs. The TFMs can be designed with a very small pole pitch which allows them to attain higher force density than conventional radial flux machines (RFM) and axial flux machines (AFM). This dissertation presents the modeling, electromagnetic design, vibration analysis, and prototype development of a novel non-rare-earth based PM-TFM for a direct-drive wind turbine application. The proposed TFM addresses the issues of low power factor, cogging torque, and torque ripple during the electromagnetic design phase. An improved Magnetic Equivalent Circuit (MEC) based analytical model was developed as an alternative to the time-consuming 3D Finite Element Analysis (FEA) for faster electromagnetic analysis of the TFM. The accuracy and reliability of the MEC model were verified, both with 3D-FEA and experimental results. The improved MEC model was integrated with a Particle Swarm Optimization (PSO) algorithm to further enhance the capability of the analytical tool for performing rigorous optimization of performance-sensitive machine design parameters to extract the highest torque density for rated speed. A novel concept of integrating the rotary transformer within the proposed TFM design was explored to completely eliminate the use of magnets from the TFM. While keeping the same machine envelope, and without changing the stator or rotor cores, the primary and secondary of a rotary transformer were embedded into the double-sided TFM. The proposed

Source Apportionment and Elemental Composition of PM2.5 and PM10 in Jeddah City, Saudi Arabia.

PubMed

Khodeir, Mamdouh; Shamy, Magdy; Alghamdi, Mansour; Zhong, Mianhua; Sun, Hong; Costa, Max; Chen, Lung-Chi; Maciejczyk, Polina

2012-07-01

This paper presents the first comprehensive investigation of PM2.5 and PM10 composition and sources in Saudi Arabia. We conducted a multi-week multiple sites sampling campaign in Jeddah between June and September, 2011, and analyzed samples by XRF. The overall mean mass concentration was 28.4 ± 25.4 μg/m 3 for PM2.5 and 87.3 ± 47.3 μg/m 3 for PM10, with significant temporal and spatial variability. The average ratio of PM2.5/PM10 was 0.33. Chemical composition data were modeled using factor analysis with varimax orthogonal rotation to determine five and four particle source categories contributing significant amount of for PM2.5 and PM10 mass, respectively. In both PM2.5 and PM10 sources were (1) heavy oil combustion characterized by high Ni and V; (2) resuspended soil characterized by high concentrations of Ca, Fe, Al, and Si; and (3) marine aerosol. The two other sources in PM2.5 were (4) Cu/Zn source; (5) traffic source identified by presence of Pb, Br, and Se; while in PM10 it was a mixed industrial source. To estimate the mass contributions of each individual source category, the CAPs mass concentration was regressed against the factor scores. Cumulatively, resuspended soil and oil combustion contributed 77 and 82% mass of PM2.5 and PM10, respectively.

Source Apportionment and Elemental Composition of PM2.5 and PM10 in Jeddah City, Saudi Arabia

PubMed Central

Khodeir, Mamdouh; Shamy, Magdy; Alghamdi, Mansour; Zhong, Mianhua; Sun, Hong; Costa, Max; Chen, Lung-Chi; Maciejczyk, Polina

2014-01-01

This paper presents the first comprehensive investigation of PM2.5 and PM10 composition and sources in Saudi Arabia. We conducted a multi-week multiple sites sampling campaign in Jeddah between June and September, 2011, and analyzed samples by XRF. The overall mean mass concentration was 28.4 ± 25.4 μg/m3 for PM2.5 and 87.3 ± 47.3 μg/m3 for PM10, with significant temporal and spatial variability. The average ratio of PM2.5/PM10 was 0.33. Chemical composition data were modeled using factor analysis with varimax orthogonal rotation to determine five and four particle source categories contributing significant amount of for PM2.5 and PM10 mass, respectively. In both PM2.5 and PM10 sources were (1) heavy oil combustion characterized by high Ni and V; (2) resuspended soil characterized by high concentrations of Ca, Fe, Al, and Si; and (3) marine aerosol. The two other sources in PM2.5 were (4) Cu/Zn source; (5) traffic source identified by presence of Pb, Br, and Se; while in PM10 it was a mixed industrial source. To estimate the mass contributions of each individual source category, the CAPs mass concentration was regressed against the factor scores. Cumulatively, resuspended soil and oil combustion contributed 77 and 82% mass of PM2.5 and PM10, respectively. PMID:24634602

PM levels in urban area of Bejaia

NASA Astrophysics Data System (ADS)

Benaissa, Fatima; Maesano, Cara Nichole; Alkama, Rezak; Annesi-Maesano, Isabella

2017-04-01

Air pollution is not routinely measured in Bejaia City, Algeria, an urban area of around 200,000 inhabitants. We present first time measurements of particulate matter (PM) mass concentrations for this city (PM10, PM7, PM4, PM2.5 and PM1) over the course of one week, from July 8 to July 14, 2015. This study covered eight urban sampling sites and 169 measurements were obtained to determine mass concentration levels. Air pollution is not routinely measured in Bejaia City, Algeria, an urban area of around 200,000 inhabitants. We present first time measurements of particulate matter (PM) mass concentrations for this city (PM10, PM7, PM4, PM2.5 and PM1) over the course of one week, from July 8 to July 14, 2015. This study covered eight urban sampling sites and 169 measurements were obtained to determine mass concentration levels. The average city-wide PM10 and PM2.5 concentrations measured during this sampling were 87.8 ± 33.9 and 28.7 ± 10.6 µg/m3 respectively. These results show that particulate matter levels are high and exceed Algerian ambient air quality standards (maximum 80 µg/m3, without specifying the particle size). Further, PM10 and PM2.5 averages were well above the prescribed 24-hour average World Health Organization Air Quality Guidelines (WHO AQG) (50 µg/m3 for PM10 and 25 µg/m3 for PM2.5). The PM1, PM2,5, PM4 and PM7 fractions accounted for 15%, 32 %, 56% and 78% respectively of the PM10 measurements. Our analysis reveals that PM concentration variations in the study region were influenced primarily by traffic. In fact, lower PM10 concentrations (21.7 and 33.1 µg/m3) were recorded in residential sites while higher values (53.1, and 45.2 µg/m3) were registered in city centers. Keywords: Particulate matter, Urban area, vehicle fleet, Bejaia.

On the origin and variability of suspended particulate matter (PM1, PM2.5 and PM10) concentrations in Cyprus.

NASA Astrophysics Data System (ADS)

Pikridas, Michael; Vrekoussis, Mihalis; Mihalopoulos, Nikolaos; Kizas, Christos; Savvides, Chrysanthos; Sciare, Jean

2017-04-01

The Eastern Mediterranean (EM) lies at the crossroad of three different continents (Europe, Asia, and Africa). EM is a densely populated region including several cities with 3M inhabitants or more (e.g. Athens, Istanbul, Izmir, and Cairo). It has been identified as the most polluted area in Europe with respect to particulate matter (PM) mainly due to the combination of high photochemical activity, which causes pollutants to oxidize and partitioning in the particle phase, with the elevated pollutants emissions from neighboring regions. In addition, the proximity to Africa and the Middle East allows frequent transport of dust particles. At the center of the Eastern Mediterranean lies the island of Cyprus, which has received very little attention regarding its PM levels despite being the location in Europe most frequently impacted by air masses from the Middle East. Herewith, we present a historical PM archive that spans 2 decades. It involves ongoing monitoring on a daily basis of particulate matter with diameters smaller than 10 μm (PM10), 2.5 μm (PM2.5), and 1 μm (PM1) conducted in at least one, of the 12 currently existing air quality stations in Cyprus since 1997, 2005, and 2009, respectively. The most extended PM datasets correspond a) to the Agia Marina Xyliatou (AMX) monitoring station established at a remote area at the foothills of mount Troodos and b) that of the inland capital, Nicosia. Based on this long-term dataset, the diurnal, temporal and annual variability is assessed. Prior to 2010, PM10 concentration at all sites remained relatively constant, but at different levels, violating the annual EU legislated PM10 limit of 40 μg m-3. Since 2010, coarse mode levels have decreased at all sites. The reported decrease was equal to 30% at AMX. As a result, since 2010 the observed levels comply with the EU legislation threshold. Satellite observations of Aerosol Optical Thickness (AOT) Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA

The powdery mildew resistance gene Pm8 derived from rye is suppressed by its wheat ortholog Pm3.

PubMed

Hurni, Severine; Brunner, Susanne; Stirnweis, Daniel; Herren, Gerhard; Peditto, David; McIntosh, Robert A; Keller, Beat

2014-09-01

The powdery mildew resistance gene Pm8 derived from rye is located on a 1BL.1RS chromosome translocation in wheat. However, some wheat lines with this translocation do not show resistance to isolates of the wheat powdery mildew pathogen avirulent to Pm8 due to an unknown genetically dominant suppression mechanism. Here we show that lines with suppressed Pm8 activity contain an intact and expressed Pm8 gene. Therefore, the absence of Pm8 function in certain 1BL.1RS-containing wheat lines is not the result of gene loss or mutation but is based on suppression. The wheat gene Pm3, an ortholog of rye Pm8, suppressed Pm8-mediated powdery mildew resistance in lines containing Pm8 in a transient single-cell expression assay. This result was further confirmed in transgenic lines with combined Pm8 and Pm3 transgenes. Expression analysis revealed that suppression is not the result of gene silencing, either in wheat 1BL.1RS translocation lines carrying Pm8 or in transgenic genotypes with both Pm8 and Pm3 alleles. In addition, a similar abundance of the PM8 and PM3 proteins in single or double homozygous transgenic lines suggested that a post-translational mechanism is involved in suppression of Pm8. Co-expression of Pm8 and Pm3 genes in Nicotiana benthamiana leaves followed by co-immunoprecipitation analysis showed that the two proteins interact. Therefore, the formation of a heteromeric protein complex might result in inefficient or absent signal transmission for the defense reaction. These data provide a molecular explanation for the suppression of resistance genes in certain genetic backgrounds and suggest ways to circumvent it in future plant breeding. © 2014 The Authors The Plant Journal © 2014 John Wiley & Sons Ltd.

Chemical characterization and mass closure of PM10 and PM2.5 at an urban site in Karachi - Pakistan

NASA Astrophysics Data System (ADS)

Shahid, Imran; Kistler, Magdalena; Mukhtar, Azam; Ghauri, Badar M.; Ramirez-Santa Cruz, Carlos; Bauer, Heidi; Puxbaum, Hans

2016-03-01

A mass balance method is applied to assess main source contributions to PM2.5 and PM10 levels in Karachi. Carbonaceous species (elemental carbon, organic carbon, carbonate carbon), soluble ions (Ca++, Mg++, Na+, K+, NH4+, Cl-, NO3-, SO4-), saccharides (levoglucosan, galactosan, mannosan, sucrose, fructose, glucose, arabitol and mannitol) were determined in atmospheric fine (PM2.5) and coarse (PM10) aerosol samples collected under pre-monsoon conditions (March-April 2009) at an urban site in Karachi (Pakistan). The concentrations of PM2.5 and PM10 were found to be 75 μg/m3 and 437 μg/m3 respectively. The large difference between PM10 and PM2.5 originated predominantly from mineral dust. "Calcareous dust" and "siliceous dust" were the over all dominating material in PM, with 46% contribution to PM2.5 and 78% to PM10-2.5. Combustion particles and secondary organics (EC + OM) comprised 23% of PM2.5 and 6% of PM10-2.5. EC, as well as OC ambient levels were higher (59% and 56%) in PM10-2.5 than in PM2.5. Biomass burning contributed about 3% to PM2.5, and had a share of about 13% of ;EC + OM; in PM2.5. The impact of bioaerosol (fungal spores) was minor and had a share of 1 and 2% of the OC in the PM2.5 and PM10-2.5 size fractions. In case of secondary inorganic aerosols, ammonium sulphate (NH4)2SO4 contributes 4.4% to PM2.5 and no detectable quantity were found in fraction PM10-2.5. The sea salt contribution is about 2% both to PM2.5 and PM10-2.5.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

PubMed Central

Nalluri, MadhuSudana Rao; K., Kannan; M., Manisha

2017-01-01

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results. PMID:29065626

40 CFR Table C-4 to Subpart C of... - Test Specifications for PM 10, PM 2.5 and PM 10-2.5 Candidate Equivalent Methods

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 6 2014-07-01 2014-07-01 false Test Specifications for PM 10, PM 2.5 and PM 10-2.5 Candidate Equivalent Methods C Table C-4 to Subpart C of Part 53 Protection of... Reference Methods Pt. 53, Subpt. C, Table C-4 Table C-4 to Subpart C of Part 53—Test Specifications for PM...

40 CFR Table C-4 to Subpart C of... - Test Specifications for PM 10, PM 2.5 and PM 10-2.5 Candidate Equivalent Methods

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 6 2013-07-01 2013-07-01 false Test Specifications for PM 10, PM 2.5 and PM 10-2.5 Candidate Equivalent Methods C Table C-4 to Subpart C of Part 53 Protection of... Reference Methods Pt. 53, Subpt. C, Table C-4 Table C-4 to Subpart C of Part 53—Test Specifications for PM...

A highly oriented hybrid microarray modified electrode fabricated by a template-free method for ultrasensitive electrochemical DNA recognition

NASA Astrophysics Data System (ADS)

Shi, Lei; Chu, Zhenyu; Dong, Xueliang; Jin, Wanqin; Dempsey, Eithne

2013-10-01

Highly oriented growth of a hybrid microarray was realized by a facile template-free method on gold substrates for the first time. The proposed formation mechanism involves an interfacial structure-directing force arising from self-assembled monolayers (SAMs) between gold substrates and hybrid crystals. Different SAMs and variable surface coverage of the assembled molecules play a critical role in the interfacial directing forces and influence the morphologies of hybrid films. A highly oriented hybrid microarray was formed on the highly aligned and vertical SAMs of 1,4-benzenedithiol molecules with rigid backbones, which afforded an intense structure-directing power for the oriented growth of hybrid crystals. Additionally, the density of the microarray could be adjusted by controlling the surface coverage of assembled molecules. Based on the hybrid microarray modified electrode with a large specific area (ca. 10 times its geometrical area), a label-free electrochemical DNA biosensor was constructed for the detection of an oligonucleotide fragment of the avian flu virus H5N1. The DNA biosensor displayed a significantly low detection limit of 5 pM (S/N = 3), a wide linear response from 10 pM to 10 nM, as well as excellent selectivity, good regeneration and high stability. We expect that the proposed template-free method can provide a new reference for the fabrication of a highly oriented hybrid array and the as-prepared microarray modified electrode will be a promising paradigm in constructing highly sensitive and selective biosensors.Highly oriented growth of a hybrid microarray was realized by a facile template-free method on gold substrates for the first time. The proposed formation mechanism involves an interfacial structure-directing force arising from self-assembled monolayers (SAMs) between gold substrates and hybrid crystals. Different SAMs and variable surface coverage of the assembled molecules play a critical role in the interfacial directing forces and

A novel type 1/2 hybrid IncC plasmid carrying fifteen antimicrobial resistance genes recovered from Proteus mirabilis in China.

PubMed

Lei, Chang-Wei; Kong, Ling-Han; Ma, Su-Zhen; Liu, Bi-Hui; Chen, Yan-Peng; Zhang, An-Yun; Wang, Hong-Ning

2017-09-01

IncC plasmids are of great concern as vehicles of broad-spectrum cephalosporins and carbapenems resistance genes bla CMY and bla NDM . The aim of this study was to sequence and characterize a multidrug resistance (MDR) IncC plasmid (pPm14C18) recovered from Proteus mirabilis. pPm14C18 was identified in a CMY-2-producing P. mirabilis isolate from chicken in China in 2014, and could be transferred to Escherichia coli conferring an MDR phenotype. Whole genome sequencing confirmed pPm14C18 was a novel type 1/2 hybrid IncC plasmid 165,992bp in size, containing fifteen antimicrobial resistance genes. It harboured a novel MDR mosaic region comprised of a hybrid Tn21 tnp -pDU mer , in which bla CTX-M-65 , dfrA32 and ereA were firstly reported in IncC plasmid. Phylogenetic relationship reconstruction based on the nucleotide sequences of the 52 IncC backbones showed all type 1 IncC plasmids were clustered into one clade, and then merged with pPm14C18 and finally with the type 2 IncC plasmids and another type 1/2 hybrid IncC plasmid pYR1. The MDR IncC plasmids in P. mirabilis of animal origin might threaten public health, which should be drawn more attention. Copyright © 2017 Elsevier Inc. All rights reserved.

40 CFR 93.116 - Criteria and procedures: Localized CO, PM10, and PM2.5 violations (hot-spots).

Code of Federal Regulations, 2012 CFR

2012-07-01

..., PM10, and PM2.5 violations (hot-spots). 93.116 Section 93.116 Protection of Environment ENVIRONMENTAL....116 Criteria and procedures: Localized CO, PM10, and PM2.5 violations (hot-spots). (a) This paragraph... hot-spot analysis in PM10 and PM2.5 nonattainment and maintenance areas for FHWA/FTA projects that are...

40 CFR 93.116 - Criteria and procedures: Localized CO, PM10, and PM2.5 violations (hot-spots).

Code of Federal Regulations, 2013 CFR

2013-07-01

..., PM10, and PM2.5 violations (hot-spots). 93.116 Section 93.116 Protection of Environment ENVIRONMENTAL....116 Criteria and procedures: Localized CO, PM10, and PM2.5 violations (hot-spots). (a) This paragraph... hot-spot analysis in PM10 and PM2.5 nonattainment and maintenance areas for FHWA/FTA projects that are...

40 CFR 93.116 - Criteria and procedures: Localized CO, PM10, and PM2.5 violations (hot-spots).

Code of Federal Regulations, 2014 CFR

2014-07-01

..., PM10, and PM2.5 violations (hot-spots). 93.116 Section 93.116 Protection of Environment ENVIRONMENTAL....116 Criteria and procedures: Localized CO, PM10, and PM2.5 violations (hot-spots). (a) This paragraph... hot-spot analysis in PM10 and PM2.5 nonattainment and maintenance areas for FHWA/FTA projects that are...

40 CFR 93.116 - Criteria and procedures: Localized CO, PM10, and PM2.5 violations (hot-spots).

Code of Federal Regulations, 2011 CFR

2011-07-01

..., PM10, and PM2.5 violations (hot-spots). 93.116 Section 93.116 Protection of Environment ENVIRONMENTAL....116 Criteria and procedures: Localized CO, PM10, and PM2.5 violations (hot-spots). (a) This paragraph... hot-spot analysis in PM10 and PM2.5 nonattainment and maintenance areas for FHWA/FTA projects that are...

40 CFR 93.116 - Criteria and procedures: Localized CO, PM10, and PM2.5 violations (hot-spots).

Code of Federal Regulations, 2010 CFR

2010-07-01

..., PM10, and PM2.5 violations (hot-spots). 93.116 Section 93.116 Protection of Environment ENVIRONMENTAL....116 Criteria and procedures: Localized CO, PM10, and PM2.5 violations (hot-spots). (a) This paragraph... hot-spot analysis in PM10 and PM2.5 nonattainment and maintenance areas for FHWA/FTA projects that are...

75 FR 44142 - Determination of Attainment for PM-10; Fort Hall PM-10 Nonattainment Area, Idaho

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-28

... of Attainment for PM-10; Fort Hall PM-10 Nonattainment Area, Idaho AGENCY: Environmental Protection Agency (EPA). ACTION: Final rule. SUMMARY: EPA is finalizing its determination that the Fort Hall PM-10... Standard for particulate matter with an aerodynamic diameter of less than or equal to 10 microns (PM-10...

On the operation of machines powered by quantum non-thermal baths

DOE PAGES

Niedenzu, Wolfgang; Gelbwaser-Klimovsky, David; Kofman, Abraham G.; ...

2016-08-02

Diverse models of engines energised by quantum-coherent, hence non-thermal, baths allow the engine efficiency to transgress the standard thermodynamic Carnot bound. These transgressions call for an elucidation of the underlying mechanisms. Here we show that non-thermal baths may impart not only heat, but also mechanical work to a machine. The Carnot bound is inapplicable to such a hybrid machine. Intriguingly, it may exhibit dual action, concurrently as engine and refrigerator, with up to 100% efficiency. Here, we conclude that even though a machine powered by a quantum bath may exhibit an unconventional performance, it still abides by the traditional principlesmore » of thermodynamics.« less

Determination of water-soluble elements in PM2.5, PM10, and PM2.5-10 collected in the surroundings of power plants

NASA Astrophysics Data System (ADS)

Zajusz-Zubek, Elwira; Mainka, Anna; Kaczmarek, Konrad

2018-01-01

The analysis reported in this study was performed to characterize the concentrations and water-soluble content of trace elements (As, Cd, Co, Cr, Hg, Mn, Ni, Pb, Sb and Se) in PM2.5, PM10 and PM2.5-10 samples collected in the surroundings of power plants in southern Poland. The solubility of trace elements bound to PM2.5 and PM10 was higher than for PM2.5-10, and in most cases, significant differences were revealed in the relative percentage concentrations of the water-soluble fractions. The occurrence of Cd, Cr, Mn, Ni, Pb and Se in first PCA (Principal Component Analysis) factor (PC1) - indicate coal combustion processes as the potential source of these elements. Other factors indicate two further anthropogenic sources: the resuspension of road dust due to vehicular activities and waste burning in domestic sources - factor (PC2), and, soil dust sources affected by fugitive dust from the mining processes and unpaved roads, as well as transportation and deposition of coal -factor (PC3).

Hybrid charge division multiplexing method for silicon photomultiplier based PET detectors

NASA Astrophysics Data System (ADS)

Park, Haewook; Ko, Guen Bae; Lee, Jae Sung

2017-06-01

Silicon photomultiplier (SiPM) is widely utilized in various positron emission tomography (PET) detectors and systems. However, the individual recording of SiPM output signals is still challenging owing to the high granularity of the SiPM; thus, charge division multiplexing is commonly used in PET detectors. Resistive charge division method is well established for reducing the number of output channels in conventional multi-channel photosensors, but it degrades the timing performance of SiPM-based PET detectors by yielding a large resistor-capacitor (RC) constant. Capacitive charge division method, on the other hand, yields a small RC constant and provides a faster timing response than the resistive method, but it suffers from an output signal undershoot. Therefore, in this study, we propose a hybrid charge division method which can be implemented by cascading the parallel combination of a resistor and a capacitor throughout the multiplexing network. In order to compare the performance of the proposed method with the conventional methods, a 16-channel Hamamatsu SiPM (S11064-050P) was coupled with a 4  ×  4 LGSO crystal block (3  ×  3  ×  20 mm3) and a 9  ×  9 LYSO crystal block (1.2  ×  1.2  ×  10 mm3). In addition, we tested a time-over-threshold (TOT) readout using the digitized position signals to further demonstrate the feasibility of the time-based readout of multiplexed signals based on the proposed method. The results indicated that the proposed method exhibited good energy and timing performance, thus inheriting only the advantages of conventional resistive and capacitive methods. Moreover, the proposed method showed excellent pulse shape uniformity that does not depend on the position of the interacted crystal. Accordingly, we can conclude that the hybrid charge division method is useful for effectively reducing the number of output channels of the SiPM array.

Contribution of microenvironments to personal exposures to PM10 and PM2.5 in summer and winter

NASA Astrophysics Data System (ADS)

Hwang, Yunhyung; Lee, Kiyoung

2018-02-01

Personal exposure to particulate matter (PM) can be affected by time-activity patterns and microenvironmental concentrations. Particle size is closely associated with potential health problems, where smaller particles have greater effects on health. We investigated the effects of time-activity patterns on personal exposure and the contribution of the microenvironment to personal exposure to PM with maximal diameters of 10 μm and 2.5 μm (PM10 and PM2.5, respectively) in summer and winter. Technicians carried a nephelometer to detect various sizes of PM while engaging in one of nine scripted time-location-activity patterns. The scripted activities were based on the time-activity patterns of nine groups of inhabitants of Seoul, Korea. The monitoring was repeated in summer and winter to assess seasonal variation. The differences of personal exposures to PM10 and PM2.5 in summer and winter were not significant. The greatest PM concentrations occurred in restaurants. The PM2.5/PM10 ratios were varied from 0.35 at schools to 0.92 at stores. In both seasons, the residential indoor microenvironment was the largest contributor to personal PM exposure. The other major contributors were restaurants, offices, schools, buses, and walking, although their contributions differed by season and particle size. The different microenvironmental contributions among the activity pattern groups suggest that personal exposure significantly differs according to activity pattern.

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

NASA Astrophysics Data System (ADS)

Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

Source apportionment of ambient PM10 and PM2.5 in Haikou, China

NASA Astrophysics Data System (ADS)

Fang, Xiaozhen; Bi, Xiaohui; Xu, Hong; Wu, Jianhui; Zhang, Yufen; Feng, Yinchang

2017-07-01

In order to identify the sources of PM10 and PM2.5 in Haikou, 60 ambient air samples were collected in winter and spring, respectively. Fifteen elements (Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn and Pb), water-soluble ions (SO42 - and NO3-), and organic carbon (OC) and elemental carbon (EC) were analyzed. It was clear that the concentration of particulate matter was higher in winter than in spring. The value of PM2.5/PM10 was > 0.6. Moreover, the proportions of TC, ions, Na, Al, Si and Ca were more high in PM10 and PM2.5. The SOC concentration was estimated by the minimum OC/EC ratio method, and deducted from particulate matter compositions when running CMB model. According to the results of CMB model, the resuspended dust (17.5-35.0%), vehicle exhaust (14.9-23.6%) and secondary particulates (20.4-28.8%) were the major source categories of ambient particulate matter. Additionally, sea salt also had partial contribution (3-8%). And back trajectory analysis results showed that particulate matter was greatly affected by regional sources in winter, while less affected in spring. So particulate matter was not only affected by local sources, but also affected by sea salt and regional sources in coastal cities. Further research could focuses on establishing the actual secondary particles profiles and identifying the local and regional sources of PM at once by one model or analysis method.

The NB-LRR gene Pm60 confers powdery mildew resistance in wheat.

PubMed

Zou, Shenghao; Wang, Huan; Li, Yiwen; Kong, Zhaosheng; Tang, Dingzhong

2018-04-01

Powdery mildew is one of the most devastating diseases of wheat. To date, few powdery mildew resistance genes have been cloned from wheat due to the size and complexity of the wheat genome. Triticum urartu is the progenitor of the A genome of wheat and is an important source for powdery mildew resistance genes. Using molecular markers designed from scaffolds of the sequenced T. urartu accession and standard map-based cloning, a powdery mildew resistance locus was mapped to a 356-kb region, which contains two nucleotide-binding and leucine-rich repeat domain (NB-LRR) protein-encoding genes. Virus-induced gene silencing, single-cell transient expression, and stable transformation assays demonstrated that one of these two genes, designated Pm60, confers resistance to powdery mildew. Overexpression of full-length Pm60 and two allelic variants in Nicotiana benthamiana leaves induced hypersensitive cell death response, but expression of the coiled-coil domain alone was insufficient to induce hypersensitive response. Yeast two-hybrid, bimolecular fluorescence complementation and luciferase complementation imaging assays showed that Pm60 protein interacts with its neighboring NB-containing protein, suggesting that they might be functionally related. The identification and cloning of this novel wheat powdery mildew resistance gene will facilitate breeding for disease resistance in wheat. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

An automated procedure for developing hybrid computer simulations of turbofan engines

NASA Technical Reports Server (NTRS)

Szuch, J. R.; Krosel, S. M.

1980-01-01

A systematic, computer-aided, self-documenting methodology for developing hybrid computer simulations of turbofan engines is presented. The methodology makes use of a host program that can run on a large digital computer and a machine-dependent target (hybrid) program. The host program performs all of the calculations and date manipulations needed to transform user-supplied engine design information to a form suitable for the hybrid computer. The host program also trims the self contained engine model to match specified design point information. A test case is described and comparisons between hybrid simulation and specified engine performance data are presented.

AC Loss Analysis of MgB2-Based Fully Superconducting Machines

NASA Astrophysics Data System (ADS)

Feddersen, M.; Haran, K. S.; Berg, F.

2017-12-01

Superconducting electric machines have shown potential for significant increase in power density, making them attractive for size and weight sensitive applications such as offshore wind generation, marine propulsion, and hybrid-electric aircraft propulsion. Superconductors exhibit no loss under dc conditions, though ac current and field produce considerable losses due to hysteresis, eddy currents, and coupling mechanisms. For this reason, many present machines are designed to be partially superconducting, meaning that the dc field components are superconducting while the ac armature coils are conventional conductors. Fully superconducting designs can provide increases in power density with significantly higher armature current; however, a good estimate of ac losses is required to determine the feasibility under the machines intended operating conditions. This paper aims to characterize the expected losses in a fully superconducting machine targeted towards aircraft, based on an actively-shielded, partially superconducting machine from prior work. Various factors are examined such as magnet strength, operating frequency, and machine load to produce a model for the loss in the superconducting components of the machine. This model is then used to optimize the design of the machine for minimal ac loss while maximizing power density. Important observations from the study are discussed.

Influence of magnet eddy current on magnetization characteristics of variable flux memory machine

NASA Astrophysics Data System (ADS)

Yang, Hui; Lin, Heyun; Zhu, Z. Q.; Lyu, Shukang

2018-05-01

In this paper, the magnet eddy current characteristics of a newly developed variable flux memory machine (VFMM) is investigated. Firstly, the machine structure, non-linear hysteresis characteristics and eddy current modeling of low coercive force magnet are described, respectively. Besides, the PM eddy current behaviors when applying the demagnetizing current pulses are unveiled and investigated. The mismatch of the required demagnetization currents between the cases with or without considering the magnet eddy current is identified. In addition, the influences of the magnet eddy current on the demagnetization effect of VFMM are analyzed. Finally, a prototype is manufactured and tested to verify the theoretical analyses.

Comprehensive assessment of PM2.5 physicochemical properties during the Southeast Asia dry season (southwest monsoon)

NASA Astrophysics Data System (ADS)

Khan, Md Firoz; Sulong, Nor Azura; Latif, Mohd Talib; Nadzir, Mohd Shahrul Mohd; Amil, Norhaniza; Hussain, Dini Fajrina Mohd; Lee, Vernon; Hosaini, Puteri Nurafidah; Shaharom, Suhana; Yusoff, Nur Amira Yasmin Mohd; Hoque, Hossain Mohammed Syedul; Chung, Jing Xiang; Sahani, Mazrura; Mohd Tahir, Norhayati; Juneng, Liew; Maulud, Khairul Nizam Abdul; Abdullah, Sharifah Mastura Syed; Fujii, Yusuke; Tohno, Susumu; Mizohata, Akira

2016-12-01

A comprehensive assessment of fine particulate matter (PM2.5) compositions during the Southeast Asia dry season is presented. Samples of PM2.5 were collected between 24 June and 14 September 2014 using a high-volume sampler. Water-soluble ions, trace species, rare earth elements, and a range of elemental carbon (EC) and organic carbon were analyzed. The characterization and source apportionment of PM2.5 were investigated. The results showed that the 24 h PM2.5 concentration ranged from 6.64 to 68.2 µg m-3. Meteorological driving factors strongly governed the diurnal concentration of aerosol, while the traffic in the morning and evening rush hours coincided with higher levels of CO and NO2. The correlation analysis for non sea-salt K+-EC showed that EC is potentially associated with biomass burning events, while the formation of secondary organic carbon had a moderate association with motor vehicle emissions. Positive matrix factorization (PMF) version 5.0 identified the sources of PM2.5: (i) biomass burning coupled with sea salt [I] (7%), (ii) aged sea salt and mixed industrial emissions (5%), (iii) road dust and fuel oil combustion (7%), (iv) coal-fired combustion (25%), (v) mineral dust (8%), (vi) secondary inorganic aerosol (SIA) coupled with F- (15%), and (vii) motor vehicle emissions coupled with sea salt [II] (24%). Motor vehicle emissions, SIA, and coal-fired power plant are the predominant sources contributing to PM2.5. The response of the potential source contribution function and Hybrid Single-Particle Lagrangian Integrated Trajectory backward trajectory model suggest that the outline of source regions were consistent to the sources by PMF 5.0.

Characterization and Cytotoxicity of PM<0.2, PM0.2–2.5 and PM2.5–10 around MSWI in Shanghai, China

PubMed Central

Cao, Lingling; Zeng, Jianrong; Liu, Ke; Bao, Liangman; Li, Yan

2015-01-01

Background: The potential impact of municipal solid waste incineration (MSWI), which is an anthropogenic source of aerosol emissions, is of great public health concern. This study investigated the characterization and cytotoxic effects of ambient ultrafine particles (PM<0.2), fine particles (PM0.2–2.5) and coarse particles (PM2.5–10) collected around a municipal solid waste incineration (MSWI) plant in the Pudong district of Shanghai. Methods: Mass concentrations of trace elements in particulate matter (PM) samples were determined using ICP-MS (Inductively Coupled Plasma Mass Spectrometry). The cytotoxicity of sampled atmospheric PM was evaluated by cell viability and reactive oxygen species (ROS) levels in A549 cells. Result: The mass percentage of PM0.2–2.5 accounted for 72.91% of the total mass of PM. Crustal metals (Mg, Al, and Ti) were abundant in the coarse particles, while the anthropogenic elements (V, Ni, Cu, Zn, Cd, and Pb) were dominant in the fine particles. The enrichment factors of Zn, Cd and Pb in the fine and ultrafine particles were extremely high (>100). The cytotoxicity of the size-resolved particles was in the order of coarse particles < fine particles < ultrafine particles. Conclusions: Fine particles dominated the MSWI ambient particles. Emissions from the MSWI could bring contamination of anthropogenic elements (Zn, Cd and Pb) into ambient environment. The PM around the MSWI plant displayed an additive toxic effect, and the ultrafine and fine particles possessed higher biological toxicity than the coarse particles. PMID:25985309

Hybrid SPECT/CT imaging in neurology.

PubMed

Ciarmiello, Andrea; Giovannini, Elisabetta; Meniconi, Martina; Cuccurullo, Vincenzo; Gaeta, Maria Chiara

2014-01-01

In recent years, the SPECT/CT hybrid modality has led to a rapid development of imaging techniques in nuclear medicine, opening new perspectives for imaging staff and patients as well. However, while, the clinical role of positron emission tomography-computed tomography (PET-CT) is well consolidated, the diffusion and the consequent value of single-photon emission tomography-computed tomography (SPECT-CT) has yet to be weighed, Hence, there is a need for a careful analysis, comparing the "potential" benefits of the hybrid modality with the "established" ones of the standalone machine. The aim of this article is to analyze the impact of this hybrid tool on the diagnosis of diseases of the central nervous system, comparing strengths and weaknesses of both modalities through the use of SWOT analysis.

Properties of CF/PA6 friction spun hybrid yarns for textile reinforced thermoplastic composites

NASA Astrophysics Data System (ADS)

Hasan, MMB; Nitsche, S.; Abdkader, A.; Cherif, Ch

2017-10-01

Due to their excellent strength, rigidity and damping properties as well as low weight, carbon fibre reinforced composites (CFRC) are widely being used for load bearing structures. On the other hand, with an increased demand und usage of CFRCs, effective methods to re-use waste carbon fibre (CF) materials, which are recoverable either from the process scraps or from the end-of-life components are attracting increased attention. In this paper, hybrid yarns consisting of staple CF and polyamide 6 (PA 6) are manufactured on a DREF-3000 friction spinning machine with various machine parameters such as spinning drum speed and suction air pressure. The relationship between different textile physical properties of the hybrid yarns, such as tensile strength and elongation with different spinning parameters and CF content of hybrid yarn is investigated. Furthermore, the tensile properties of uni-directional (UD) composites manufactured from the developed hybrid yarn shows 80% of the UD composite strength made from CF filament yarn.

Probable Health Risks Due to Exposure to Outdoor PM2.5 in India

NASA Astrophysics Data System (ADS)

Dey, S.; Chowdhury, S.

2014-12-01

Particulate matter of size <2.5 μm (commonly referred to as PM2.5) is considered to be the best indicator of health risks due to exposure to particulate pollution. Unlike the decreasing trends in the developed countries, aerosol loading continues to increase over the Indian subcontinent in the recent past, exposing ~1.6 billion population at risk. Lack of direct measurements prompted us to utilize satellite data in establishing a robust long-term database of surface PM2.5 at high spatial resolution. The hybrid approach utilizes a chemical transport model to constrain the relation between columnar aerosol optical depth (AOD) and surface PM2.5 and establish mean monthly conversion factor. Satellite-derived daily AODs for the period 2000-2012 are then converted to PM2.5 using the conversion factors. The dataset (after validation against coincident in-situ measurements and bias-correction) was used to carry out the exposure assessment. 51% of the population is exposed to PM2.5 concentration exceeding WHO air quality interim target-3 threshold (35 μg m-3). The health impacts are categorized in terms of four diseases - cardio ortho-pulmonary disease (COPD), stroke, ischemic heart disease (IHD) and lung cancer (LC). In absence of any region-specific cohort study, published studies are consulted to estimate risk. The risks relative to the background concentration of 10 μg m-3 are estimated by logarithmic fitting of the individual cohort studies against the corresponding PM2.5 concentration. This approach considers multiple (>100) cohort studies across a wide variety of adult population from various socio-economic backgrounds. Therefore, the calculated risks are considered to be better estimates in relative to any one particular type of risk function model (e.g. linear 50 or linear 70 or exponential). The risk values are used to calculate the additional mortality due to exposure to PM2.5 in each of the administrative districts in India to identify the vulnerable regions

Breeding highbush blueberry cultivars adapted to machine harvest for the fresh market

USDA-ARS?s Scientific Manuscript database

In recent years, world blueberry production has been split evenly between processing and fresh fruit markets. Machine harvest of highbush blueberry [northern highbush (NHB, Vaccinium corymbosum L.), southern highbush (SHB, Vaccinium corymbosum interspecific hybrids), and rabbiteye (RE, Vaccinium vi...

Concentration characteristics of extractable organohalogens in PM2.5 and PM10 in Beijing, China

NASA Astrophysics Data System (ADS)

Xu, Diandou; Dan, Mo; Song, Yan; Chai, Zhifang; Zhuang, Guoshun

PM2.5 and PM10 samples were simultaneously collected at a downtown site in Beijing from May 2002 to April 2003 and analyzed by instrumental neutron activation analysis (INAA) and gas chromatography (GC) combined with organic solvent extraction method for the concentrations and distributions of extractable organohalogens (EOX), including extractable organo-chlorine (EOCl), -bromine (EOBr) and -iodine compounds (EOI), and organochlorinated pesticides (OCPs) and polychlorinated biphenyls (PCBs). The concentrations of EOX were increasing in the order of EOCl≫EOBr˜EOI. EOCl accounted for 73-88% and 69-91% of EOX in PM2.5 and PM10, respectively, suggesting that EOCl was the major component of the organohalogens in the atmosphere. The relative proportions of the known organochlorines (such as HCHs, DDTs, chlordanes, and PCBs) to total EOCl were 0.04-0.7% and 0.06-0.3% in PM2.5 and PM10, respectively, which implied that most of EOCl measured in aerosol was unknown. The ratios of α/γ-HCH (0.9-1.5) and p,p'-DDE+DDD/ p,p'-DDT (0.2-0.5) revealed the presence of the recent use of lindane and DDTs or impure dicofol in Beijing. In the plots of the logarithm of the OCPs concentrations versus reciprocal temperature (1/T), their linear relations were observed for PM2.5, which could be partly explained by temperature differences, but poor linearity for PM10.

Local PM10 and PM2.5 emission inventories from agricultural tillage and harvest in northeastern China.

PubMed

Chen, Weiwei; Tong, Daniel Q; Zhang, Shichun; Zhang, Xuelei; Zhao, Hongmei

2017-07-01

Mineral particles or particulate matters (PMs) emitted during agricultural activities are major recurring sources of atmospheric aerosol loading. However, precise PM inventory from agricultural tillage and harvest in agricultural regions is challenged by infrequent local emission factor (EF) measurements. To understand PM emissions from these practices in northeastern China, we measured EFs of PM 10 and PM 2.5 from three field operations (i.e., tilling, planting and harvesting) in major crop production (i.e., corn and soybean), using portable real-time PM analyzers and weather station data. County-level PM 10 and PM 2.5 emissions from agricultural tillage and harvest were estimated, based on local EFs, crop areas and crop calendars. The EFs averaged (107±27), (17±5) and 26mg/m 2 for field tilling, planting and harvesting under relatively dry conditions (i.e., soil moisture <15%), respectively. The EFs of PM from field tillage and planting operations were negatively affected by topsoil moisture. The magnitude of PM 10 and PM 2.5 emissions from these three activities were estimated to be 35.1 and 9.8 kilotons/yr in northeastern China, respectively, of which Heilongjiang Province accounted for approximately 45%. Spatiotemporal distribution showed that most PM 10 emission occurred in April, May and October and were concentrated in the central regions of the northeastern plain, which is dominated by dryland crops. Further work is needed to estimate the contribution of agricultural dust emissions to regional air quality in northeastern China. Copyright © 2016. Published by Elsevier B.V.

Contamination characteristics and possible sources of PM10 and PM2.5 in different functional areas of Shanghai, China

NASA Astrophysics Data System (ADS)

Wang, Jun; Hu, Zimei; Chen, Yuanyuan; Chen, Zhenlou; Xu, Shiyuan

2013-04-01

From July 2009 through September 2010, PM10 and PM2.5 were collected at two different functional areas in Shanghai (Baoshan district, an industrial area, and Putuo district, a mixed-use area of residential, commercial, and educational compounds). In our analysis, 15 elements were determined using a 710-ES Inductively Coupled Plasma-Emission Spectrometer (ICP-AES). The contents of PM2.5, PM10, and metal elements at the two different sites were comparatively analyzed. The results show that the mean annual concentrations of PM10 and PM2.5 (149.22 μg m-3 and 103.07 μg m-3, respectively) in Baoshan district were significantly higher than those in Putuo district (97.44 μg m-3 and 62.25 μg m-3 respectively). The concentrations of PM10 and PM2.5 were both greatest in winter and lowest in summer, with the two different sites exhibiting the same seasonal variation. It was found that the proportions of 15 metal elements in PM10 and PM2.5 in Baoshan district were 20.49% and 20.56%, respectively, while the proportions in Putuo district were higher (25.98% and 25.93%, respectively). In addition, the proportions of eight heavy metals in PM10 and PM2.5 were 5.50% and 3.07%, respectively, for Baoshan district, while these proportions in Putuo district were 3.18% and 2.77%, respectively, indicating that heavy metal pollution is more pronounced in Baoshan district. Compared with cities in developed countries, the total levels of PM10, PM2.5 and heavy metals in Shanghai were slightly higher. Scanning electron microscopy (SEM) and principal component analysis (PCA) suggested that the possible sources of PM10 in Baoshan district were ground level fugitive dust, traffic sources, and industrial activities, whereas PM2.5 mainly originated from industrial activities, coal combustion, and traffic sources. The sources are same for PM10 and PM2.5 in Putuo region, which originate from traffic sources and ground level fugitive dust.

Source apportionment studies on particulate matter (PM10 and PM2.5) in ambient air of urban Mangalore, India.

PubMed

Kalaiarasan, Gopinath; Balakrishnan, Raj Mohan; Sethunath, Neethu Anitha; Manoharan, Sivamoorthy

2018-07-01

Particulate matter (PM 10 and PM 2.5 ) samples were collected from six sites in urban Mangalore and the mass concentrations for PM 10 and PM 2.5 were measured using gravimetric technique. The measurements were found to exceed the national ambient air quality standards (NAAQS) limits, with the highest concentration of 231.5 μg/m 3 for PM 10 particles at Town hall and 120.3 μg/m 3 for PM 2.5 particles at KMC Attavar. The elemental analysis using inductively coupled plasma optical emission spectrophotometer (ICPOES) revealed twelve different elements (As, Ba, Cd, Cr, Cu, Fe, Mg, Mn, Mo, Ni, Sr and Zn) for PM 10 particles and nine different elements (Ba, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Sr and Zn) for PM 2.5 particles. Similarly, ionic composition of these samples measured by ion chromatography (IC) divulged nine different ions (F - , Cl - , NO 3 - , PO 4 3- , SO 4 2- , Na + , K + , Mg 2+ and Ca 2+ ) for PM 10 particles and ten different ions (F - , Cl - , NO 3 - , PO 4 3- , SO 4 2- , Na + , NH 4 + , K + , Mg 2+ and Ca 2+ ) for PM 2.5 particles. The source apportionment study of PM 10 and PM 2.5 for urban Mangalore in accordance with these six sample sites using chemical mass balance model (CMBv8.2) revealed nine and twelve predominant contributors for both PM 10 and PM 2.5 , respectively. The highest contributor of PM 10 was found to be paved road dust followed by diesel and gasoline vehicle emissions. Correspondingly, PM 2.5 was found to be contributed mainly from two-wheeler vehicle emissions followed by four-wheeler and heavy vehicle emissions (diesel vehicles). The current study depicts that the PM 10 and PM 2.5 in ambient air of Mangalore region has 70% of its contribution from vehicular emissions (both exhaust and non-exhaust). Copyright © 2018 Elsevier Ltd. All rights reserved.

Isolation and Role of PmRGL2 in GA-mediated Floral Bud Dormancy Release in Japanese Apricot (Prunus mume Siebold et Zucc.)

PubMed Central

Lv, Lin; Huo, Ximei; Wen, Luhua; Gao, Zhihong; Khalil-ur-Rehman, Muhammad

2018-01-01

Bud dormancy release is regulated by gibberellins (GAs). DELLA proteins are highly conserved and act as negative regulators in GA signaling pathway. The present study established a relationship between PmRGL2 in Japanese apricot and GA4 levels during dormancy release of floral buds. Overexpression of PmRGL2 in poplar delayed the onset of bud dormancy and resulted in dwarf plants, relative to wild-type trees. PmRGL2 exhibited higher expression during ecodormancy and relatively lower expression during endodormancy. The relative level of GA4 exhibited an increasing trend at the transition from endodormancy to ecodormancy and displayed a similar expression pattern of genes related to GA metabolism, PmGA20ox2, PmGA3ox1, PmGID1b, in both Japanese apricot and transgenic poplar. These results suggests that PmRGL2 acts as an integrator and negative regulator of dormancy via a GA-signaling pathway. Moreover, an interaction between RGL2 and SLY1 in a yeast two hybrid (Y2H) system further suggests that SCF E3 ubiquitin ligases, such as SLY1, may be a critical factor in the regulation of RGL2 through an SCFSLY1-proteasome pathway. Our study demonstrated that PmRGL2 plays a negative role in bud dormancy release by regulating the GA biosynthetic enzymes, GA20ox and GA3ox1 and the GA receptor, GID1b. PMID:29434610

Day-Ahead PM2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution

PubMed Central

Wang, Deyun; Liu, Yanling; Luo, Hongyuan; Yue, Chenqiang; Cheng, Sheng

2017-01-01

Accurate PM2.5 concentration forecasting is crucial for protecting public health and atmospheric environment. However, the intermittent and unstable nature of PM2.5 concentration series makes its forecasting become a very difficult task. In order to improve the forecast accuracy of PM2.5 concentration, this paper proposes a hybrid model based on wavelet transform (WT), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by differential evolution (DE) algorithm. Firstly, WT is employed to disassemble the PM2.5 concentration series into a number of subsets with different frequencies. Secondly, VMD is applied to decompose each subset into a set of variational modes (VMs). Thirdly, DE-BP model is utilized to forecast all the VMs. Fourthly, the forecast value of each subset is obtained through aggregating the forecast results of all the VMs obtained from VMD decomposition of this subset. Finally, the final forecast series of PM2.5 concentration is obtained by adding up the forecast values of all subsets. Two PM2.5 concentration series collected from Wuhan and Tianjin, respectively, located in China are used to test the effectiveness of the proposed model. The results demonstrate that the proposed model outperforms all the other considered models in this paper. PMID:28704955

Characterization of short- and medium-chain chlorinated paraffins in outdoor/indoor PM10/PM2.5/PM1.0 in Beijing, China.

PubMed

Huang, Huiting; Gao, Lirong; Xia, Dan; Qiao, Lin; Wang, Runhua; Su, Guijin; Liu, Wenbin; Liu, Guorui; Zheng, Minghui

2017-06-01

Persistent organic pollutants (POPs) were listed in the Stockholm Convention, because of their adverse health effects, persistence, bioaccumulation and ubiquitous presence in the environment. Short chain chlorinated paraffins (SCCPs), chlorinated derivatives of n-alkanes, have been listed as candidate POPs under Stockholm Convention. Inhalation uptake was an important exposure pathway for non-occupational adult human and the pollution of particle matter has caused great concern. There are some studies focused on POPs such as polychlorinated biphenyls, polychlorinated dibenzo-p-dioxins and dibenzofurans and polybrominated diphenyl ethers in different size particles. However, there were no studies that discussed CP concentrations in particulate matter (PM) with different sizes. In this study, a total of 30 PM samples were collected both outdoors and indoors at a sampling site in Beijing. These samples were used to investigate the concentrations and distributions of SCCPs and medium chain chlorinated paraffins (MCCPs) in PM fractions of different sizes, and to evaluate inhalation exposure risks. The results showed that the average SCCPs and MCCPs in the outdoor PM 10 were 23.9 and 3.6 ng m -3 , while the mean values in indoor were 61.1 and 6.9 ng m -3 , respectively. The levels of SCCPs and MCCPs in indoor and outdoor were relatively high. SCCP and MCCP concentrations in the indoor PM 10 /PM 2.5 /PM 1.0 samples were higher than the corresponding values in the outdoor, because of the using of some products containing CPs in the indoors, like paints and coatings, leather and rubber products. In both outdoor and indoor air, CPs are mainly associated with particles ≤2.5 μm in diameter. The main homolog groups for both SCCPs and MCCPs were C 10-11 Cl 7-8 . It is assumed that SCCPs in the outdoor and indoor PM samples may mainly derive from the production and use of CP-42 and CP-52. Copyright © 2017 Elsevier Ltd. All rights reserved.

Main components and human health risks assessment of PM10, PM2.5, and PM1 in two areas influenced by cement plants

NASA Astrophysics Data System (ADS)

Sánchez-Soberón, Francisco; Rovira, Joaquim; Mari, Montse; Sierra, Jordi; Nadal, Martí; Domingo, José L.; Schuhmacher, Marta

2015-11-01

Particulate matter (PM) is widely recorded as a source of diseases, being more harmful those particles with smaller size. PM is released to the environment as a consequence of different activities, being one of them cement production. The objective of this pilot study was to characterize PM of different sizes around cement facilities to have a preliminary approach of their origin, and evaluate their potential health risks. For that purpose, three fractions of PM (10, 2.5, and 1) were collected in the nearby area of two cement plants with different backgrounds (urban and rural) in different seasons. Subsequently, main components, outdoor and indoor concentrations, exposure, and human health risks were assessed. Greatest levels of PM1, organic matter, and metals were found in urban location, especially in winter. Consequently, environmental exposure and human health risks registered their highest values in the urban plant during wintertime. Exposure was higher for indoor activities, expressing some metals their peak values in the PM1 fraction. Non-carcinogenic risks were below the safety threshold (HQ < 1). Carcinogenic risks for most of the metals were below the limit of 10-5, except for Cr (VI), which exceeded it in both locations, but being in the range considered as assumable (10-6-10-4).

Implementing Molecular Dynamics for Hybrid High Performance Computers - 1. Short Range Forces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, W Michael; Wang, Peng; Plimpton, Steven J

The use of accelerators such as general-purpose graphics processing units (GPGPUs) have become popular in scientific computing applications due to their low cost, impressive floating-point capabilities, high memory bandwidth, and low electrical power requirements. Hybrid high performance computers, machines with more than one type of floating-point processor, are now becoming more prevalent due to these advantages. In this work, we discuss several important issues in porting a large molecular dynamics code for use on parallel hybrid machines - 1) choosing a hybrid parallel decomposition that works on central processing units (CPUs) with distributed memory and accelerator cores with shared memory,more » 2) minimizing the amount of code that must be ported for efficient acceleration, 3) utilizing the available processing power from both many-core CPUs and accelerators, and 4) choosing a programming model for acceleration. We present our solution to each of these issues for short-range force calculation in the molecular dynamics package LAMMPS. We describe algorithms for efficient short range force calculation on hybrid high performance machines. We describe a new approach for dynamic load balancing of work between CPU and accelerator cores. We describe the Geryon library that allows a single code to compile with both CUDA and OpenCL for use on a variety of accelerators. Finally, we present results on a parallel test cluster containing 32 Fermi GPGPUs and 180 CPU cores.« less

Insect-machine Hybrid System: Remote Radio Control of a Freely Flying Beetle (Mercynorrhina torquata).

PubMed

Vo Doan, T Thang; Sato, Hirotaka

2016-09-02

The rise of radio-enabled digital electronic devices has prompted the use of small wireless neuromuscular recorders and stimulators for studying in-flight insect behavior. This technology enables the development of an insect-machine hybrid system using a living insect platform described in this protocol. Moreover, this protocol presents the system configuration and free flight experimental procedures for evaluating the function of the flight muscles in an untethered insect. For demonstration, we targeted the third axillary sclerite (3Ax) muscle to control and achieve left or right turning of a flying beetle. A thin silver wire electrode was implanted on the 3Ax muscle on each side of the beetle. These were connected to the outputs of a wireless backpack (i.e., a neuromuscular electrical stimulator) mounted on the pronotum of the beetle. The muscle was stimulated in free flight by alternating the stimulation side (left or right) or varying the stimulation frequency. The beetle turned to the ipsilateral side when the muscle was stimulated and exhibited a graded response to an increasing frequency. The implantation process and volume calibration of the 3 dimensional motion capture camera system need to be carried out with care to avoid damaging the muscle and losing track of the marker, respectively. This method is highly beneficial to study insect flight, as it helps to reveal the functions of the flight muscle of interest in free flight.

Direct RNA detection without nucleic acid purification and PCR: Combining sandwich hybridization with signal amplification based on branched hybridization chain reaction.

PubMed

Xu, Yao; Zheng, Zhi

2016-05-15

We have developed a convenient, robust and low-cost RNA detection system suitable for high-throughput applications. This system uses a highly specific sandwich hybridization to capture target RNA directly onto solid support, followed by on-site signal amplification via 2-dimensional, branched hybridizing chain polymerization through toehold-mediated strand displacement reaction. The assay uses SYBR Green to detect targets at concentrations as low as 1 pM, without involving nucleic acid purification or any enzymatic reaction, using ordinary oligonucleotides without modification or labeling. The system was demonstrated in the detection of malaria RNA in blood and GAPDH gene expression in cell lysate. Copyright © 2015 Elsevier B.V. All rights reserved.

Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

NASA Astrophysics Data System (ADS)

Kachach, Redouane; Cañas, José María

2016-05-01

Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade-Lucas-Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

Analysis and control on changeable wheel tool system of hybrid grinding and polishing machine tool for blade finishing

NASA Astrophysics Data System (ADS)

He, Qiuwei; Lv, Xingming; Wang, Xin; Qu, Xingtian; Zhao, Ji

2017-01-01

Blade is the key component in the energy power equipment of turbine, aircraft engines and so on. Researches on the process and equipment for blade finishing become one of important and difficult point. To control precisely tool system of developed hybrid grinding and polishing machine tool for blade finishing, the tool system with changeable wheel for belt polishing is analyzed in this paper. Firstly, the belt length and wrap angle of each wheel in different position of tension wheel swing angle in the process of changing wheel is analyzed. The reasonable belt length is calculated by using MATLAB, and relationships between wrap angle of each wheel and cylinder expansion amount of contact wheel are obtained. Then, the control system for changeable wheel tool structure is developed. Lastly, the surface roughness of blade finishing is verified by experiments. Theoretical analysis and experimental results show that reasonable belt length and wheel wrap angle can be obtained by proposed analysis method, the changeable wheel tool system can be controlled precisely, and the surface roughness of blade after grinding meets the design requirements.

Estimation of PM2.5 and PM10 using ground-based AOD measurements during KORUS-AQ campaign

NASA Astrophysics Data System (ADS)

Koo, J. H.; Kim, J.; Kim, S.; Go, S.; Lee, S.; Lee, H.; Mok, J.; Hong, J.; Lee, J.; Eck, T. F.; Holben, B. N.

2017-12-01

During the KORUS-AQ campaign (2 May - 12 June, 2016), aerosol optical depth (AOD) was obtained at multiple channels using various ground-based instruments at Yonsei University, Seoul: AERONET sunphotometer, SKYNET skyradiometer, Brewer spectrophotometer, and multi-filter rotating shadowband radiometer (MFRSR). At the same location, planetary boundary layer (PBL) height and vertical profile of backscattering coefficients also can be obtained based on the celiometer measurements. Using celiometer products and various AODs, we try to estimate the amount of particular matter (PM2.5 and PM10) and validate with in-situ surface PM2.5 and PM10 measurements from AIRKOREA network. Direct comparison between PM2.5 and AOD reveals that the ultraviolet(UV) channel AOD has better correlations, due to the higher sensitivity of short wavelength to the fine-mode particle. In contrast, PM10 shows the highest correlation with the near-infrared(NIR) AOD. Next, we extract the boundary-layer portion of AOD using either PBL height or vertical profile of backscattering coefficients to compare with PM2.5 and PM10. Both results enhance the correlation, but consideration of weighting factor calculated from backscattering coefficients shows larger contribution to the correlation increase. Finally, we performed the multiple linear regression to estimate PM2.5 and PM10 using AODs. Consideration of meteorology (temperature, wind speed, and relative humidity) can enhance the correlation and also O3 and NO2 consideration highly contributes to the high correlation. This finding implies the importance to consider the ambient condition of secondary aerosol formation related to the PM2.5 variation. Multiple regression model finally finds the correlation 0.7-0.8, and diminishes the wavelength-dependent correlation patterns.

Hybrid statistical testing for nuclear material accounting data and/or process monitoring data in nuclear safeguards

DOE PAGES

Burr, Tom; Hamada, Michael S.; Ticknor, Larry; ...

2015-01-01

The aim of nuclear safeguards is to ensure that special nuclear material is used for peaceful purposes. Historically, nuclear material accounting (NMA) has provided the quantitative basis for monitoring for nuclear material loss or diversion, and process monitoring (PM) data is collected by the operator to monitor the process. PM data typically support NMA in various ways, often by providing a basis to estimate some of the in-process nuclear material inventory. We develop options for combining PM residuals and NMA residuals (residual = measurement - prediction), using a hybrid of period-driven and data-driven hypothesis testing. The modified statistical tests canmore » be used on time series of NMA residuals (the NMA residual is the familiar material balance), or on a combination of PM and NMA residuals. The PM residuals can be generated on a fixed time schedule or as events occur.« less

A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

PubMed

Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

2017-02-15

The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

Wintertime vertical variations in particulate matter (PM) and precursor concentrations in the San Joaquin Valley during the California Regional Coarse PM/Fine PM Air Quality Study.

PubMed

Brown, Steven G; Roberts, Paul T; McCarthy, Michael C; Lurmann, Frederick W; Hyslop, Nicole P

2006-09-01

Air quality monitoring was conducted at a rural site with a tower in the middle of California's San Joaquin Valley (SJV) and at elevated sites in the foothills and mountains surrounding the SJV for the California Regional PM10/ PM2.5 Air Quality Study. Measurements at the surface and n a tower at 90 m were collected in Angiola, CA, from December 2000 through February 2001 and included hourly black carbon (BC), particle counts from optical particle counters, nitric oxide, ozone, temperature, relative humidity, wind speed, and direction. Boundary site measurements were made primarily using 24-hr integrated particulate matter (PM) samples. These measurements were used to understand the vertical variations of PM and PM precursors, the effect of stratification in the winter on concentrations and chemistry aloft and at the surface, and the impact of aloft-versus-surface transport on PM concentrations. Vertical variations of concentrations differed among individual species. The stratification may be important to atmospheric chemistry processes, particularly nighttime nitrate formation aloft, because NO2 appeared to be oxidized by ozone in the stratified aloft layer. Additionally, increases in accumulation-mode particle concentrations in the aloft layer during a fine PM (PM2.5) episode corresponded with increases in aloft nitrate, demonstrating the likelihood of an aloft nighttime nitrate formation mechanism. Evidence of local transport at the surface and regional transport aloft was found; transport processes also varied among the species. The distribution of BC appeared to be regional, and BC was often uniformly mixed vertically. Overall, the combination of time-resolved tower and surface measurements provided important insight into PM stratification, formation, and transport.

Levels of PM2.5/PM10 and associated metal(loid)s in rural households of Henan Province, China.

PubMed

Wu, Fuyong; Wang, Wei; Man, Yu Bon; Chan, Chuen Yu; Liu, Wenxin; Tao, Shu; Wong, Ming Hung

2015-04-15

Although a majority of China's rural residents use solid fuels (biomass and coal) for household cooking and heating, clean energy such as electricity and liquid petroleum gas is becoming more popular in the rural area. Unfortunately, both solid fuels and clean energy could result in indoor air pollution. Daily respirable particulate matter (PM≤10 μm) and inhalable particulate matter (PM≤2.5 μm) were investigated in kitchens, sitting rooms and outdoor area in rural Henan during autumn (Sep to Oct 2012) and winter (Jan 2013). The results showed that PM (PM2.5 and PM10) and associated metal(loid)s varied among the two seasons and the four types of domestic energy used. Mean concentrations of PM2.5 and PM10 in kitchens during winter were 59.2-140.4% and 30.5-145.1% higher than those during autumn, respectively. Similar with the trends of PM2.5 and PM10, concentrations of As, Pb, Zn, Cd, Cu, Ni and Mn in household PM2.5 and PM10 were apparently higher in winter than those in autumn. The highest mean concentrations of PM2.5 and PM10 (368.5 and 588.7 μg m(-3)) were recorded in sitting rooms in Baofeng during winter, which were 5.7 and 3.9 times of corresponding health based guidelines for PM2.5 and PM10, respectively. Using coal can result in severe indoor air pollutants including PM and associated metal(loid)s compared with using crop residues, electricity and gas in rural Henan Province. Rural residents' exposure to PM2.5 and PM10 would be roughly reduced by 13.5-22.2% and 8.9-37.7% via replacing coal or crop residues with electricity. The present study suggested that increased use of electricity as domestic energy would effectively improve indoor air quality in rural China. Copyright © 2015 Elsevier B.V. All rights reserved.

Variability of levels and composition of PM10 and PM2.5 in the Barcelona metro system

NASA Astrophysics Data System (ADS)

Querol, X.; Moreno, T.; Karanasiou, A.; Reche, C.; Alastuey, A.; Viana, M.; Font, O.; Gil, J.; de Miguel, E.; Capdevila, M.

2012-03-01

From an environmental perspective, the underground metro system is one of the cleanest forms of public transportation in urban agglomerations. Current studies report contradicting results regarding air quality in the metro systems: whereas some reveal poor air quality, others report PM levels which are lower or of the same order of magnitude than those measured in traffic sites above ground level. The present work assesses summer indoor air quality and passenger exposure in the Barcelona metro, focusing on PM levels and their metal contents. In addition, the impact on indoor air quality of platform screen door systems (automated systems consisting of closed rail track and platforms) is evaluated, to determine whether these systems reduce passenger exposure to PM when compared with conventional systems (open tracks and platforms). In the Barcelona metro, PM levels inside the trains in summer are amongst the lowest reported for worldwide metro systems (11-32 μPM2.5 m-3). This is most probably due to the air conditioning system working in all carriages of the Barcelona metro during the whole year. On the platforms, levels were considerably higher, reaching mean levels of 59 and 88 μgPM2.5 m-3 in the new (L9) and old (L3) lines, respectively. PM10 data are also reported in the present study, but comparison with other metro systems is more difficult due to the scarcity of data compared with PM2.5. Results showed clear PM daily cycles, with a drastic increase from 06:00 to 07:00 a.m., a diurnal maximum from 07:00 a.m. to 10:00 p.m., and marked decreases between 10:00 p.m. and 05:00 a.m. The elements with the highest enrichment are those associated with wheel or brake abrasion products (Ba, Fe, Cu, Mn, Cr, Sb, As, Mo, Co, Sr, among others). Laminar hematite (Fe2O3) was the dominant particle type, being mainly originated by mechanical abrasion of the rail track and wheels. Regarding passenger exposure to PM inside the metro system, the contribution of commuting by metro

In Vitro Exposures in Diesel Exhaust Atmospheres: Resuspension of PM from Filters Verses Direct Deposition of PM from Air

PubMed Central

Lichtveld, Kim M.; Ebersviller, Seth M.; Sexton, Kenneth G.; Vizuete, William; Jaspers, Ilona; Jeffries, Harvey E.

2012-01-01

One of the most widely used in vitro particulate matter (PM) exposures methods is the collection of PM on filters, followed by resuspension in a liquid medium, with subsequent addition onto a cell culture. To avoid disruption of equilibria between gases and PM, we have developed a direct in vitro sampling and exposure method (DSEM) capable of PM-only exposures. We hypothesize that the separation of phases and post-treatment of filter-collected PM significantly modifies the toxicity of the PM compared to direct deposition, resulting in a distorted view of the potential PM health effects. Controlled test environments were created in a chamber that combined diesel exhaust with an urban-like mixture. The complex mixture was analyzed using both the DSEM and concurrently-collected filter samples. The DSEM showed that PM from test atmospheres produced significant inflammatory response, while the resuspension exposures at the same exposure concentration did not. Increasing the concentration of resuspended PM sixteen times was required to yield measurable IL-8 expression. Chemical analysis of the resuspended PM indicated a total absence of carbonyl compounds compared to the test atmosphere during the direct-exposures. Therefore, collection and resuspension of PM into liquid modifies its toxicity and likely leads to underestimating toxicity. PMID:22834915

Investigating the contribution of shipping emissions to atmospheric PM2.5 using a combined source apportionment approach.

PubMed

Lang, Jianlei; Zhou, Ying; Chen, Dongsheng; Xing, Xiaofan; Wei, Lin; Wang, Xiaotong; Zhao, Na; Zhang, Yanyun; Guo, Xiurui; Han, Lihui; Cheng, Shuiyuan

2017-10-01

Many studies have been conducted focusing on the contribution of land emission sources to PM 2.5 in China; however, little attention had been paid to other contributions, especially the secondary contributions from shipping emissions to atmospheric PM 2.5 . In this study, a combined source apportionment approach, including principle component analysis (PCA) and WRF-CMAQ simulation, was applied to identify both primary and secondary contributions from ships to atmospheric PM 2.5 . An intensive PM 2.5 observation was conducted from April 2014 to January 2015 in Qinhuangdao, which was close to the largest energy output port of China. The chemical components analysis results showed that the primary component was the major contributor to PM 2.5 , with proportions of 48.3%, 48.9%, 55.1% and 55.4% in spring, summer, autumn and winter, respectively. The secondary component contributed higher fractions in summer (48.2%) and winter (36.8%), but had lower percentages in spring (30.1%) and autumn (32.7%). The hybrid source apportionment results indicated that the secondary contribution (SC) of shipping emissions to PM 2.5 could not be ignored. The annual average SC was 2.7%, which was comparable to the primary contribution (2.9%). The SC was higher in summer (5.3%), but lower in winter (1.1%). The primary contributions to atmospheric PM 2.5 were 3.0%, 2.5%, 3.4% and 2.7% in spring, summer, autumn and winter, respectively. As for the detailed chemical components, the contributions of shipping emissions were 2.3%, 0.5%, 0.1%, 1.0%, 1.7% and 0.1% to elements & sea salt, primary organic aerosol (POA), element carbon (EC), nitrate, sulfate and secondary organic carbon (SOA), respectively. The results of this study will further the understanding of the implications of shipping emissions in PM 2.5 pollution. Copyright © 2017 Elsevier Ltd. All rights reserved.

Creation of operation algorithms for combined operation of anti-lock braking system (ABS) and electric machine included in the combined power plant

NASA Astrophysics Data System (ADS)

Bakhmutov, S. V.; Ivanov, V. G.; Karpukhin, K. E.; Umnitsyn, A. A.

2018-02-01

The paper considers the Anti-lock Braking System (ABS) operation algorithm, which enables the implementation of hybrid braking, i.e. the braking process combining friction brake mechanisms and e-machine (electric machine), which operates in the energy recovery mode. The provided materials focus only on the rectilinear motion of the vehicle. That the ABS task consists in the maintenance of the target wheel slip ratio, which depends on the tyre-road adhesion coefficient. The tyre-road adhesion coefficient was defined based on the vehicle deceleration. In the course of calculated studies, the following operation algorithm of hybrid braking was determined. At adhesion coefficient ≤0.1, driving axle braking occurs only due to the e-machine operating in the energy recovery mode. In other cases, depending on adhesion coefficient, the e-machine provides the brake torque, which changes from 35 to 100% of the maximum available brake torque. Virtual tests showed that values of the wheel slip ratio are close to the required ones. Thus, this algorithm makes it possible to implement hybrid braking by means of the two sources creating the brake torque.

Human health risk due to variations in PM10-PM2.5 and associated PAHs levels

NASA Astrophysics Data System (ADS)

Sosa, Beatriz S.; Porta, Andrés; Colman Lerner, Jorge Esteban; Banda Noriega, Roxana; Massolo, Laura

2017-07-01

WHO (2012) reports that chronic exposure to air pollutants, including particulate matter (PM), causes the death of 7 million people, constituting the most important environmental risk for health in the world. IARC classifies contaminated outdoor air as carcinogenic, Group 1 category. However, in our countries there are few studies regarding air pollution levels and possible associated effects on public health. The current study determined PM and associated polycyclic aromatic hydrocarbons (PAHs) levels in outdoor air, identified their possible emission sources and analysed health risks in the city of Tandil (Argentina). PM10 and PM2.5 samples were collected using a low volume sampler (MiniVol TAS) in three areas: city centre, industrial and residential. Concentrations were determined by gravimetric methods and the content of the US EPA 16 priority PAHs was found by high performance liquid chromatography (HPLC). Description of the main emission sources and selection of monitoring sites resulted from spatial analysis and the IVE (International Vehicle Emissions) model was used in the characterisation of the traffic flow. Median values of 35.7 μgm-3 and 9.6 μgm-3 in PM10 and PM2.5 respectively and characteristic profiles were found for each area. Local values PAHs associated to PM10 and PM2.5, in general, were lower than 10ngm-3. The estimated Unit Risk for the three areas exceeds US EPA standards (9 × 10-5). The number of deaths attributable to short term exposure to outdoor PM10 was 4 cases in children under 5 years of age, and 21 cases in total population, for a relative risk of 1.037.

Characterization of PM-PEMS for in-use measurements conducted during validation testing for the PM-PEMS measurement allowance program

NASA Astrophysics Data System (ADS)

Khan, M. Yusuf; Johnson, Kent C.; Durbin, Thomas D.; Jung, Heejung; Cocker, David R.; Bishnu, Dipak; Giannelli, Robert

2012-08-01

This study provides an evaluation of the latest Particulate Matter-Portable Emissions Measurement Systems (PM-PEMS) under different environmental and in-use conditions. It characterizes four PM measurement systems based on different measurement principles. At least three different units were tested for each PM-PEMS to account for variability. These PM-PEMS were compared with a UC Riverside's mobile reference laboratory (MEL). PM measurements were made from a class 8 truck with a 2008 Cummins diesel engine with a diesel particulate filter (DPF). A bypass around the DPF was installed in the exhaust to achieve a brake specific PM (bsPM) emissions level of 25 mg hp-1h-1. PM was dominated by elemental carbon (EC) during non-regeneration conditions and by hydrated sulfate (H2SO4.6H2O) during regeneration. The photo-acoustic PM-PEMS performed best, with a linear regression slope of 0.90 and R2 of 0.88 during non-regenerative conditions. With the addition of a filter, the photo-acoustic PM-PEMS slightly over reported than the total PM mass (slope = 1.10, R2 = 0.87). Under these same non-regeneration conditions, a PM-PEMS equipped with a quartz crystal microbalance (QCM) technology performed the poorest, and had a slope of 0.22 and R2 of 0.13. Re-tests performed on upgraded QCM PM-PEMS showed a better slope (0.66), and a higher R2 of 0.25. In the case of DPF regeneration, all PM-PEMS performed poorly, with the best having a slope of 0.20 and R2 of 0.78. Particle size distributions (PSD) showed nucleation during regeneration, with a shift of particle size to smaller diameters (˜64 nm to ˜13 nm) with elevated number concentrations when compared to non-regeneration conditions.

A hybrid modulation for the dissemination of weather data to aircraft

NASA Technical Reports Server (NTRS)

Akos, Dennis M.

1991-01-01

Ohio University is continuing to conduct research to improve its system for weather data dissemination to aircraft. The current experimental system transmit compressed weather radar reflectivity patterns from a ground based station to aircraft. Although an effective system, the limited frequency spectrum does not provide a channel for transmission. This introduces the idea of a hybrid modulation. The hybrid technique encodes weather data using phase modulation (PM) onto an existing aeronautical channel which employs amplitude modulation (AM) for voice signal transmission. Ideally, the two modulations are independent of one another. The planned implementation and basis of the system are the reviewed.

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

PubMed Central

Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-01-01

Background Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Objective Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Methods Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Results Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. Conclusions To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. PMID:29506966

COMPARISON OF PARALLEL AND SERIES HYBRID POWERTRAINS FOR TRANSIT BUS APPLICATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Zhiming; Daw, C Stuart; Smith, David E

2016-01-01

The fuel economy and emissions of both conventional and hybrid buses equipped with emissions aftertreatment were evaluated via computational simulation for six representative city bus drive cycles. Both series and parallel configurations for the hybrid case were studied. The simulation results indicate that series hybrid buses have the greatest overall advantage in fuel economy. The series and parallel hybrid buses were predicted to produce similar CO and HC tailpipe emissions but were also predicted to have reduced NOx tailpipe emissions compared to the conventional bus in higher speed cycles. For the New York bus cycle (NYBC), which has the lowestmore » average speed among the cycles evaluated, the series bus tailpipe emissions were somewhat higher than they were for the conventional bus, while the parallel hybrid bus had significantly lower tailpipe emissions. All three bus powertrains were found to require periodic active DPF regeneration to maintain PM control. Plug-in operation of series hybrid buses appears to offer significant fuel economy benefits and is easily employed due to the relatively large battery capacity that is typical of the series hybrid configuration.« less

Spatiotemporal variation of PM1 pollution in China

NASA Astrophysics Data System (ADS)

Chen, Gongbo; Morawska, Lidia; Zhang, Wenyi; Li, Shanshan; Cao, Wei; Ren, Hongyan; Wang, Boguang; Wang, Hao; Knibbs, Luke D.; Williams, Gail; Guo, Jianping; Guo, Yuming

2018-04-01

Understanding spatiotemporal variation of PM1 (mass concentrations of particles with aerodynamic diameter < 1 μm) is important due to its adverse effects on health, which is potentially more severe for its deeper penetrating capability into human bodies compared with larger particles. This study aimed to quantify the spatial and temporal distribution of PM1 across China as well as its ratio with PM2.5 (<2.5 μm) and relationships with meteorological parameters in order to deepen our knowledge of the drivers of air pollution in China. Ground-based monitoring PM1 and PM2.5 measurements, along with collocated meteorological data, were obtained from 96 stations in China for the period from November 2013 to December 2014. Generalized additive models were employed to examine the relationships between PM1 and meteorological parameters. We showed that PM1 concentrations were the lowest in summer and the highest in winter. Across China, the PM1/PM2.5 ratios ranged from 0.75-0.88, reaching higher levels in January and lower in August. For spatial distribution, higher PM1/PM2.5 ratios (>0.9) were observed in North-Eastern China, North China Plain, coastal areas of Eastern China and Sichuan Basin while lower ratios (<0.7) were present in remote areas in North-Western and Northern China (e.g., Xinjiang, Tibet and Inner Mongolia). Higher PM1/PM2.5 ratios were observed on heavily polluted days and lower ratios on clean days. The high PM1/PM2.5 ratios observed in China suggest that smaller particles, PM1 fraction, are key drivers of air pollution, and that they effectively account for the majority of PM2.5 concentrations. This emphasised the role of combustion process and secondary particle formation, the sources of PM1, and the significance of controlling them.

SCIENCE VERSION OF PM CHEMISTRY MODEL

EPA Science Inventory

PM chemistry models containing detailed treatments of key chemical processes controlling ambient concentrations of inorganic and organic compounds in PM2.5 are needed to develop strategies for reducing PM2.5 concentrations. This task, that builds on previous research conducted i...

Rotary ultrasonic machining of CFRP: A comparison with grinding.

PubMed

Ning, F D; Cong, W L; Pei, Z J; Treadwell, C

2016-03-01

Carbon fiber reinforced plastic (CFRP) composites have been intensively used in various industries due to their superior properties. In aircraft and aerospace industry, a large number of holes are required to be drilled into CFRP components at final stage for aircraft assembling. There are two major types of methods for hole making of CFRP composites in industry, twist drilling and its derived multi-points machining methods, and grinding and its related methods. The first type of methods are commonly used in hole making of CFRP composites. However, in recent years, rotary ultrasonic machining (RUM), a hybrid machining process combining ultrasonic machining and grinding, has also been successfully used in drilling of CFRP composites. It has been shown that RUM is superior to twist drilling in many aspects. However, there are no reported investigations on comparisons between RUM and grinding in drilling of CFRP. In this paper, these two drilling methods are compared in five aspects, including cutting force, torque, surface roughness, hole diameter, and material removal rate. Copyright © 2015 Elsevier B.V. All rights reserved.

Investigation of air pollution of Shanghai subway stations in ventilation seasons in terms of PM2.5 and PM10.

PubMed

Guo, Erbao; Shen, Henggen; He, Lei; Zhang, Jiawen

2017-07-01

In November 2015, the PM 2.5 and PM 10 particulate matter (PM) levels in platforms, station halls, and rail areas of the Shangcheng and Jiashan Road Station were monitored to investigate air pollution in the Shanghai subway system. The results revealed that in subway stations, PM 2.5 and PM 10 concentrations were significantly higher than those in outdoor environments. In addition, particle concentrations in the platforms exceeded maximum levels that domestic safety standards allowed. Particularly on clear days, PM 2.5 and PM 10 concentrations in platforms were significantly higher than maximum standards levels. Owing to the piston effect, consistent time-varying trends were exhibited by PM 2.5 concentrations in platforms, station halls, and rail areas. Platform particle concentrations were higher than the amount in station halls, and they were higher on clear days than on rainy days. The time-varying trends of PM 10 and PM 2.5 concentrations in platforms and station halls were similar to each other. Activities within the station led to most of the inhalable particles within the station area. The mass concentration ratios of PM 2.5 and PM 10 in platforms were within 0.65-0.93, and fine particles were the dominant components.

ANALYZE EXISTING DATA ON PM COMPOSITION TO IDENTIFY KEY FACTORS WHICH INFLUENCE HUMAN EXPOSURES TO PM CONSTITUENTS

EPA Science Inventory

An association has been demonstrated between ambient particulate matter (PM 2.5 and PM 10) concentrations and human morbidity/mortality. However, little is known regarding the most important sources of PM exposure, interpersonal and intrapersonal variability in exposure, and the...

Variability of levels and composition of PM10 and PM2.5 in the Barcelona metro system

NASA Astrophysics Data System (ADS)

Querol, X.; Moreno, T.; Karanasiou, A.; Reche, C.; Alastuey, A.; Viana, M.; Font, O.; Gil, J.; de Miguel, E.; Capdevila, M.

2012-06-01

From an environmental perspective, the underground metro system is one of the cleanest forms of public transportation in urban agglomerations. Current studies report contradicting results regarding air quality in the metro systems: whereas some reveal poor air quality, others report PM levels which are lower or of the same order of magnitude than those measured in traffic sites above ground level. The present work assesses summer and winter indoor air quality and passenger exposure in the Barcelona metro, focusing on PM levels and their metal contents. In addition, the impact on indoor air quality of platform screen door systems (automated systems consisting of closed rail track and platforms) is evaluated, to determine whether these systems reduce passenger exposure to PM when compared with conventional systems (open tracks and platforms). In the Barcelona metro PM levels inside the trains in summer are amongst the lowest reported for worldwide metro systems (11-32 μg m-3 PM2.5). This is most likely due to the air conditioning system working in all carriages of the Barcelona metro during the whole year. Levels were considerably higher on the platforms, reaching mean levels of 46 and 125 μg m3 in the new (L9) and old (L3) lines, respectively. PM10 data are also reported in the present study, but comparison with other metro systems is difficult due to the scarcity of data compared with PM2.5. Results showed distinct PM daily cycles, with a drastic increase from 06:00 to 07:00 a.m., a diurnal maximum from 07:00 to 10:00 p.m., and marked decrease between 10:00 p.m. and 05:00 a.m. The elements with the highest enrichment were those associated with wheel or brake abrasion products (Ba, Fe, Cu, Mn, Cr, Sb, As, Mo, Co, Sr, among others). Laminar hematite (Fe2O3) was the dominant particle type, being mainly originated by mechanical abrasion of the rail track and wheels. Regarding passenger exposure to PM, the contribution of commuting by metro was estimated to account

Basic statistics of PM2.5 and PM10 in the atmosphere of Mexico City.

PubMed

Vega, E; Reyes, E; Sánchez, G; Ortiz, E; Ruiz, M; Chow, J; Watson, J; Edgerton, S

2002-03-27

The high levels of fine particulate matter in Mexico City are of concern since they may induce severe public health effects as well as the attenuation of visible light. Sequential filter samplers were used at six different sites from 23 February to 22 March 1997. The sampling campaign was carried out as part of the project 'Investigación sobre Materia Particulada y Deterioro Atmosferico-Aerosol and Visibility Evaluation Research'. This research was a cooperative project sponsored by PEMEX and by the US Department of Energy. Sampling sites represent the different land uses along the city, the northwest station, Tlalnepantla, is located in a mixed medium income residential and industrial area. The northeast station, Xalostoc, is located in a highly industrialized area, Netzahualcoyotl is located in a mixed land use area, mainly commercial and residential. Station La Merced is located in the commercial and administrative district downtown. The southwest station is located in the Pedregal de San Angel, in a high-income neighborhood, and the southeast station located in Cerro de la Estrella is a mixed medium income residential and commercial area. Samples were collected four times a day in Cerro de la Estrella (CES), La Merced (MER) and Xalostoc (XAL) with sampling periods of 6 h. In Pedregal (PED), Tlalnepantla (TLA) and Netzahualcoyot1 (NEZ) sampling periods were every 24 h. In this paper the basic statistics of PM2.5 and PM10 mass concentrations are presented. The average results showed that 49, 61, 46, 57, 51 and 44% of the PM10 consisted of PM2.5 for CES, MER, XAL, PED, TLA and NEZ, respectively. The 24-h average highest concentrations of PM25 and PM10 were registered at NEZ (184 and 267 microg/m3) and the lowest at PED (22 and 39 microg/m3). The highest PM10 correlations were between XAL-CES (0.79), PED-TLA (0.80). In contrast, the highest PM2.5 correlations were between CES-PED (0.74), MER-CES (0.73) and TLA-PED (0.72), showing a lower correlation than the PM10

MARTI: man-machine animation real-time interface

NASA Astrophysics Data System (ADS)

Jones, Christian M.; Dlay, Satnam S.

1997-05-01

The research introduces MARTI (man-machine animation real-time interface) for the realization of natural human-machine interfacing. The system uses simple vocal sound-tracks of human speakers to provide lip synchronization of computer graphical facial models. We present novel research in a number of engineering disciplines, which include speech recognition, facial modeling, and computer animation. This interdisciplinary research utilizes the latest, hybrid connectionist/hidden Markov model, speech recognition system to provide very accurate phone recognition and timing for speaker independent continuous speech, and expands on knowledge from the animation industry in the development of accurate facial models and automated animation. The research has many real-world applications which include the provision of a highly accurate and 'natural' man-machine interface to assist user interactions with computer systems and communication with one other using human idiosyncrasies; a complete special effects and animation toolbox providing automatic lip synchronization without the normal constraints of head-sets, joysticks, and skilled animators; compression of video data to well below standard telecommunication channel bandwidth for video communications and multi-media systems; assisting speech training and aids for the handicapped; and facilitating player interaction for 'video gaming' and 'virtual worlds.' MARTI has introduced a new level of realism to man-machine interfacing and special effect animation which has been previously unseen.

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

PubMed Central

Sohaib, Muhammad; Kim, Cheol-Hong; Kim, Jong-Myon

2017-01-01

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs). PMID:29232908

Control Demonstration of Multiple Doubly-Fed Induction Motors for Hybrid Electric Propulsion

NASA Technical Reports Server (NTRS)

Sadey, David J.; Bodson, Marc; Csank, Jeffrey T.; Hunker, Keith R.; Theman, Casey J.; Taylor, Linda M.

2017-01-01

The Convergent Aeronautics Solutions (CAS) High Voltage-Hybrid Electric Propulsion (HVHEP) task was formulated to support the move into future hybrid-electric aircraft. The goal of this project is to develop a new AC power architecture to support the needs of higher efficiency and lower emissions. This proposed architecture will adopt the use of the doubly-fed induction machine (DFIM) for propulsor drive motor application.The Convergent Aeronautics Solutions (CAS) High Voltage-Hybrid Electric Propulsion (HVHEP) task was formulated to support the move into future hybrid-electric aircraft. The goal of this project is to develop a new AC power architecture to support the needs of higher efficiency and lower emissions. This proposed architecture will adopt the use of the doubly-fed induction machine (DFIM) for propulsor drive motor application. DFIMs are attractive for several reasons, including but not limited to the ability to self-start, ability to operate sub- and super-synchronously, and requiring only fractionally rated power converters on a per-unit basis depending on the required range of operation. The focus of this paper is based specifically on the presentation and analysis of a novel strategy which allows for independent operation of each of the aforementioned doubly-fed induction motors. This strategy includes synchronization, soft-start, and closed loop speed control of each motor as a means of controlling output thrust; be it concurrently or differentially. The demonstration of this strategy has recently been proven out on a low power test bed using fractional horsepower machines. Simulation and hardware test results are presented in the paper.

Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2 - Results of the ESCAPE project

NASA Astrophysics Data System (ADS)

Eeftens, Marloes; Tsai, Ming-Yi; Ampe, Christophe; Anwander, Bernhard; Beelen, Rob; Bellander, Tom; Cesaroni, Giulia; Cirach, Marta; Cyrys, Josef; de Hoogh, Kees; De Nazelle, Audrey; de Vocht, Frank; Declercq, Christophe; Dėdelė, Audrius; Eriksen, Kirsten; Galassi, Claudia; Gražulevičienė, Regina; Grivas, Georgios; Heinrich, Joachim; Hoffmann, Barbara; Iakovides, Minas; Ineichen, Alex; Katsouyanni, Klea; Korek, Michal; Krämer, Ursula; Kuhlbusch, Thomas; Lanki, Timo; Madsen, Christian; Meliefste, Kees; Mölter, Anna; Mosler, Gioia; Nieuwenhuijsen, Mark; Oldenwening, Marieke; Pennanen, Arto; Probst-Hensch, Nicole; Quass, Ulrich; Raaschou-Nielsen, Ole; Ranzi, Andrea; Stephanou, Euripides; Sugiri, Dorothee; Udvardy, Orsolya; Vaskövi, Éva; Weinmayr, Gudrun; Brunekreef, Bert; Hoek, Gerard

2012-12-01

The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates relationships between long-term exposure to outdoor air pollution and health using cohort studies across Europe. This paper analyses the spatial variation of PM2.5, PM2.5 absorbance, PM10 and PMcoarse concentrations between and within 20 study areas across Europe.We measured NO2, NOx, PM2.5, PM2.5 absorbance and PM10 between October 2008 and April 2011 using standardized methods. PMcoarse was determined as the difference between PM10 and PM2.5. In each of the twenty study areas, we selected twenty PM monitoring sites to represent the variability in important air quality predictors, including population density, traffic intensity and altitude. Each site was monitored over three 14-day periods spread over a year, using Harvard impactors. Results for each site were averaged after correcting for temporal variation using data obtained from a reference site, which was operated year-round.Substantial concentration differences were observed between and within study areas. Concentrations for all components were higher in Southern Europe than in Western and Northern Europe, but the pattern differed per component with the highest average PM2.5 concentrations found in Turin and the highest PMcoarse in Heraklion. Street/urban background concentration ratios for PMcoarse (mean ratio 1.42) were as large as for PM2.5 absorbance (mean ratio 1.38) and higher than those for PM2.5 (1.14) and PM10 (1.23), documenting the importance of non-tailpipe emissions. Correlations between components varied between areas, but were generally high between NO2 and PM2.5 absorbance (average R2 = 0.80). Correlations between PM2.5 and PMcoarse were lower (average R2 = 0.39). Despite high correlations, concentration ratios between components varied, e.g. the NO2/PM2.5 ratio varied between 0.67 and 3.06.In conclusion, substantial variability was found in spatial patterns of PM2.5, PM2.5 absorbance, PM10 and PMcoarse. The

Quantifying the decadal changes of PM2.5 over New York through a combination of satellite, model and in-situ measurements

NASA Astrophysics Data System (ADS)

Jin, X.; Fiore, A. M.; Curci, G.; Lyapustin, A.; Wang, Y.; Civerolo, K.; Ku, M.; van Donkelaar, A.; Martin, R.

2017-12-01

Ambient exposure to fine particulate matter (PM2.5) is one of the top global health concerns. Efforts have been made to regulate PM2.5 precursor emissions across the U.S.A, which are expected to mitigate the air pollution related health impacts. However, quantifying the health outcomes from emission controls requires robust estimates of PM2.5 exposures that accurately describe the spatial and temporal variability of PM2.5. Satellite remote sensing offers the potential to fill the gaps of the sparse, limited sampling of in situ measurement networks and is increasingly being used in health assessments. We provide new estimates of PM2.5 over New York State with 1 km spatial resolution that use Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD and a regional air quality model (CMAQ) to estimate the AOD-PM2.5 scaling factors. Next, we evaluate three major sources of uncertainties of satellite-derived PM2.5 data and their impacts on the derived decadal changes: 1) satellite retrieval of AOD, 2) optical properties of the particles, 3) relationships between the aerosol burden in the planetary boundary layer and full atmospheric column. Finally, we analyze the decadal changes of PM2.5 over New York State using the newly developed PM2.5 data, alongside four other PM2.5 estimates including satellite-derived PM2.5 developed by van Donkelaar et al. (2015), statistical land use regression developed by Beckerman et al. (2013), CMAQ simulations, and a Bayesian fusion of CMAQ and ground-based measurements. By evaluating the decadal changes of PM2.5 from multiple datasets over areas with dense (e.g. New York City area) and sparse ground-based measurements (e.g. upstate New York), we evaluate the extent to which satellite remote sensing could help better quantify the health outcomes of emission controls. References: Beckerman et al., (2013), A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM2.5 in the Contiguous United States, Environ. Sci

PM POPULATION EXPOSURE AND DOSE MODELS

EPA Science Inventory

The overall objective of this study is the development of a refined probabilistic exposure and dose model for particulate matter (PM) suitable for predicting PM10 and PM2.5 population exposures. This modeling research will be conducted both in-house by EPA scientists and through...

Auto-Relevancy Baseline: A Hybrid System Without Human Feedback

DTIC Science & Technology

2010-11-01

classical Bayes algorithm upon the pseudo-hybridization of SemanticA and Latent Semantic IndexingBC systems should smooth out historically high yet...black box emulated a machine learning topic expert. Similar to some Web methods, the initial topics within the legal document were expanded upon

Growth, extracellular alkaline phosphatase activity, and kinetic characteristic responses of the bloom-forming toxic cyanobacterium, Microcystis aeruginosa, to atmospheric particulate matter (PM2.5, PM2.5-10, and PM>10).

PubMed

Xu, Ziran; Wang, Shoubing; Wang, Yuanan; Zhang, Jie

2018-03-01

Atmospheric particulate matter (APM), commonly seen and widely excited in environment, appears great enough to influence the biochemical processes in aquatic microorganisms and phytoplankton. Understanding the response of cyanobacteria to various factors is fundamental for eutrophication control. To clarify the response of cyanobacteria to APM, the effects of PM 2.5 , PM 2.5-10 , and PM >10 on Microcystis aeruginosa were researched. Variabilities in cell density, chlorophyll a, soluble protein, malondialdehyde, extracellular activity, and kinetic parameters of alkaline phosphatase were evaluated by lab-cultured experiments. Results showed that the PM 2.5 had a slight stimulation impact on the growth and enhanced both of the 48- and 72-h extracellular alkaline phosphatase activity (APA), the affinity of alkaline phosphatase for substrate, and the 72-h maximum enzymatic reaction velocity (V max ). Moreover, the stimulations in extracellular APA and V max enhanced with the increasing exposure concentrations. We also found there were no obvious distinctions on the effects of growth and alkaline phosphatase in M. aeruginosa between PM 2.5-10 and PM >10 exposure groups. Obviously, inhibitory effects on growth existed in 4.0 and 8.0 mg/L PM 2.5-10 and 8.0 mg/L PM >10 at 120 h. Furthermore, PM 2.5-10 and PM >10 exerted inhibitory effects on the extracellular APA during the 72-h exposure. Simultaneously, the V max was notably inhibited and the affinity of alkaline phosphatase for substrate was more inseparable compared with control in PM 2.5-10 and PM >10 treatments. Nevertheless, the inhibitors in extracellular APA and kinetic parameters were unrelated to PM 2.5-10 and PM >10 exposure concentrations. Two-way ANOVA results revealed that there were significant interactions between exposure concentration and diameter of APM on the 120-h cell density, soluble protein content, APA, and 72 h APA of M. aeruginosa. These results in our study would be meaningful to further

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods

PubMed Central

Burlina, Philippe; Billings, Seth; Joshi, Neil

2017-01-01

Objective To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Methods Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and “engineered” features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. Results The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). Conclusions This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification. PMID:28854220

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

PubMed

Burlina, Philippe; Billings, Seth; Joshi, Neil; Albayda, Jemima

2017-01-01

To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

Batch Scheduling for Hybrid Assembly Differentiation Flow Shop to Minimize Total Actual Flow Time

NASA Astrophysics Data System (ADS)

Maulidya, R.; Suprayogi; Wangsaputra, R.; Halim, A. H.

2018-03-01

A hybrid assembly differentiation flow shop is a three-stage flow shop consisting of Machining, Assembly and Differentiation Stages and producing different types of products. In the machining stage, parts are processed in batches on different (unrelated) machines. In the assembly stage, each part of the different parts is assembled into an assembly product. Finally, the assembled products will further be processed into different types of final products in the differentiation stage. In this paper, we develop a batch scheduling model for a hybrid assembly differentiation flow shop to minimize the total actual flow time defined as the total times part spent in the shop floor from the arrival times until its due date. We also proposed a heuristic algorithm for solving the problems. The proposed algorithm is tested using a set of hypothetic data. The solution shows that the algorithm can solve the problems effectively.

Hybrid MEFPI/FBG sensor for simultaneous measurement of strain and magnetic field

NASA Astrophysics Data System (ADS)

Chen, Mao-qing; Zhao, Yong; Lv, Ri-qing; Xia, Feng

2017-12-01

A hybrid fiber-optic sensor consisting of a micro extrinsic Fabry-Perot Interferometer (MEFPI) and an etched fiber Bragg grating (FBG) is proposed, which can measure strain and magnetic field simultaneously. The etched FBG is sealed in a capillary with ferrofluids to detect the surrounding magnetic field. FBG with small diameter will be more sensitive to magnetic field is confirmed by simulation results. The MEFPI sensor that is prepared through welding a short section of hollow-core fiber (HCF) with single-mode fiber (SMF) is effective for strain detection. The experiment shows that strain and magnetic field can be successfully simultaneously detected based on hybrid MEFPI/FBG sensor. The sensitivities of the strain and magnetic field intensity are measured to be up to 1.41 pm/με and 5.11 pm/mT respectively. There is a negligible effect on each other, hence simultaneously measuring strain and magnetic field is feasible. It is anticipated that such easy preparation, compact and low-cost fiber-optic sensors for simultaneous measurement of strain and magnetic field could find important applications in practice.

Investigation of fault modes in permanent magnet synchronous machines for traction applications

NASA Astrophysics Data System (ADS)

Choi, Gilsu

minimizing the demagnetization risks while examining corresponding trade-offs. Two PM machines have been tested to validate the predicted fault currents and braking torque as well as demagnetization risks in PMSM drives. The generality and scalability of key results have also been demonstrated by analyzing several PM machines with a variety of stator, rotor, and winding configurations for various power ratings.

Elemental characterization and source apportionment of PM10 and PM2.5 in the western coastal area of central Taiwan.

PubMed

Hsu, Chin-Yu; Chiang, Hung-Che; Lin, Sheng-Lun; Chen, Mu-Jean; Lin, Tzu-Yu; Chen, Yu-Cheng

2016-01-15

This study investigated seasonal variations in PM10 and PM2.5 mass and associated trace metal concentrations in a residential area in proximity to the crude oil refinery plants and industrial parks of central Taiwan. Particle measurements were conducted during winter, spring and summer in 2013 and 2014. Twenty-six trace metals in PM10 and PM2.5 were analyzed using ICP-MS. Multiple approaches of the backward trajectory model, enrichment factor (EF), Lanthanum enrichment and positive matrix fraction (PMF) were used to identify potential sources of particulate metals. Mean concentrations of PM10 in winter, spring and summer were 76.4 ± 22.6, 33.2 ± 9.9 and 37.4 ± 17.0 μg m(-3), respectively, while mean levels of PM2.5 in winter, spring and summer were 47.8 ± 20.0, 23.9 ± 11.2 and 16.3 ± 8.2 μg m(-3), respectively. The concentrations of carcinogenic metals (Ni, As and adjusted Cr(VI)) in PM10 and PM2.5 exceeded the guideline limits published by WHO. The result of EF analysis confirmed that Mo, Sb, Cd, Zn, Mg, Cr, As, Pb, Cu, Ni and V were attributable to anthropogenic emission. PMF analysis demonstrated that trace metals in PM10 and PM2.5 were from the similar sources, such as coal combustion, oil combustion and traffic-related emission, except for soil dust and crustal element emissions only observed in PM10 and secondary aluminum smelter only observed in PM2.5. Considering health-related particulate metals, the traffic-related emission and coal combustion for PM10 and PM2.5, respectively, are important to control for reducing potential carcinogenic risk. The results could aid efforts to clarify the impact of source-specific origins on human health. Copyright © 2015 Elsevier B.V. All rights reserved.

EVALUATION OF THE SMPS-APS SYSTEM AS A CONTINUOUS MONITOR FOR MEASURING PM2.5, PM10 AND COARSE (PM2.5-10) CONCENTRATIONS. (R827352C011)

EPA Science Inventory

Respirable particulate matter (PM) has been linked to mortality and morbidity by a variety of epidemiological studies. This research has led to the creation of a new PM standard for particles with diameters <2.5 μm (PM_2.5). Since the conclusion of these studie...

Source apportionment of PM10 and PM2.5 air pollution, and possible impacts of study characteristics in South Korea.

PubMed

Ryou, Hyoung Gon; Heo, Jongbae; Kim, Sun-Young

2018-09-01

Studies of source apportionment (SA) for particulate matter (PM) air pollution have enhanced understanding of dominant pollution sources and quantification of their contribution. Although there have been many SA studies in South Korea over the last two decades, few studies provided an integrated understanding of PM sources nationwide. The aim of this study was to summarize findings of PM SA studies of South Korea and to explore study characteristics. We selected studies that estimated sources of PM 10 and PM 2.5 performed for 2000-2017 in South Korea using Positive Matrix Factorization and Chemical Mass Balance. We reclassified the original PM sources identified in each study into seven categories: motor vehicle, secondary aerosol, soil dust, biomass/field burning, combustion/industry, natural source, and others. These seven source categories were summarized by using frequency and contribution across four regions, defined by northwest, west, southeast, and southwest regions, by PM 10 and PM 2.5 . We also computed the population-weighted mean contribution of each source category. In addition, we compared study features including sampling design, sampling and lab analysis methods, chemical components, and the inclusion of Asian dust days. In the 21 selected studies, all six PM 10 studies identified motor vehicle, soil dust, and combustion/industry, while all 15 PM 2.5 studies identified motor vehicle and soil dust. Different from the frequency, secondary aerosol produced a large contribution to both PM 10 and PM 2.5 . Motor vehicle contributed highly to both, whereas the contribution of combustion/industry was high for PM 10 . The population-weighted mean contribution was the highest for the motor vehicle and secondary aerosol sources for both PM10 and PM2.5. However, these results were based on different subsets of chemical speciation data collected at a single sampling site, commonly in metropolitan areas, with short overlap and measured by different lab analysis

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation.

PubMed

Sadat, Md Nazmus; Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-03-05

Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. ©Md Nazmus Sadat, Xiaoqian Jiang, Md Momin Al Aziz, Shuang Wang, Noman Mohammed. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.03.2018.

Inhalable Microorganisms in Beijing’s PM2.5 and PM10 Pollutants during a Severe Smog Event

PubMed Central

2014-01-01

Particulate matter (PM) air pollution poses a formidable public health threat to the city of Beijing. Among the various hazards of PM pollutants, microorganisms in PM2.5 and PM10 are thought to be responsible for various allergies and for the spread of respiratory diseases. While the physical and chemical properties of PM pollutants have been extensively studied, much less is known about the inhalable microorganisms. Most existing data on airborne microbial communities using 16S or 18S rRNA gene sequencing to categorize bacteria or fungi into the family or genus levels do not provide information on their allergenic and pathogenic potentials. Here we employed metagenomic methods to analyze the microbial composition of Beijing’s PM pollutants during a severe January smog event. We show that with sufficient sequencing depth, airborne microbes including bacteria, archaea, fungi, and dsDNA viruses can be identified at the species level. Our results suggested that the majority of the inhalable microorganisms were soil-associated and nonpathogenic to human. Nevertheless, the sequences of several respiratory microbial allergens and pathogens were identified and their relative abundance appeared to have increased with increased concentrations of PM pollution. Our findings may serve as an important reference for environmental scientists, health workers, and city planners. PMID:24456276

Trends in arsenic levels in PM10 and PM 2.5 aerosol fractions in an industrialized area.

PubMed

García-Aleix, J R; Delgado-Saborit, J M; Verdú-Martín, G; Amigó-Descarrega, J M; Esteve-Cano, V

2014-01-01

Arsenic is a toxic element that affects human health and is widely distributed in the environment. In the area of study, the main Spanish and second largest European industrial ceramic cluster, the main source of arsenic aerosol is related to the impurities in some boracic minerals used in the ceramic process. Epidemiological studies on cancer occurrence in Spain points out the study region as one with the greater risk of cancer. Concentrations of particulate matter and arsenic content in PM10 and PM2.5 were measured and characterized by ICP-MS in the area of study during the years 2005-2010. Concentrations of PM10 and its arsenic content range from 27 to 46 μg/m(3) and from 0.7 to 6 ng/m(3) in the industrial area, respectively, and from 25 to 40 μg/m(3) and from 0.7 to 2.8 ng/m(3) in the urban area, respectively. Concentrations of PM2.5 and its arsenic content range from 12 to 14 μg/m(3) and from 0.5 to 1.4 ng/m(3) in the urban background area, respectively. Most of the arsenic content is present in the fine fraction, with ratios of PM2.5/PM10 in the range of 0.65-0.87. PM10, PM2.5, and its arsenic content show a sharp decrease in recent years associated with the economic downturn, which severely hit the production of ceramic materials in the area under study. The sharp production decrease due to the economic crisis combined with several technological improvements in recent years such as substitution of boron, which contains As impurities as raw material, have reduced the concentrations of PM10, PM2.5, and As in air to an extent that currently meets the existing European regulations.

Influence of Stacking Sequence and Notch Angle on the Charpy Impact Behavior of Hybrid Composites

NASA Astrophysics Data System (ADS)

Behnia, S.; Daghigh, V.; Nikbin, K.; Fereidoon, A.; Ghorbani, J.

2016-09-01

The low-velocity impact behavior of hybrid composite laminates was investigated. The epoxy matrix was reinforced with aramid, glass, basalt, and carbon fabrics using the hand lay-up technique. Different stacking sequences and notch angles were and notch angles considered and tested using a Charpy impact testing machine to study the hybridization and notch angle effects on the impact response of the hybrid composites. The energy absorption capability of specimens with different stacking sequences and notch angles is compared and discussed. It is shown that the hybridization can enhance the mechanical performance of composite materials.

Hypothermic machine perfusion permits extended cold ischemia times with improved early graft function.

PubMed

Guy, Alison; McGrogan, Damian; Inston, Nicholas; Ready, Andrew

2015-04-01

The logistics of deceased-donor renal transplants are largely affected by cold ischemia time. However, to attain successful outcomes, other issues must be considered. Extending cold ischemia time to accommodate these issues would be valuable. We investigated the role of hypothermic machine perfusion to extend cold ischaemia time. Deceased-donor kidneys were allocated to a storage method, depending on predicted time to operation. Kidneys to be transplanted from 8:00 AM to 8:00 PM in the transplant room remained in static cold storage. If predicted operating time was out of hours, the kidney was transferred to hypothermic machine perfusion and transplanted at the earliest opportunity on the dedicated transplant list. There were 74 kidneys transplanted from hypothermic machine perfusion and 101 kidneys from static cold storage. Median cold ischemia time was 23.85 hours in the hypothermic machine perfusion group, compared with 13 hours in the static cold storage group (P ≤ .0001). There were 20 kidneys (27%) from hypothermic machine perfusion that had delayed graft function, compared with 47 kidneys (47%) in the static cold storage group (P = .012). There were no other significant differences in graft or postoperative complications. This study demonstrated that improved early graft outcomes can be achieved following longer cold ischemia time by using hypothermic machine perfusion rather than static cold storage. This effect is likely multifactorial including the inherent effects of hypothermic machine perfusion, improved recipient preparation, and possibly better perioperative conditions.

Non-exhaust PM emissions from electric vehicles

NASA Astrophysics Data System (ADS)

Timmers, Victor R. J. H.; Achten, Peter A. J.

2016-06-01

Particulate matter (PM) exposure has been linked to adverse health effects by numerous studies. Therefore, governments have been heavily incentivising the market to switch to electric passenger cars in order to reduce air pollution. However, this literature review suggests that electric vehicles may not reduce levels of PM as much as expected, because of their relatively high weight. By analysing the existing literature on non-exhaust emissions of different vehicle categories, this review found that there is a positive relationship between weight and non-exhaust PM emission factors. In addition, electric vehicles (EVs) were found to be 24% heavier than equivalent internal combustion engine vehicles (ICEVs). As a result, total PM10 emissions from EVs were found to be equal to those of modern ICEVs. PM2.5 emissions were only 1-3% lower for EVs compared to modern ICEVs. Therefore, it could be concluded that the increased popularity of electric vehicles will likely not have a great effect on PM levels. Non-exhaust emissions already account for over 90% of PM10 and 85% of PM2.5 emissions from traffic. These proportions will continue to increase as exhaust standards improve and average vehicle weight increases. Future policy should consequently focus on setting standards for non-exhaust emissions and encouraging weight reduction of all vehicles to significantly reduce PM emissions from traffic.

Characterisation of PM(10), PM(2.5) and benzene soluble organic fraction of particulate matter in an urban area of Kolkata, India.

PubMed

Gupta, A K; Nag, Subhankar; Mukhopadhyay, U K

2006-04-01

In this study, the relationship between inhalable particulate (PM(10)), fine particulate (PM(2.5)), coarse particles (PM(2.5 - 10)) and meteorological parameters such as temperature, relative humidity, solar radiation, wind speed were statistically analyzed and modelled for urban area of Kolkata during winter months of 2003-2004. Ambient air quality was monitored with a sampling frequency of twenty-four hours at three monitoring sites located near traffic intersections and in an industrial area. The monitoring sites were located 3-5 m above ground near highly trafficked and congested areas. The 24 h average PM(10) and PM(2.5) samples were collected using Thermo-Andersen high volume samplers and exposed filter papers were extracted and analysed for benzene soluble organic fraction. The ratios between PM(2.5) and PM(10) were found to be in the range of 0.6 to 0.92 and the highest ratio was found in the most polluted urban site. Statistical analysis has shown a strong positive correlation between PM(10) and PM(2.5) and inverse correlation was observed between particulate matter (PM(10) and PM(2.5)) and wind speed. Statistical analysis of air quality data shows that PM(10) and PM(2.5) are showing poor correlation with temperature, relative humidity and solar radiation. Regression equations for PM(10) and PM(2.5) and meteorological parameters were developed. The organic fraction of particulate matter soluble in benzene is an indication of poly aromatic hydrocarbon (PAH) concentration present in particulate matter. The relationship between the benzene soluble organic fraction (BSOF) of inhalable particulate (PM(10)) and fine particulate (PM(2.5)) were analysed for urban area of Kolkata. Significant positive correlation was observed between benzene soluble organic fraction of PM(10) (BSM10) and benzene soluble organic fraction of PM(2.5) (BSM2.5). Regression equations for BSM10 and BSM2.5 were developed.

Mass concentration and elemental composition of indoor PM 2.5 and PM 10 in University rooms in Thessaloniki, northern Greece

NASA Astrophysics Data System (ADS)

Gemenetzis, Panagiotis; Moussas, Panagiotis; Arditsoglou, Anastasia; Samara, Constantini

The mass concentration and the elemental composition of PM 2.5 and PM 10 were measured in 40 rooms (mainly offices or mixed office-lab rooms, and photocopying places) of the Aristotle University of Thessaloniki, northern Greece. A total of 27 major, minor and trace elements were determined by ED-XRF analysis. The PM 2.5/PM 10 concentration ratios averaged 0.8±0.2, while the corresponding elemental ratios ranged between 0.4±0.2 and 0.9±0.2. The concentrations of PM 2.5 and PM 10 were significantly higher (by 70% and 50%, respectively) in the smokers' rooms compared to the non-smokers' places. The total elemental concentrations were also higher in the smokers' rooms (11.5 vs 8.2 μg m -3 for PM 2.5, and 10.3 vs 7.6 μg m -3 for PM 2.5-10). Fine particle concentrations (PM 2.5) were found to be quite proportional to smoking strength. On the contrary, the two environments exhibited similar coarse (PM 2.5-10) particle fractions not related to the number of cigarettes smoked. A slight decrease of particle concentrations with increasing the floor level was also observed, particularly for PM 2.5, suggesting that high-level floors are less impacted by near ground-level sources like traffic emissions. Finally, the removal efficiency of air purification systems was evaluated.

PM composition and source reconciliation in Mexico City

NASA Astrophysics Data System (ADS)

Mugica, V.; Ortiz, E.; Molina, L.; De Vizcaya-Ruiz, A.; Nebot, A.; Quintana, R.; Aguilar, J.; Alcántara, E.

PM 2.5 and PM 10 were collected during 24-h sampling intervals from March 1st to 31st, 2006 during the MILAGRO campaign carried out in Mexico City's northern region, in order to determine their chemical composition, oxidative activity and the estimation of the source contributions during the sampling period by means of the chemical mass balance (CMB) receptor model. PM 2.5 concentrations ranged from 32 to 70 μg m -3 while that of PM10 did so from 51 to 132 μg m -3. The most abundant chemical species for both PM fractions were: OC, EC, SO 42-, NO 3-, NH 4+, Si, Fe and Ca. The majority of the PM mass was comprised of carbon, up to about 52% and 30% of the PM2.5 and PM10, respectively. PM2.5 constituted more than 50% of PM10. The redox activity, assessed by the dithiothreitol (DTT) assay, was greater for PM 2.5 than for PM 10, and did not display significant differences during the sampling period. The PM 2.5 source reconciliation showed that in average, vehicle exhaust emissions were its most important source in an urban site with a 42% contribution, followed by re-suspended dust with 26%, secondary inorganic aerosols with 11%, and industrial emissions and food cooking with 10% each. These results had a good agreement with the Emission Inventory. In average, the greater mass concentration occurred during O 3S that corresponds to a wind shift initially with transport to the South but moving back to the North. Taken together these results show that PM chemical composition, oxidative potential, and source contribution is influenced by the meteorological conditions.

Trends and variability of atmospheric PM2.5 and PM10-2.5 concentration in the Po Valley, Italy

NASA Astrophysics Data System (ADS)

Bigi, Alessandro; Ghermandi, Grazia

2016-12-01

The Po Valley is one of the largest European regions with a remarkably high concentration level of atmospheric pollutants, both for particulate and gaseous compounds. In the last decade stringent regulations on air quality standards and on anthropogenic emissions have been set by the European Commission, including also for PM2.5 and its main components since 2008. These regulations have led to an overall improvement in air quality across Europe, including the Po Valley and specifically PM10, as shown in a previous study by Bigi and Ghermandi (2014). In order to assess the trend and variability in PM2.5 in the Po Valley and its role in the decrease in PM10, we analysed daily gravimetric equivalent concentration of PM2.5 and of PM10-2.5 at 44 and 15 sites respectively across the Po Valley. The duration of the times series investigated in this work ranges from 7 to 10 years. For both PM sizes, the trend in deseasonalized monthly means, annual quantiles and in monthly frequency distribution was estimated: this showed a significant decreasing trend at several sites for both size fractions and mostly occurring in winter. All series were tested for a significant weekly periodicity (a proxy to estimate the impact of primary anthropogenic emissions), yielding positive results for summer PM2.5 and for summer and winter PM10-2.5. Hierarchical cluster analysis showed moderate variability in PM2.5 across the valley, with two to three main clusters, dividing the area in western, eastern and southern/Apennines foothill sectors. The trend in atmospheric concentration was compared with the time series of local emissions, vehicular fleet details and fuel sales, suggesting that the decrease in PM2.5 and in PM10 originates from a drop both in primary and in precursors of secondary inorganic aerosol emissions, largely ascribed to vehicular traffic. Potentially, the increase in biomass burning emissions in winter and the modest decrease in NH3 weaken an otherwise even larger drop in

16,000-rpm Interior Permanent Magnet Reluctance Machine with Brushless Field Excitation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hsu, J.S.; Burress, T.A.; Lee, S.T.

2007-10-31

The reluctance interior permanent magnet (RIPM) motor is currently used by many leading auto manufacturers for hybrid vehicles. The power density for this type of motor is high compared with that of induction motors and switched reluctance motors. The primary drawback of the RIPM motor is the permanent magnet (PM) because during high-speed operation, the fixed PM produces a huge back electromotive force (emf) that must be reduced before the current will pass through the stator windings. This reduction in back-emf is accomplished with a significant direct-axis (d-axis) demagnetization current, which opposes the PM's flux to reduce the flux seenmore » by the stator wires. This may lower the power factor and efficiency of the motor and raise the requirement on the alternate current (ac) power supply; consequently, bigger inverter switching components, thicker motor winding conductors, and heavier cables are required. The direct current (dc) link capacitor is also affected when it must accommodate heavier harmonic currents. It is commonly agreed that, for synchronous machines, the power factor can be optimized by varying the field excitation to minimize the current. The field produced by the PM is fixed and cannot be adjusted. What can be adjusted is reactive current to the d-axis of the stator winding, which consumes reactive power but does not always help to improve the power factor. The objective of this project is to avoid the primary drawbacks of the RIPM motor by introducing brushless field excitation (BFE). This offers both high torque per ampere (A) per core length at low speed by using flux, which is enhanced by increasing current to a fixed excitation coil, and flux, which is weakened at high speed by reducing current to the excitation coil. If field weakening is used, the dc/dc boost converter used in a conventional RIPM motor may be eliminated to reduce system costs. However, BFE supports a drive system with a dc/dc boost converter, because it can further

The direct influence of ship traffic on atmospheric PM2.5, PM10 and PAH in Venice.

PubMed

Contini, D; Gambaro, A; Belosi, F; De Pieri, S; Cairns, W R L; Donateo, A; Zanotto, E; Citron, M

2011-09-01

The direct influence of ship traffic on atmospheric levels of coarse and fine particulate matter (PM(2.5), PM(10)) and fifteen polycyclic aromatic hydrocarbons (PAHs) has been estimated in the urban area of Venice. Data analysis has been performed on results collected at three sites over the summer, when ship traffic is at a maximum. Results indicate that monitoring of the PM daily concentrations is not sufficiently detailed for the evaluation of this contribution, even though it could be useful for specific markers such as PAHs. Therefore a new methodology, based on high temporal resolution measurements coupled with wind direction information and the database of ship passages of the Harbour Authority of Venice has been developed. The sampling sites were monitored with optical detectors (DustTrack(®) and Mie pDR-1200) operating at a high temporal resolution (20s and 1s respectively) for PM(2.5) and PM(10). PAH in the particulate and gas phases were recovered from quartz fibre filters and polyurethane foam plugs using pressurised solvent extraction, the extracts were then analysed by gas chromatography- high-resolution mass spectrometry. Our results shows that the direct contribution of ships traffic to PAHs in the gas phase is 10% while the contribution to PM(2.5) and to PM(10) is from 1% up to 8%. Copyright © 2011 Elsevier Ltd. All rights reserved.

US Japan Workshop. Hybrid 2000 Conference Held in Ithaca, New York on May 7-12, 2000

DTIC Science & Technology

2001-05-31

Illinois University) 10:30-10:45 BREAK 10:45-11:30 "Nano-Objects With Controlled Shape, Size and Composition From Block Copolymer-Ceramic Hybrid...Alkoxysilanes", Yoshiyuki Sugahara (Waseda University) 12:30 depart for Corning Glass Museum Tour Box lunches provided by Ramada Inn 5:30PM drop off at MV...well-controlled. If the size of the clusters is too large, the hybrids would be brittle as Si02 gels and glasses are. Thus, the control over the

Rotor apparatus for high strength undiffused brushless electric machine

DOEpatents

Hsu, John S [Oak Ridge, TN

2006-01-24

A radial gap brushless electric machine (30) having a stator (31) and a rotor (32) and a main air gap (34) also has at least one stationary excitation coil (35a, 36a) separated from the rotor (32) by a secondary air gap (35e, 35f, 36e, 36f) so as to induce a secondary flux in the rotor (32) which controls a resultant flux in the main air gap (34). Permanent magnetic (PM) material (38) is disposed in spaces between the rotor pole portions (39) to inhibit the second flux from leaking from the pole portions (39) prior to reaching the main air gap (34). By selecting the direction of current in the stationary excitation coil (35a, 36a) both flux enhancement and flux weakening are provided for the main air gap (34). Improvements of a laminated rotor, an end pole structure, and an arrangement of the PM elements for providing an arrangement of the flux paths from the auxiliary field coil assemblies are also disclosed.

Hardware Photos: Image Showing JWST Engineering Demonstration Mirror, Mounted Ready for Machining at AXYS and Image Showing HIP Can Containing Light Mirrors 1 and 2 Ready for Mirror Fabrication

NASA Technical Reports Server (NTRS)

OKeefe, Sean

2004-01-01

The images in this viewgraph presentation have the following captions: 1) EDU mirror after being sawed in half; 2) EDU Delivered to Axsys; 3) Be EDU Blank Received and Machining Started; 4) Loaded HIP can for flight PM segments 1 and 2; 5) Flight Blanks 1 and 2 Loaded into HIP Can at Brush-Wellman; 6) EDU in Machining at Axsys.

Wetting properties of hybrid structure with hydrophilic ridges and hydrophobic channels

NASA Astrophysics Data System (ADS)

Lee, Dong-Ki; Choi, Su Young; Park, Min Soo; Cho, Young Hak

2018-02-01

In the present study, we fabricated a hybrid structure where the upper surface of the ridge is hydrophilic and the inner surface of the channel is hydrophobic. Laser-induced backside wet etching (LIBWE) process was performed to machine the hybrid structure on a Pyrex glass substrate. Wetting properties were evaluated from static contact angles (CAs) measurement in parallel and orthogonal directions. The water droplet on the hybrid structure was in the Cassie-Baxter state and showed anisotropic wetting property along groove lines. Moisture condensation studies under humid condition indicated that water droplets grew and coalesced on the ridge with hydrophilicity. Furthermore, water-oil separation was tested using a microfluidic chip with the developed hybrid structure. In case of hybrid microfluidic chip, the water could not flow into channel but the hexadecane could flow due to the capillary pressure difference.

Source Identification of PM2.5 in Steubenville, Ohio Using a Hybrid Method for Highly Time-resolved Data

EPA Science Inventory

A new source-type identification method, Reduction and Species Clustering Using Episodes (ReSCUE), was developed to exploit the temporal synchronicity between species to form clusters of species that vary together. High time-resolution (30 min) PM_2.5 sampling was condu...

Exact analytical modeling of magnetic vector potential in surface inset permanent magnet DC machines considering magnet segmentation

NASA Astrophysics Data System (ADS)

Jabbari, Ali

2018-01-01

Surface inset permanent magnet DC machine can be used as an alternative in automation systems due to their high efficiency and robustness. Magnet segmentation is a common technique in order to mitigate pulsating torque components in permanent magnet machines. An accurate computation of air-gap magnetic field distribution is necessary in order to calculate machine performance. An exact analytical method for magnetic vector potential calculation in surface inset permanent magnet machines considering magnet segmentation has been proposed in this paper. The analytical method is based on the resolution of Laplace and Poisson equations as well as Maxwell equation in polar coordinate by using sub-domain method. One of the main contributions of the paper is to derive an expression for the magnetic vector potential in the segmented PM region by using hyperbolic functions. The developed method is applied on the performance computation of two prototype surface inset magnet segmented motors with open circuit and on load conditions. The results of these models are validated through FEM method.

40 CFR 1065.290 - PM gravimetric balance.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 34 2012-07-01 2012-07-01 false PM gravimetric balance. 1065.290 Section 1065.290 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR POLLUTION CONTROLS ENGINE-TESTING PROCEDURES Measurement Instruments Pm Measurements § 1065.290 PM gravimetric...

40 CFR 1065.290 - PM gravimetric balance.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 33 2014-07-01 2014-07-01 false PM gravimetric balance. 1065.290 Section 1065.290 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR POLLUTION CONTROLS ENGINE-TESTING PROCEDURES Measurement Instruments Pm Measurements § 1065.290 PM gravimetric...

40 CFR 1065.290 - PM gravimetric balance.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 34 2013-07-01 2013-07-01 false PM gravimetric balance. 1065.290 Section 1065.290 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR POLLUTION CONTROLS ENGINE-TESTING PROCEDURES Measurement Instruments Pm Measurements § 1065.290 PM gravimetric...

Characterization of carbonaceous materials in PM2.5 and PM10 size fractions in Morogoro, Tanzania, during 2006 wet season campaign

NASA Astrophysics Data System (ADS)

Mkoma, Stelyus L.; Chi, Xuguang; Maenhaut, Willy

2010-05-01

Atmospheric aerosol samples in PM10 and PM2.5 size fractions were collected in parallel at a rural site in Morogoro during wet season in March and April 2006. All samples were analysed for the particulate matter mass, for organic, elemental, and total carbon (OC, EC, and TC), and for water-soluble OC (WSOC). The average PM10 and PM2.5 mass concentrations and associated standard deviations were 14 ± 13 μg/m 3 and 7.3 ± 4 μg/m 3 respectively. On average, TC accounted for 33% of the PM10 mass and 44% of the PM2.5 mass for the campaign. The average OC/PM percentage ratios were 27% and 33% in PM10 and PM2.5 size fractions respectively and a larger fraction of the OC was water-soluble. The observed low EC/TC mean percentage ratios of 10-14% respectively for PM10 and PM2.5 fractions indicate that the carbonaceous aerosol originates mainly from biogenic aerosols and/or biomass burning. A simple source apportionment approach was used to apportion the OC to biofuel and charcoal burning. On average, 93% of the PM10 OC was attributed to biofuel and 7% to charcoal burning in the 2006 wet season campaign. However, it is suggested that a contribution to the OC at Morogoro could also come from other natural biogenic matter, and/or biomass burning aerosols. The results for the sources of OC at Morogoro should therefore be considered with great caution.

Identification of dicarboxylic acids and aldehydes of PM10 and PM2.5 aerosols in Nanjing, China

NASA Astrophysics Data System (ADS)

Wang, Gehui; Niu, Sulian; Liu, Caie; Wang, Liansheng

In this study aerosol samples of PM10 and PM2.5 collected from 18 February 2001 to 1 May 2001 in Nanjing, China were analyzed for their water-soluble organic compounds. A series of homologous dicarboxylic acids (C 2-10) and two kinds of aldehydes (methylglyoxal and 2-oxo-malonaldehyde) were detected by GC and GC/MS. Among the identified compounds, the concentration of oxalic acid was the highest at all the five sites, which ranged from 178 to 1423 ng/m 3. The second highest concentration of dicarboxylic acids were malonic and succinic acids, which ranged from 26.9 to 243 ng/m 3. Higher level of azelaic acid was also observed, of which the maximum was 301 ng/m 3. As the highest fraction of dicarboxylic acids, oxalic acid comprised from 28% to 86% of total dicarboxylic acids in PM10 and from 41% to 65% of total dicarboxylic acids in PM2.5. The dicarboxylic acids (C 2, C 3, C 4) together accounted for 38-95% of total dicarboxylic acids in PM10 and 59-87% of dicarboxylic acids in PM2.5. In this study, the total dicarboxylic acids accounted for 2.8-7.9% of total organic carbon (TOC) of water-soluble matters for PM10 and 3.4-11.8% of TOC for PM2.5. All dicarboxylic acids detected in this study together accounted for about 1% of particle mass. The concentration of azelaic acid was higher at one site than others, which may be resulted from higher level of volatile fat used for cooking. The amounts of dicarboxyic acids (C 2,3,4,9) and 2-oxo-malonaldehyde of PM2.5 were higher in winter and lower in spring. Compared with other major metropolitans in the world, the level of oxalic acid concentration of Nanjing is much higher, which may be contributed to higher level of particle loadings, especially for fine particles.

[Preliminary study of source apportionment of PM10 and PM2.5 in three cities of China during spring].

PubMed

Gao, Shen; Pan, Xiao-chuan; Madaniyazi, Li-na; Xie, Juan; He, Ya-hui

2013-09-01

To study source apportionment of atmospheric PM10 (particle matter ≤ 10 µm in aerodynamic diameter) and PM2.5 (particle matter ≤ 2.5 µm in aerodynamic diameter) in Beijing,Urumqi and Qingdao, China. The atmospheric particle samples of PM10 and PM2.5 collected from Beijing between May 17th and June 18th, 2005, from Urumqi between April 20th and June 1st, 2006 and from Qingdao between April 4th and May 15th, 2005, were detected to trace the source apportionment by factor analysis and enrichment factor methods. In Beijing, the source apportionment results derived from factor analysis model for PM10 were construction dust and soil sand dust (contributing rate of variance at 45.35%), industry dust, coal-combusted smoke and vehicle emissions (contributing rate at 31.83%), and biomass burning dust (13.57%). The main pollution element was Pb, while the content (median (minimum value-maximum value)was 0.216 (0.040-0.795) µg/m(3)) . As for PM2.5, the sources were construction dust and soil sand dust (38.86%), industry dust, coal-combusted smoke and vehicle emissions (25.73%), biomass burning dust (13.10%) and burning oil dust (11.92%). The main pollution element was Zn (0.365(0.126-0.808) µg/m(3)).In Urumqi, source apportionment results for PM10 were soil sand dust and coal-combusted dust(49.75%), industry dust, vehicle emissions and secondary particles dust (30.65%). The main characteristic pollution element was Cd (0.463(0.033-1.351) ng/m(3)). As for PM2.5, the sources were soil sand dust and coal-combusted dust (43.26%), secondary particles dust (22.29%), industry dust and vehicle emissions (20.50%). The main characteristic pollution element was As (14.599 (1.696-36.741) µg/m(3)).In Qingdao, source apportionment results for PM10 were construction dust (30.91%), vehicle emissions and industry dust (29.65%) and secondary particles dust (28.99%). The main characteristic pollution element was Pb (64.071 (5.846-346.831) µg/m(3)). As for PM2.5, the sources were

Dynamic force profile in hydraulic hybrid vehicles: a numerical investigation

NASA Astrophysics Data System (ADS)

Mohaghegh-Motlagh, Amin; Elahinia, Mohammad H.

2010-04-01

A hybrid hydraulic vehicle (HHV) combines a hydraulic sub-system with the conventional drivetrain in order to improve fuel economy for heavy vehicles. The added hydraulic module manages the storage and release of fluid power necessary to assist the motion of the vehicle. The power collected by a pump/motor (P/M) from the regenerative braking phase is stored in a high-pressure accumulator and then released by the P/M to the driveshaft during the acceleration phase. This technology is effective in significantly improving fuel-economy for heavy-class vehicles with frequent stop-and-go drive schedules. Despite improved fuel economy and higher vehicle acceleration, noise and vibrations are one of the main problems of these vehicles. The dual function P/Ms are the main source of noise and vibration in a HHV. This study investigates the dynamics of a P/M and particularly the profile and frequency-dependence of the dynamic forces generated by a bent-axis P/M unit. To this end, the fluid dynamics side of the problem has been simplified for investigating the system from a dynamics perspective. A mathematical model of a bent axis P/M has been developed to investigate the cause of vibration and noise in HHVs. The forces are calculated in time and frequency domains. The results of this work can be used to study the vibration response of the chassis and to design effective vibration isolation systems for HHVs.

UVSiPM: A light detector instrument based on a SiPM sensor working in single photon counting

NASA Astrophysics Data System (ADS)

Sottile, G.; Russo, F.; Agnetta, G.; Belluso, M.; Billotta, S.; Biondo, B.; Bonanno, G.; Catalano, O.; Giarrusso, S.; Grillo, A.; Impiombato, D.; La Rosa, G.; Maccarone, M. C.; Mangano, A.; Marano, D.; Mineo, T.; Segreto, A.; Strazzeri, E.; Timpanaro, M. C.

2013-06-01

UVSiPM is a light detector designed to measure the intensity of electromagnetic radiation in the 320-900 nm wavelength range. It has been developed in the framework of the ASTRI project whose main goal is the design and construction of an end-to-end Small Size class Telescope prototype for the Cherenkov Telescope Array. The UVSiPM instrument is composed by a multipixel Silicon Photo-Multiplier detector unit coupled to an electronic chain working in single photon counting mode with 10 nanosecond double pulse resolution, and by a disk emulator interface card for computer connection. The detector unit of UVSiPM is of the same kind as the ones forming the camera at the focal plane of the ASTRI prototype. Eventually, the UVSiPM instrument can be equipped with a collimator to regulate its angular aperture. UVSiPM, with its peculiar characteristics, will permit to perform several measurements both in lab and on field, allowing the absolute calibration of the ASTRI prototype.

Characterization of Fine Particulate Matter (PM) and Secondary PM Precursor Gases in the Mexico City Metropolitan Area

DOE R&D Accomplishments Database

Molina, Luisa T.; Volkamer, Rainer; de Foy, Benjamin; Lei, Wenfang; Zavala, Miguel; Velasco, Erik; Molina; Mario J.

2008-10-31

This project was one of three collaborating grants funded by DOE/ASP to characterize the fine particulate matter (PM) and secondary PM precursors in the Mexico City Metropolitan Area (MCMA) during the MILAGRO Campaign. The overall effort of MCMA-2006, one of the four components, focused on i) examination of the primary emissions of fine particles and precursor gases leading to photochemical production of atmospheric oxidants and secondary aerosol particles; ii) measurement and analysis of secondary oxidants and secondary fine PM production, with particular emphasis on secondary organic aerosol (SOA), and iii) evaluation of the photochemical and meteorological processes characteristic of the Mexico City Basin. The collaborative teams pursued the goals through three main tasks: i) analyses of fine PM and secondary PM precursor gaseous species data taken during the MCMA-2002/2003 campaigns and preparation of publications; ii) planning of the MILAGRO Campaign and deployment of the instrument around the MCMA; and iii) analysis of MCMA-2006 data and publication preparation.

Association of IL-6 with PM2.5 Components: Importance of Characterizing Filter-Based PM2.5 Following Extraction.

PubMed

Roper, Courtney; Chubb, Lauren G; Cambal, Leah; Tunno, Brett; Clougherty, Jane E; Fattman, Cheryl; Mischler, Steven E

2017-01-01

Filter-based toxicology studies are conducted to establish the biological plausibility of the well-established health impacts associated with fine particulate matter (PM 2.5 ) exposure. Ambient PM 2.5 collected on filters is extracted into solution for toxicology applications, but frequently, characterization is nonexistent or only performed on filter-based PM 2.5 , without consideration of compositional differences that occur during the extraction processes. To date, the impact of making associations to measured components in ambient instead of extracted PM 2.5 has not been investigated. Filter-based PM 2.5 was collected at locations ( n = 5) and detailed characterization of both ambient and extracted PM 2.5 was performed. Alveolar macrophages (AMJ2-C11) were exposed (3, 24, and 48 h) to PM 2.5 and the pro-inflammatory cytokine interleukin (IL)-6 was measured. IL-6 release differed significantly between PM 2.5 collected from different locations; surprisingly, IL-6 release was highest following treatment with PM 2.5 from the lowest ambient concentration location. IL-6 was negatively correlated with the sum of ambient metals analyzed, as well as with concentrations of specific constituents which have been previously associated with respiratory health effects. However, positive correlations of IL-6 with extracted concentrations indicated that the negative associations between IL-6 and ambient concentrations do not accurately represent the relationship between inflammation and PM 2.5 exposure. Additionally, seven organic compounds had significant associations with IL-6 release when considering ambient concentrations, but they were not detected in the extracted solution. Basing inflammatory associations on ambient concentrations that are not necessarily representative of in vitro exposures creates misleading results; this study highlights the importance of characterizing extraction solutions to conduct accurate health impact research.

Parametric Optimization of Wire Electrical Discharge Machining of Powder Metallurgical Cold Worked Tool Steel using Taguchi Method

NASA Astrophysics Data System (ADS)

Sudhakara, Dara; Prasanthi, Guvvala

2017-04-01

Wire Cut EDM is an unconventional machining process used to build components of complex shape. The current work mainly deals with optimization of surface roughness while machining P/M CW TOOL STEEL by Wire cut EDM using Taguchi method. The process parameters of the Wire Cut EDM is ON, OFF, IP, SV, WT, and WP. L27 OA is used for to design of the experiments for conducting experimentation. In order to find out the effecting parameters on the surface roughness, ANOVA analysis is engaged. The optimum levels for getting minimum surface roughness is ON = 108 µs, OFF = 63 µs, IP = 11 A, SV = 68 V and WT = 8 g.

Monetary Valuation of PM10-Related Health Risks in Beijing China: The Necessity for PM10 Pollution Indemnity

PubMed Central

Yin, Hao; Xu, Linyu; Cai, Yanpeng

2015-01-01

Severe health risks caused by PM10 (particulate matter with an aerodynamic diameter ≤10 μm) pollution have induced inevitable economic losses and have rendered pressure on the sustainable development of society as a whole. In China, with the “Polluters Pay Principle”, polluters should pay for the pollution they have caused, but how much they should pay remains an intractable problem for policy makers. This paper integrated an epidemiological exposure-response model with economics methods, including the Amended Human Capital (AHC) approach and the Cost of Illness (COI) method, to value the economic loss of PM10-related health risks in 16 districts and also 4 functional zones in Beijing from 2008 to 2012. The results show that from 2008 to 2012 the estimated annual deaths caused by PM10 in Beijing are around 56,000, 58,000, 63,000, 61,000 and 59,000, respectively, while the economic losses related to health damage increased from around 23 to 31 billion dollars that PM10 polluters should pay for pollution victims between 2008 and 2012. It is illustrated that not only PM10 concentration but also many other social economic factors influence PM10-related health economic losses, which makes health economic losses show a time lag discrepancy compared with the decline of PM10 concentration. In conclusion, health economic loss evaluation is imperative in the pollution indemnity system establishment and should be considered for the urban planning and policy making to control the burgeoning PM10 health economic loss. PMID:26308020

Monetary Valuation of PM10-Related Health Risks in Beijing China: The Necessity for PM10 Pollution Indemnity.

PubMed

Yin, Hao; Xu, Linyu; Cai, Yanpeng

2015-08-21

Severe health risks caused by PM10 (particulate matter with an aerodynamic diameter ≤10 μm) pollution have induced inevitable economic losses and have rendered pressure on the sustainable development of society as a whole. In China, with the "Polluters Pay Principle", polluters should pay for the pollution they have caused, but how much they should pay remains an intractable problem for policy makers. This paper integrated an epidemiological exposure-response model with economics methods, including the Amended Human Capital (AHC) approach and the Cost of Illness (COI) method, to value the economic loss of PM10-related health risks in 16 districts and also 4 functional zones in Beijing from 2008 to 2012. The results show that from 2008 to 2012 the estimated annual deaths caused by PM10 in Beijing are around 56,000, 58,000, 63,000, 61,000 and 59,000, respectively, while the economic losses related to health damage increased from around 23 to 31 billion dollars that PM10 polluters should pay for pollution victims between 2008 and 2012. It is illustrated that not only PM10 concentration but also many other social economic factors influence PM10-related health economic losses, which makes health economic losses show a time lag discrepancy compared with the decline of PM10 concentration. In conclusion, health economic loss evaluation is imperative in the pollution indemnity system establishment and should be considered for the urban planning and policy making to control the burgeoning PM10 health economic loss.

Measurement of $$D^{*\\pm}$$, $$D^\\pm$$ and $$D_s^\\pm$$ meson production cross sections in pp collisions at $$\\sqrt{s} = 7$$ TeV with the ATLAS detector

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aad, G.

The production ofmore » $$D^{*\\pm}$$, $$D^\\pm$$ and $$D_s^\\pm$$ charmed mesons has been measured with the ATLAS detector in pp collisions at √s = 7 TeV at the LHC, using data corresponding to an integrated luminosity of 280 nb -1. The charmed mesons have been reconstructed in the range of transverse momentum 3.5 < pT(D) < 100 GeV and pseudorapidity |η(D)| < 2.1. The differential cross sections as a function of transverse momentum and pseudorapidity were measured for $$D^{*\\pm}$$, $$D^\\pm$$ production. The next-to-leading-order QCD predictions are consistent with the data in the visible kinematic region within the large theoretical uncertainties. Lastly, using the visible D cross sections and an extrapolation to the full kinematic phase space, the strangeness-suppression factor in charm fragmentation, the fraction of charged non-strange D mesons produced in a vector state, and the total cross section of charm production at √s = 7TeV were derived.« less

Measurement of $$D^{*\\pm}$$, $$D^\\pm$$ and $$D_s^\\pm$$ meson production cross sections in pp collisions at $$\\sqrt{s} = 7$$ TeV with the ATLAS detector

DOE PAGES

Aad, G.

2016-04-25

The production ofmore » $$D^{*\\pm}$$, $$D^\\pm$$ and $$D_s^\\pm$$ charmed mesons has been measured with the ATLAS detector in pp collisions at √s = 7 TeV at the LHC, using data corresponding to an integrated luminosity of 280 nb -1. The charmed mesons have been reconstructed in the range of transverse momentum 3.5 < pT(D) < 100 GeV and pseudorapidity |η(D)| < 2.1. The differential cross sections as a function of transverse momentum and pseudorapidity were measured for $$D^{*\\pm}$$, $$D^\\pm$$ production. The next-to-leading-order QCD predictions are consistent with the data in the visible kinematic region within the large theoretical uncertainties. Lastly, using the visible D cross sections and an extrapolation to the full kinematic phase space, the strangeness-suppression factor in charm fragmentation, the fraction of charged non-strange D mesons produced in a vector state, and the total cross section of charm production at √s = 7TeV were derived.« less

Crosstalk of PmCBFs and PmDAMs Based on the Changes of Phytohormones under Seasonal Cold Stress in the Stem of Prunus mume

PubMed Central

Zhou, Yuzhen; Li, Yushu; Zhuo, Xiaokang; Ahmad, Sagheer; Han, Yu; Yong, Xue; Zhang, Qixiang

2018-01-01

Plants facing the seasonal variations always need a growth restraining mechanism when temperatures turn down. C-repeat binding factor (CBF) genes work essentially in the cold perception. Despite lots of researches on CBFs, the multiple crosstalk is still interesting on their interaction with hormones and dormancy-associated MADS (DAM) genes in the growth and dormancy control. Therefore, this study highlights roles of PmCBFs in cold-induced dormancy from different orgens. And a sense-response relationship between PmCBFs and PmDAMs is exhibited in this process, jointly regulated by six PmCBFs and PmDAM4–6. Meantime, GA3 and ABA showed negative and positive correlation with PmCBFs expression levels, respectively. We also find a high correlation between IAA and PmDAM1–3. Finally, we display the interaction mode of PmCBFs and PmDAMs, especially PmCBF1-PmDAM1. These results can disclose another view of molecular mechanism in plant growth between cold-response pathway and dormancy regulation together with genes and hormones. PMID:29360732

Vibration and Operational Characteristics of a Composite-Steel (Hybrid) Gear

NASA Technical Reports Server (NTRS)

Handschuh, Robert F.; LaBerge, Kelsen E.; DeLuca, Samuel; Pelagalli, Ryan

2014-01-01

Hybrid gears have been tested consisting of metallic gear teeth and shafting connected by composite web. Both free vibration and dynamic operation tests were completed at the NASA Glenn Spur Gear Fatigue Test Facility, comparing these hybrid gears to their steel counterparts. The free vibration tests indicated that the natural frequency of the hybrid gear was approximately 800 Hz lower than the steel test gear. The dynamic vibration tests were conducted at five different rotational speeds and three levels of torque in a four square test configuration. The hybrid gears were tested both as fabricated (machined, composite layup, then composite cure) and after regrinding the gear teeth to the required aerospace tolerance. The dynamic vibration tests indicated that the level of vibration for either type of gearing was sensitive to the level of load and rotational speed.

Segmentation and classification of brain images using firefly and hybrid kernel-based support vector machine

NASA Astrophysics Data System (ADS)

Selva Bhuvaneswari, K.; Geetha, P.

2017-05-01

Magnetic resonance imaging segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumour detection techniques are presented in the literature. The entire segmentation process of our proposed work comprises three phases: threshold generation with dynamic modified region growing phase, texture feature generation phase and region merging phase. by dynamically changing two thresholds in the modified region growing approach, the first phase of the given input image can be performed as dynamic modified region growing process, in which the optimisation algorithm, firefly algorithm help to optimise the two thresholds in modified region growing. After obtaining the region growth segmented image using modified region growing, the edges can be detected with edge detection algorithm. In the second phase, the texture feature can be extracted using entropy-based operation from the input image. In region merging phase, the results obtained from the texture feature-generation phase are combined with the results of dynamic modified region growing phase and similar regions are merged using a distance comparison between regions. After identifying the abnormal tissues, the classification can be done by hybrid kernel-based SVM (Support Vector Machine). The performance analysis of the proposed method will be carried by K-cross fold validation method. The proposed method will be implemented in MATLAB with various images.

Trajectory and Concentration PM10 on Forest and Vegetation Peat-Fire HYSPLIT Model Outputs and Observations (Period: September - October 2015)

NASA Astrophysics Data System (ADS)

Khairullah; Effendy, S.; Makmur, E. E. S.

2017-03-01

Forest and vegetation peat-fire is one of the main sources of air pollution in Kalimantan, predominantly during the dry period. In 2015, forest and vegetation fire in Central Kalimantan and South Kalimantan emit large quantities of smoke leading to poor air quality. Haze is a phenomenon characterized by high concentration of particulate matter. This research objective is to analyze trajectory and dispersion of concentration particulate matter, PM10 in Banjarbaru and Palangka Raya. Dynamics of PM10 (Particulate matter less than or 10 µm in size) on vegetation peat-fire is done using GDAS (Global Data Assimilation System) output with a horizontal resolution 1º which corresponds to 100 km × 100 km for input model. Climate conditions in the period September to October 2015 at generally during dry season of El Nino year. The Hybrid-single Langrangian Integrated Trajectory (HYSPLIT) model was used to investigate concentration and long-range movement of this pollutant from the source to the receptor area. We used time-series data on PM10 readings obtained from two stations Banjarbaru (South Kalimantan) and Palangka Raya (Central Kalimantan) belonging to Meteorology Climatology and Geophysics Agency (BMKG). We also used weather parameter such as wind speed and direction. We investigated trajectory run from hotspots information MoF (Sipongi Output Programs) and HYSPLIT. We compared concentration obtained from PM10 observation and its concentrations trend. The dispersion pattern, as simulated by HYSPLIT showed that the distribution of PM10 was greatly influenced by the wind direction and topography. There is a large difference between the concentration of PM10 Palangka Raya and Banjarbaru.

Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources

NASA Astrophysics Data System (ADS)

Querol, Xavier; Alastuey, Andrés; Rodriguez, Sergio; Plana, Felicià; Mantilla, Enrique; Ruiz, Carmen R.

Investigations on the monitoring of ambient air levels of atmospheric particulates were developed around a large source of primary anthropogenic particulate emissions: the industrial ceramic area in the province of Castelló (Eastern Spain). Although these primary particulate emissions have a coarse grain-size distribution, the atmospheric transport dominated by the breeze circulation accounts for a grain-size segregation, which results in ambient air particles occurring mainly in the 2.5-10 μm range. The chemical composition of the ceramic particulate emissions is very similar to the crustal end-member but the use of high Al, Ti and Fe as tracer elements as well as a peculiar grain-size distribution in the insoluble major phases allow us to identify the ceramic input in the bulk particulate matter. PM2.5 instead of PM10 monitoring may avoid the interference of crustal particles without a major reduction in the secondary anthropogenic load, with the exception of nitrate. However, a methodology based in PM2.5 measurement alone is not adequate for monitoring the impact of primary particulate emissions (such as ceramic emissions) on air quality, since the major ambient air particles derived from these emissions are mainly in the range of 2.5-10 μm. Consequently, in areas characterised by major secondary particulate emissions, PM2.5 monitoring should detect anthropogenic particulate pollutants without crustal particulate interference, whereas PM10 measurements should be used in areas with major primary anthropogenic particulate emissions.

a Gsa-Svm Hybrid System for Classification of Binary Problems

NASA Astrophysics Data System (ADS)

Sarafrazi, Soroor; Nezamabadi-pour, Hossein; Barahman, Mojgan

2011-06-01

This paperhybridizesgravitational search algorithm (GSA) with support vector machine (SVM) and made a novel GSA-SVM hybrid system to improve the classification accuracy in binary problems. GSA is an optimization heuristic toolused to optimize the value of SVM kernel parameter (in this paper, radial basis function (RBF) is chosen as the kernel function). The experimental results show that this newapproach can achieve high classification accuracy and is comparable to or better than the particle swarm optimization (PSO)-SVM and genetic algorithm (GA)-SVM, which are two hybrid systems for classification.

ADVANCES IN CONTROL OF PM2..5 AND PM2..5 PRECURSORS GENERATED BY THE COMBUSTION OF PULVERIZED COAL

EPA Science Inventory

Particulate matter smaller than 2.5 micrometers in aerodynamic diameter (PM2.5) is of concern due to adverse health effects associated with elevated ambient mass concentrations of PM2.5. PM2.5 from coal-fired utility boilers is composed of directly emitted (primary) particles and...

Tensile properties of interwoven hemp/PET (Polyethylene Terephthalate) epoxy hybrid composites

NASA Astrophysics Data System (ADS)

Ahmad, M. A. A.; Majid, M. S. A.; Ridzuan, M. J. M.; Firdaus, A. Z. A.; Amin, N. A. M.

2017-10-01

This paper describes the experimental investigation of the tensile properties of interwoven Hemp/PET hybrid composites. The effect of hybridization of hemp (warp) with PET fibres (weft) on tensile properties was of interest. Hemp and PET fibres were selected as the reinforcing material while epoxy resin was chosen as the matrix. The interwoven Hemp/PET fabric was used to produce hybrid composite using a vacuum infusion process. The tensile test was conducted using Universal Testing Machine in accordance to the ASTM D638. The tensile properties of the interwoven Hemp/PET hybrid composite were then compared with the neat woven hemp/epoxy composite. The results show that the strength of hemp/PET with the warp direction was increased by 8% compared to the neat woven hemp composite. This enhancement of tensile strength was due to the improved interlocking structure of interwoven Hemp/PET hybrid fabric.

Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning.

PubMed

Liu, Zhijian; Li, Hao; Cao, Guoqing

2017-07-30

Indoor airborne culturable bacteria are sometimes harmful to human health. Therefore, a quick estimation of their concentration is particularly necessary. However, measuring the indoor microorganism concentration (e.g., bacteria) usually requires a large amount of time, economic cost, and manpower. In this paper, we aim to provide a quick solution: using knowledge-based machine learning to provide quick estimation of the concentration of indoor airborne culturable bacteria only with the inputs of several measurable indoor environmental indicators, including: indoor particulate matter (PM 2.5 and PM 10 ), temperature, relative humidity, and CO₂ concentration. Our results show that a general regression neural network (GRNN) model can sufficiently provide a quick and decent estimation based on the model training and testing using an experimental database with 249 data groups.

Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning.

PubMed

Reid, Colleen E; Jerrett, Michael; Petersen, Maya L; Pfister, Gabriele G; Morefield, Philip E; Tager, Ira B; Raffuse, Sean M; Balmes, John R

2015-03-17

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

Urban-scale mapping of PM2.5 distribution via data fusion between high-density sensor network and MODIS Aerosol Optical Depth

NASA Astrophysics Data System (ADS)

Ba, Yu Tao; xian Liu, Bao; Sun, Feng; Wang, Li hua; Tang, Yu jia; Zhang, Da wei

2017-04-01

High-resolution mapping of PM2.5 is the prerequisite for precise analytics and subsequent anti-pollution interventions. Considering the large variances of particulate distribution, urban-scale mapping is challenging either with ground-based fixed stations, with satellites or via models. In this study, a dynamic fusion method between high-density sensor network and MODIS Aerosol Optical Depth (AOD) was introduced. The sensor network was deployed in Beijing ( > 1000 fixed monitors across 16000 km2 area) to provide raw observations with high temporal resolution (sampling interval < 1 hour), high spatial resolution in flat areas ( < 1 km), and low spatial resolution in mountainous areas ( > 5 km). The MODIS AOD was calibrated to provide distribution map with low temporal resolution (daily) and moderate spatial resolution ( = 3 km). By encoding the data quality and defects (e.g. could, reflectance, abnormal), a hybrid interpolation procedure with cross-validation generated PM2.5 distribution with both high temporal and spatial resolution. Several no-pollutant and high-pollution periods were tested to validate the proposed fusion method for capturing the instantaneous patterns of PM2.5 emission.

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

PubMed Central

Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

2013-01-01

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

First assessment of the PM10 and PM2.5 particulate level in the ambient air of Belgrade city.

PubMed

Rajsić, Slavica F; Tasić, Mirjana D; Novaković, Velibor T; Tomasević, Milica N

2004-01-01

As the strong negative health effect of exposure to the inhalable particulate matter PM10 in the urban environment has been confirmed, the study of the mass concentrations, physico-chemical characteristics, sources, as well as spatial and temporal variation of atmospheric aerosol particles becomes very important. This work is a pilot study to assess the concentration level of ambient suspended particulate matter, with an aerodynamic diameter of less than 10 microm, in the Belgrade central urban area. Average daily concentrations of PM10 and PM2.5 have been measured at three representative points in the city between June 2002 and December 2002. The influence of meteorological parameters on PM10 and PM2.5 concentrations was analyzed, and possible pollution sources were identified. Suspended particles were collected on Pure Teflon filters by using a Mini-Vol low-volume air sampler (Airmetrics Co., Inc.; 5 l min(-1) flow rate). Particle mass was determined gravimetrically after 48 h of conditioning in a desiccator, in a Class 100 clean room at the temperature T = 20 degrees C and at about 50% constant relative humidity (RH). Analysis of the PM10 data indicated a marked difference between season without heating--(summer; mean value 56 microg m(-3)) and heating season--(winter; mean value 96 microg m3); 62% of samples exceeded the level of 50 microg m(-3). The impact of meteorological factors on PM concentrations was not immediately apparent, but there was a significant negative correlation with the wind speed. The PM10 and PM2.5 mass concentrations in the Belgrade urban area had high average values (77 microg m(-3) and 61 microg m(-3)) in comparison with other European cities. The main sources of particulate matter were traffic emission, road dust resuspension, and individual heating emissions. When the air masses are coming from the SW direction, the contribution from the Obrenovac power plants is evident. During days of exceptionally severe pollution, in both summer

PM CHEMISTRY

EPA Science Inventory

Although PM2.5 can be directly introduced into the atmosphere through primary emissions, its mass concentration is also strongly affected by secondary processes such as nucleation or condensation of nonvolatile and semivolatile compounds on pre-existing aerosols. Chemical modules...

Chromosomal Location and Comparative Genomics Analysis of Powdery Mildew Resistance Gene Pm51 in a Putative Wheat-Thinopyrum ponticum Introgression Line

PubMed Central

Zhang, Xiaojun; Li, Xin; Guo, Huijuan; Gong, Wenping; Jia, Juqing; Qiao, Linyi; Ren, Yongkang; Yang, Zujun; Chang, Zhijian

2014-01-01

Powdery mildew (PM) is a very destructive disease of wheat (Triticum aestivum L.). Wheat-Thinopyrum ponticum introgression line CH7086 was shown to possess powdery mildew resistance possibly originating from Th. ponticum. Genomic in situ hybridization and molecular characterization of the alien introgression failed to identify alien chromatin. To study the genetics of resistance, CH7086 was crossed with susceptible genotypes. Segregation in F2 populations and F2:3 lines tested with Chinese Bgt race E09 under controlled conditions indicated that CH7086 carries a single dominant gene for powdery mildew resistance. Fourteen SSR and EST-PCR markers linked with the locus were identified. The genetic distances between the locus and the two flanking markers were 1.5 and 3.2 cM, respectively. Based on the locations of the markers by nullisomic-tetrasomic and deletion lines of ‘Chinese Spring’, the resistance gene was located in deletion bin 2BL-0.89-1.00. Conserved orthologous marker analysis indicated that the genomic region flanking the resistance gene has a high level of collinearity to that of rice chromosome 4 and Brachypodium chromosome 5. Both resistance specificities and tests of allelism suggested the resistance gene in CH7086 was different from previously reported powdery mildew resistance genes on 2BL, and the gene was provisionally designated PmCH86. Molecular analysis of PmCH86 compared with other genes for resistance to Bgt in the 2BL-0.89-1.00 region suggested that PmCH86 may be a new PM resistance gene, and it was therefore designated as Pm51. The closely linked flanking markers could be useful in exploiting this putative wheat-Thinopyrum translocation line for rapid transfer of Pm51 to wheat breeding programs. PMID:25415194

Functional specification of the Performance Measurement (PM) module

NASA Technical Reports Server (NTRS)

Berliner, J. E.

1980-01-01

The design of the Performance Measurement Module is described with emphasis on what the PM Module would do, and what it would look like to the user. The PM Module as described could take several man-years to develop. An evolutionary approach to the implementation of the PM Module is presented which would provide an operational baseline PM Module within a few months.

Metabolomic prediction of yield in hybrid rice.

PubMed

Xu, Shizhong; Xu, Yang; Gong, Liang; Zhang, Qifa

2016-10-01

Rice (Oryza sativa) provides a staple food source for more than 50% of the world's population. An increase in yield can significantly contribute to global food security. Hybrid breeding can potentially help to meet this goal because hybrid rice often shows a considerable increase in yield when compared with pure-bred cultivars. We recently developed a marker-guided prediction method for hybrid yield and showed a substantial increase in yield through genomic hybrid breeding. We now have transcriptomic and metabolomic data as potential resources for prediction. Using six prediction methods, including least absolute shrinkage and selection operator (LASSO), best linear unbiased prediction (BLUP), stochastic search variable selection, partial least squares, and support vector machines using the radial basis function and polynomial kernel function, we found that the predictability of hybrid yield can be further increased using these omic data. LASSO and BLUP are the most efficient methods for yield prediction. For high heritability traits, genomic data remain the most efficient predictors. When metabolomic data are used, the predictability of hybrid yield is almost doubled compared with genomic prediction. Of the 21 945 potential hybrids derived from 210 recombinant inbred lines, selection of the top 10 hybrids predicted from metabolites would lead to a ~30% increase in yield. We hypothesize that each metabolite represents a biologically built-in genetic network for yield; thus, using metabolites for prediction is equivalent to using information integrated from these hidden genetic networks for yield prediction. © 2016 The Authors The Plant Journal © 2016 John Wiley & Sons Ltd.

PM Program Prevents Early AM Repairs

ERIC Educational Resources Information Center

McRae, David

1974-01-01

Discusses how to initiate a preventive maintenance (PM) program: (1) make inventory of equipment that needs a PM program; (2) gather data about each piece of equipment; and (3) set maintenance goals. (Author/PG)

ENSO-related PM10 variability on the Korean Peninsula

NASA Astrophysics Data System (ADS)

Wie, Jieun; Moon, Byung-Kwon

2017-10-01

Particulate matter, defined as particles of less than 10 μm in diameter (PM10), was analyzed over the Korean Peninsula from 2001 to 2015 to examine the influence of the El Niño-Southern Oscillation (ENSO) on subseasonal PM10 variability. The PM10 data were obtained from 151 air quality monitoring stations provided by the Korea Environment Corporation (KECO). Lead-lag correlation analysis, which was performed to investigate the connection between NDJF (November-February) NINO3 index and seasonal mean PM10 data, did not yield any statistically significant correlations. However, using five-pentad moving-averaged PM10 data, statistically significant correlations between NDJF NINO3 index and PM10 variability were found in four subseasonal periods, with alternating positive and negative correlations. In the periods during which PM10 levels on the Korean Peninsula were positively (negatively) correlated with the ENSO index, the positive PM10 anomalies are associated with El Niño (La Niña) years, which implies that the occurrence of high-PM10 events could be modulated by the ENSO phase. In addition, this ENSO-related PM10 variation is negatively correlated with ENSO-related precipitation in the Korean Peninsula, indicating that more (less) wet deposition leads to lower (higher) PM10 level. Therefore, we conclude that the ENSO-induced precipitation anomalies over the Korean Peninsula are mainly responsible for ENSO-related PM10 variations. This study will be helpful for further identifying detailed chemistry-climate processes that control PM10 concentrations.

Analysis of laser-induction hybrid cladding processing conditions

NASA Astrophysics Data System (ADS)

Huang, Yongjun; Zeng, Xiaoyan; Hu, Qianwu

2007-12-01

A new cladding approach based on laser-induction hybrid technique on flat sheets is presented in this paper. Coating is produced by means of 5kw cw CO II laser equipped with 100kw high frequent inductor, and the experiments set-up, involving a special machining-head, which can provide laser-induction hybrid heat resources simultaneously. The formation of thick NiCrSiB coating on a steel substrate by off-axial powder feeding is studied from an experimental point of view. A substrate melting energy model is developed to describe the energy relationship between laser-induction hybrid cladding and laser cladding alone quantitatively. By comparing the experimental results with the calculational ones, it is shown that the tendency of fusion zone height of theoretical calculation is in agreement with that of tests in laser-induction hybrid cladding. Via analyses and tests, the conclusions can be lead to that the fusion zone height can be increased easily and the good bond of cladding track can be achieved within wide cladding processing window in laser-induction hybrid processing. It shows that the induction heating has an obvious effect on substrate melting and metallurgical bond.

Electric machine differential for vehicle traction control and stability control

NASA Astrophysics Data System (ADS)

Kuruppu, Sandun Shivantha

Evolving requirements in energy efficiency and tightening regulations for reliable electric drivetrains drive the advancement of the hybrid electric (HEV) and full electric vehicle (EV) technology. Different configurations of EV and HEV architectures are evaluated for their performance. The future technology is trending towards utilizing distinctive properties in electric machines to not only to improve efficiency but also to realize advanced road adhesion controls and vehicle stability controls. Electric machine differential (EMD) is such a concept under current investigation for applications in the near future. Reliability of a power train is critical. Therefore, sophisticated fault detection schemes are essential in guaranteeing reliable operation of a complex system such as an EMD. The research presented here emphasize on implementation of a 4kW electric machine differential, a novel single open phase fault diagnostic scheme, an implementation of a real time slip optimization algorithm and an electric machine differential based yaw stability improvement study. The proposed d-q current signature based SPO fault diagnostic algorithm detects the fault within one electrical cycle. The EMD based extremum seeking slip optimization algorithm reduces stopping distance by 30% compared to hydraulic braking based ABS.

Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data.

PubMed

Kloog, Itai; Sorek-Hamer, Meytar; Lyapustin, Alexei; Coull, Brent; Wang, Yujie; Just, Allan C; Schwartz, Joel; Broday, David M

2015-12-01

Estimates of exposure to PM 2.5 are often derived from geographic characteristics based on land-use regression or from a limited number of fixed ground monitors. Remote sensing advances have integrated these approaches with satellite-based measures of aerosol optical depth (AOD), which is spatially and temporally resolved, allowing greater coverage for PM 2.5 estimations. Israel is situated in a complex geo-climatic region with contrasting geographic and weather patterns, including both dark and bright surfaces within a relatively small area. Our goal was to examine the use of MODIS-based MAIAC data in Israel, and to explore the reliability of predicted PM 2.5 and PM 10 at a high spatiotemporal resolution. We applied a three stage process, including a daily calibration method based on a mixed effects model, to predict ground PM 2.5 and PM 10 over Israel. We later constructed daily predictions across Israel for 2003-2013 using spatial and temporal smoothing, to estimate AOD when satellite data were missing. Good model performance was achieved, with out-of-sample cross validation R 2 values of 0.79 and 0.72 for PM 10 and PM 2.5 , respectively. Model predictions had little bias, with cross-validated slopes (predicted vs. observed) of 0.99 for both the PM 2.5 and PM 10 models. To our knowledge, this is the first study that utilizes high resolution 1km MAIAC AOD retrievals for PM prediction while accounting for geo-climate complexities, such as experienced in Israel. This novel model allowed the reconstruction of long- and short-term spatially resolved exposure to PM 2.5 and PM 10 in Israel, which could be used in the future for epidemiological studies.

Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data

PubMed Central

Kloog, Itai; Sorek-Hamer, Meytar; Lyapustin, Alexei; Coull, Brent; Wang, Yujie; Just, Allan C.; Schwartz, Joel; Broday, David M.

2017-01-01

Estimates of exposure to PM2.5 are often derived from geographic characteristics based on land-use regression or from a limited number of fixed ground monitors. Remote sensing advances have integrated these approaches with satellite-based measures of aerosol optical depth (AOD), which is spatially and temporally resolved, allowing greater coverage for PM2.5 estimations. Israel is situated in a complex geo-climatic region with contrasting geographic and weather patterns, including both dark and bright surfaces within a relatively small area. Our goal was to examine the use of MODIS-based MAIAC data in Israel, and to explore the reliability of predicted PM2.5 and PM10 at a high spatiotemporal resolution. We applied a three stage process, including a daily calibration method based on a mixed effects model, to predict ground PM2.5 and PM10 over Israel. We later constructed daily predictions across Israel for 2003–2013 using spatial and temporal smoothing, to estimate AOD when satellite data were missing. Good model performance was achieved, with out-of-sample cross validation R2 values of 0.79 and 0.72 for PM10 and PM2.5, respectively. Model predictions had little bias, with cross-validated slopes (predicted vs. observed) of 0.99 for both the PM2.5 and PM10 models. To our knowledge, this is the first study that utilizes high resolution 1km MAIAC AOD retrievals for PM prediction while accounting for geo-climate complexities, such as experienced in Israel. This novel model allowed the reconstruction of long- and short-term spatially resolved exposure to PM2.5 and PM10 in Israel, which could be used in the future for epidemiological studies. PMID:28966551

Reduction of PM emissions from specific sources reflected on key components concentrations of ambient PM10

NASA Astrophysics Data System (ADS)

Minguillon, M. C.; Querol, X.; Monfort, E.; Alastuey, A.; Escrig, A.; Celades, I.; Miro, J. V.

2009-04-01

The relationship between specific particulate emission control and ambient levels of some PM10 components (Zn, As, Pb, Cs, Tl) was evaluated. To this end, the industrial area of Castellón (Eastern Spain) was selected, where around 40% of the EU glazed ceramic tiles and a high proportion of EU ceramic frits (middle product for the manufacture of ceramic glaze) are produced. The PM10 emissions from the ceramic processes were calculated over the period 2000 to 2007 taking into account the degree of implementation of corrective measures throughout the study period. Abatement systems (mainly bag filters) were implemented in the majority of the fusion kilns for frit manufacture in the area as a result of the application of the Directive 1996/61/CE, leading to a marked decrease in PM10 emissions. On the other hand, ambient PM10 sampling was carried out from April 2002 to July 2008 at three urban sites and one suburban site of the area and a complete chemical analysis was made for about 35 % of the collected samples, by means of different techniques (ICP-AES, ICP-MS, Ion Chromatography, selective electrode and elemental analyser). The series of chemical composition of PM10 allowed us to apply a source contribution model (Principal Component Analysis), followed by a multilinear regression analysis, so that PM10 sources were identified and their contribution to bulk ambient PM10 was quantified on a daily basis, as well as the contribution to bulk ambient concentrations of the identified key components (Zn, As, Pb, Cs, Tl). The contribution of the sources identified as the manufacture and use of ceramic glaze components, including the manufacture of ceramic frits, accounted for more than 65, 75, 58, 53, and 53% of ambient Zn, As, Pb, Cs and Tl levels, respectively (with the exception of Tl contribution at one of the sites). The important emission reductions of these sources during the study period had an impact on ambient key components levels, such that there was a high

Assessment of the long-term impacts of PM10 and PM2.5 particles from construction works on surrounding areas.

PubMed

Azarmi, Farhad; Kumar, Prashant; Marsh, Daniel; Fuller, Gary

2016-02-01

Construction activities are common across cities; however, the studies assessing their contribution to airborne PM10 (≤10 μm) and PM2.5 (≤2.5 μm) particles on the surrounding air quality are limited. Herein, we assessed the impact of PM10 and PM2.5 arising from construction works in and around London. Measurements were carried out at 17 different monitoring stations around three construction sites between January 2002 and December 2013. Tapered element oscillating microbalance (TEOM 1400) and OSIRIS (2315) particle monitors were used to measure the PM10 and PM2.5 fractions in the 0.1-10 μm size range along with the ambient meteorological data. The data was analysed using bivariate concentration polar plots and k-means clustering techniques. Daily mean concentrations of PM10 were found to exceed the European Union target limit value of 50 μg m(-3) at 11 monitoring stations but remained within the allowable 35 exceedences per year, except at two monitoring stations. In general, construction works were found to influence the downwind concentrations of PM10 relatively more than PM2.5. Splitting of the data between working (0800-1800 h; local time) and non-working (1800-0800 h) periods showed about 2.2-fold higher concentrations of PM10 during working hours when compared with non-working hours. However, these observations did not allow to conclude that this increase was from the construction site emissions. Together, the polar concentration plots and the k-means cluster analysis applied to a pair of monitoring stations across the construction sites (i.e. one in upwind and the other in downwind) confirmed the contribution of construction sources on the measured concentrations. Furthermore, pairing the monitoring stations downwind of the construction sites showed a logarithmic decrease (with R(2) about 0.9) in the PM10 and PM2.5 concentration with distance. Our findings clearly indicate an impact of construction activities on the nearby downwind areas and a need

Temporal variations and spatial distribution of ambient PM2.2 and PM10 concentrations in Dhaka, Bangladesh.

PubMed

Begum, Bilkis A; Biswas, Swapan K; Hopke, Philip K

2006-04-01

Concentrations and characteristics of airborne particulate matter (PM(10), PM(2.2) and BC) on air quality have been studied at two air quality-monitoring stations in Dhaka, the capital of Bangladesh. One site is at the Farm Gate area, a hot spot with very high pollutant concentrations because of its proximity to major roadways. The other site is at a semi-residential area located at the Atomic Energy Centre, Dhaka Campus, (AECD) with relatively less traffic. The samples were collected using a 'Gent' stacked filter unit in two fractions of 0-2.2 mum and 2.2-10 mum sizes. Samples of fine (PM(2.2)) and coarse (PM(2.2-10)) airborne particulate matter fractions collected from 2000 to 2003 were studied. It has been observed that fine particulate matter has a decreasing trend, from prior year measurements, because of Government policy interventions like phase-wise plans to take two-stroke three-wheelers off the roads in Dhaka and finally banned from January 1, 2003. Other policy interventions were banning of old buses and trucks to ply on Dhaka city promotion of the using compressed natural gas (CNG), introducing air pollution control devices in vehicles, etc. It was found that both local (mostly from vehicular emissions) and possibly some regional emission sources are responsible for high PM(2.2) and BC concentrations in Dhaka. PM(2.2), PM(2.2-10) and black carbon concentration levels depend on the season, wind direction and wind speed. Transport related emissions are the major source of BC and long-range transportation from fossil fuel related sources and biomass burning could be another substantial source of BC.

[Re-signification of the human in the context of the "ciborgzation": a look at the human being-machine relationship in intensive care].

PubMed

Vargas, Mara Ambrosina de O; Meyer, Dagmar Estermann

2005-06-01

This study discusses the human being-machine relationship in the process called "cyborgzation" of the nurse who works in intensive care, based on post-structuralist Cultural Studies and highlighting Haraway's concept of cyborg. In it, manuals used by nurses in Intensive Care Units have been examined as cultural texts. This cultural analysis tries to decode the various senses of "human" and "machine", with the aim of recognizing processes that turn nurses into cyborgs. The argument is that intensive care nurses fall into a process of "technology embodiment" that turns the body-professional into a hybrid that makes possible to disqualify, at the same time, notions such as machine and body "proper", since it is the hybridization between one and the other that counts there. Like cyborgs, intensive care nurses learn to "be with" the machine, and this connection limits the specificity of their actions. It is suggested that processes of "cyborgzation" such as this are useful for questioning - and to deal with in different ways - the senses of "human" and "humanity" that support a major part of knowledge/action in health.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

PubMed Central

Dipnall, Joanna F.

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

PubMed

Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

2016-01-01

Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and

Method and apparatus for executing an asynchronous clutch-to-clutch shift in a hybrid transmission

DOEpatents

Demirovic, Besim; Gupta, Pinaki; Kaminsky, Lawrence A.; Naqvi, Ali K.; Heap, Anthony H.; Sah, Jy-Jen F.

2014-08-12

A hybrid transmission includes first and second electric machines. A method for operating the hybrid transmission in response to a command to execute a shift from an initial continuously variable mode to a target continuously variable mode includes increasing torque of an oncoming clutch associated with operating in the target continuously variable mode and correspondingly decreasing a torque of an off-going clutch associated with operating in the initial continuously variable mode. Upon deactivation of the off-going clutch, torque outputs of the first and second electric machines and the torque of the oncoming clutch are controlled to synchronize the oncoming clutch. Upon synchronization of the oncoming clutch, the torque for the oncoming clutch is increased and the transmission is operated in the target continuously variable mode.

Chemical composition and source apportionment of PM10 and PM2.5 in different functional areas of Lanzhou, China.

PubMed

Qiu, Xionghui; Duan, Lei; Gao, Jian; Wang, Shulan; Chai, Fahe; Hu, Jun; Zhang, Jingqiao; Yun, Yaru

2016-02-01

To elucidate the air pollution characteristics of northern China, airborne PM10 (atmospheric dynamic equivalent diameter ≤ 10 μm) and PM2.5 (atmospheric dynamic equivalent diameter ≤ 2.5 μm) were sampled in three different functional areas (Yuzhong County, Xigu District and Chengguan District) of Lanzhou, and their chemical composition (elements, ions, carbonaceous species) was analyzed. The results demonstrated that the highest seasonal mean concentrations of PM10 (369.48 μg/m(3)) and PM2.5 (295.42 μg/m(3)) were detected in Xigu District in the winter, the lowest concentration of PM2.5 (53.15 μg/m(3)) was observed in Yuzhong District in the fall and PM10 (89.60 μg/m(3)) in Xigu District in the fall. The overall average OC/EC (organic carbon/elemental carbon) value was close to the representative OC/EC ratio for coal consumption, implying that the pollution of Lanzhou could be attributed to the burning of coal. The content of SNA (the sum of sulfate, nitrate, ammonium, SNA) in PM2.5 in Yuzhong County was generally lower than that at other sites in all seasons. The content of SNA in PM2.5 and PM10 in Yuzhong County was generally lower than that at other sites in all seasons (0.24-0.38), indicating that the conversion ratios from precursors to secondary aerosols in the low concentration area was slower than in the area with high and intense pollutants. Six primary particulate matter sources were chosen based on positive matrix factorization (PMF) analysis, and emissions from dust, secondary aerosols, and coal burning were identified to be the primary sources responsible for the particle pollution in Lanzhou. Copyright © 2015. Published by Elsevier B.V.

Respiratory hospitalizations in association with fine PM and its ...

EPA Pesticide Factsheets

Despite observed geographic and temporal variation in particulate matter (PM)-related health morbidities, only a small number of epidemiologic studies have evaluated the relation between PM2.5 chemical constituents and respiratory disease. Most assessments are limited by inadequate spatial and temporal resolution of ambient PM measurements and/or by their approaches to examine the role of specific PM components on health outcomes. In a case-crossover analysis using daily average ambient PM2.5 total mass and species estimates derived from the Community Multiscale Air Quality (CMAQ) model and available observations, we examined the association between the chemical components of PM (including elemental and organic carbon, sulfate, nitrate, ammonium, and other remaining) and respiratory hospitalizations in New York State. We evaluated relationships between levels (low, medium, high) of PM constituent mass fractions, and assessed modification of the PM2.5–hospitalization association via models stratified by mass fractions of both primary and secondary PM components. In our results, average daily PM2.5 concentrations in New York State were generally lower than the 24-hr average National Ambient Air Quality Standard (NAAQS). Year-round analyses showed statistically significant positive associations between respiratory hospitalizations and PM2.5 total mass, sulfate, nitrate, and ammonium concentrations at multiple exposure lags (0.5–2.0% per interquartile range [IQR

Simulations of the Fuel Economy and Emissions of Hybrid Transit Buses over Planned Local Routes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Zhiming; LaClair, Tim J; Daw, C Stuart

2014-01-01

We present simulated fuel economy and emissions city transit buses powered by conventional diesel engines and diesel-hybrid electric powertrains of varying size. Six representative city drive cycles were included in the study. In addition, we included previously published aftertreatment device models for control of CO, HC, NOx, and particulate matter (PM) emissions. Our results reveal that bus hybridization can significantly enhance fuel economy by reducing engine idling time, reducing demands for accessory loads, exploiting regenerative braking, and shifting engine operation to speeds and loads with higher fuel efficiency. Increased hybridization also tends to monotonically reduce engine-out emissions, but trends inmore » the tailpipe (post-aftertreatment) emissions involve more complex interactions that significantly depend on motor size and drive cycle details.« less

Correlating bioaerosol load with PM2.5 and PM10cf concentrations: a comparison between natural desert and urban-fringe aerosols

NASA Astrophysics Data System (ADS)

Boreson, Justin; Dillner, Ann M.; Peccia, Jordan

2004-11-01

Seasonal allergies and microbial mediated respiratory diseases, can coincide with elevated particulate matter concentrations, often when dry desert soils are disturbed. In addition to effects from the allergens, allergic and asthmatic responses may be enhanced when chemical and biological constituents of particulate matter (PM) are combined together. Because of these associations and also the recent regulatory and health-related interests of monitoring PM2.5, separately from total PM10, the biological loading between the fine (dp<2.5 μm) and coarse (2.5 μmPM was studied. To investigate spatial and seasonal differences of biological loading within PM, 24-h fine and coarse PM fractions were collected at a natural desert area and an urban fringe site located in the expanding Phoenix, Arizona metropolitan area during winter, spring, and summer seasons. Elemental carbon and inorganic ions were measured to determine the relative influence that anthropogenic sources, such as traffic, had on the aerosol composition. Total protein concentration was used as a surrogate measure of total biological concentration within the PM2.5 and PM10cf (coarse fraction) size ranges. In all seasons, coarse protein at the urban fringe was consistently higher than the natural desert. When high-anthropogenic PM events were separated from the data set, a positive significant correlation (p<0.05) was found between protein and coarse PM fraction, but not in the fine fraction. An 18S rDNA clone library was developed from PM10 aerosol samples to characterize the type and phylogenetic diversity of airborne eukaryotic (non-bacterial) microorganisms existing in ambient PM for the urban fringe and natural desert. Both sites contained allergenic organisms. Some groups of eukaryotic species were exclusive to only one of the sites. The natural desert contained more species of Basidiomycota fungi and the urban fringe contained more species of green plants, suggesting that the

Size distribution of PM at Cape Verde - Santiago Island

NASA Astrophysics Data System (ADS)

Pio, C.; Nunes, T.; Cardoso, J.; Caseiro, A.; Cerqueira, M.; Custodio, D.; Freitas, M. C.; Almeida, S. M.

2012-04-01

The archipelago of Cape Verde is located on the eastern North Atlantic, about 500 km west of the African coast. Its geographical location, inside the main area of dust transport over tropical Atlantic and near the coast of Africa, is strongly affected by mineral dust from the Sahara and the Sahel regions. In the scope of the CVDust project a surface field station was implemented in the surroundings of Praia City, Santiago Island (14° 55' N e 23° 29' W, 98 m at sea level), where aerosol sampling throughout different samplers was performed during one year. To study the size distribution of aerosol, an optical dust monitor (Grimm 180), from 0.250 to 32 μm in 31 size channels, was running almost continuously from January 2011 to December 2011. The performance of Grimm 180 to quantify PM mass concentration in an area affected by the transport of Saharan dust particles was evaluated throughout the sampling period by comparison with PM10 mass concentrations obtained with the gravimetric reference method (PM10 TSI High-Volume, PM10 Partisol and PM10 TCR-Tecora). PM10 mass concentration estimated with the Grimm 180 dust monitor, an optical counter, showed a good correlation with the reference gravimetric method, with R2= 0.94 and a linear regression equation of PM10Grimm = 0.81PM10TCR- 5.34. The number and mass size distribution of PM at ground level together with meteorological and back trajectories were analyzed and compared for different conditions aiming at identifying different signatures related to sources and dust transport. January and February, the months when most Saharan dust events occurred, showed the highest concentrations, with PM10 daily average of 66.6±60.2 μg m-3 and 91.6±97.4 μg m-3, respectively. During these months PM1 and PM2.5 accounted for less than 11% and 47% of PM10 respectively, and the contribution of fine fractions (PM1 and PM2.5) to PM mass concentrations tended to increase for the other months. During Saharan dust events, the PM2

A Comprehensive Review of Permanent Magnet Transverse Flux Machines for Direct Drive Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muljadi, Eduard; Husain, Tausif; Hasan, Iftekhar

The use of direct drive machines in renewable and industrial applications are increasing at a rapid rate. Transverse flux machines (TFM) are ideally suited for direct drive applications due to their high torque density. In this paper, a comprehensive review of the permanent magnet (PM) TFMs for direct drive applications is presented. The paper introduces TFMs and their operating principle and then reviews the different type of TFMs proposed in the literature. The TFMs are categorized according to the number of stator sides, types of stator cores and magnet arrangement in the rotor. The review covers different design topologies, materialsmore » used for manufacturing, structural and thermal analysis, modeling and design optimization and cogging torque minimization in TFMs. The paper also reviews various applications and comparisons for TFMs that have been presented in the literature.« less

Method and radial gap machine for high strength undiffused brushless operation

DOEpatents

Hsu, John S.

2006-10-31

A radial gap brushless electric machine (30) having a stator (31) and a rotor (32) and a main air gap (34) also has at least one stationary excitation coil (35a, 36a) separated from the rotor (32) by a secondary air gap (35e, 35f, 36e, 36f) so as to induce a secondary flux in the rotor (32) which controls a resultant flux in the main air gap (34). Permanent magnetic (PM) material (38) is disposed in spaces between the rotor pole portions (39) to inhibit the second flux from leaking from the pole portions (39) prior to reaching the main air gap (34). By selecting the direction of current in the stationary excitation coil (35a, 36a) both flux enhancement and flux weakening are provided for the main air gap (34). A method of non-diffused flux enhancement and flux weakening for a radial gap machine is also disclosed.

Vibration Damping Analysis of Lightweight Structures in Machine Tools

PubMed Central

Aggogeri, Francesco; Borboni, Alberto; Merlo, Angelo; Pellegrini, Nicola; Ricatto, Raffaele

2017-01-01

The dynamic behaviour of a machine tool (MT) directly influences the machining performance. The adoption of lightweight structures may reduce the effects of undesired vibrations and increase the workpiece quality. This paper aims to present and compare a set of hybrid materials that may be excellent candidates to fabricate the MT moving parts. The selected materials have high dynamic characteristics and capacity to dampen mechanical vibrations. In this way, starting from the kinematic model of a milling machine, this study evaluates a number of prototypes made of Al foam sandwiches (AFS), Al corrugated sandwiches (ACS) and composite materials reinforced by carbon fibres (CFRP). These prototypes represented the Z-axis ram of a commercial milling machine. The static and dynamical properties have been analysed by using both finite element (FE) simulations and experimental tests. The obtained results show that the proposed structures may be a valid alternative to the conventional materials of MT moving parts, increasing machining performance. In particular, the AFS prototype highlighted a damping ratio that is 20 times greater than a conventional ram (e.g., steel). Its application is particularly suitable to minimize unwanted oscillations during high-speed finishing operations. The results also show that the CFRP structure guarantees high stiffness with a weight reduced by 48.5%, suggesting effective applications in roughing operations, saving MT energy consumption. The ACS structure has a good trade-off between stiffness and damping and may represent a further alternative, if correctly evaluated. PMID:28772653

Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States.

PubMed

Lee, Seung-Jae; Serre, Marc L; van Donkelaar, Aaron; Martin, Randall V; Burnett, Richard T; Jerrett, Michael

2012-12-01

A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data. We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation. We developed a space-time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals. The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates. We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.

A hybrid prognostic model for multistep ahead prediction of machine condition

NASA Astrophysics Data System (ADS)

Roulias, D.; Loutas, T. H.; Kostopoulos, V.

2012-05-01

Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

Emission and profile characteristic of polycyclic aromatic hydrocarbons in PM2.5 and PM10 from stationary sources based on dilution sampling

NASA Astrophysics Data System (ADS)

Kong, Shaofei; Ji, Yaqin; Li, Zhiyong; Lu, Bing; Bai, Zhipeng

2013-10-01

The mass concentrations and profile characteristic for 18 kinds of polycyclic aromatic hydrocarbons (PAHs) in PM2.5 and PM10 from stack gases for six types of stationary sources in Shandong Province, China were studied by a dilution sampling system and GC-MS analysis method from February to March in 2010. The mass concentrations of PM2.5 and PM10 from the six types of stationary sources varied in 8.2-79.4 mg m-3 and 23.3-156.7 mg m-3, respectively. The total mass concentrations of analyzed PAHs in PM2.5 and PM10 were in the ranges of 0.40-94.35 μg m-3 and 9.16-122.91 μg m-3. The most toxic ashes were from sinter and coke oven for both PM2.5 and PM10 with high carcinogenic PAHs concentrations. BbF, Phe, NaP, BghiP, Pyr, BaP and BeP were abundant which was different from formers and one of the key reasons may be the differences of sampling methods. Diversities in PAHs compositions existed between fly ashes within PM2.5 and PM10 fractions for coke oven according to coefficient of divergence (CD) values. PAHs profiles for PM10 emitted from coke oven were different from those of other stationary sources (with CD values higher than 0.35) and for PM2.5, it was the same for sinter (with most CD values close to 0.30). There existed similar PAHs markers for fine particles emitted from stationary sources excepted for the sinter. For PM10, PAHs markers were primary 3-ring PAHs except for the coke oven with BbF, IND and BghiP as its signatures. Diagnostic ratios of BaA/(BaA + Chr), Flu/(Flu + Pyr), BaP/(BaP + BeP), BeP/BghiP and IND/(IND + BghiP) could be not well distinguished for the six types of stationary sources with the maximum/minimum ratios lower than 2 for both PM2.5 and PM10 of fly ashes which should be not used for source identification studies. The mass concentrations and source profiles of PAHs should be updated timely for size-differentiated fly ashes from various stationary sources by dilution sampling method.

Comparative hybrid and digital simulation studies of the behaviour of a wind generator equipped with a static frequency converter

NASA Astrophysics Data System (ADS)

Dube, B.; Lefebvre, S.; Perocheau, A.; Nakra, H. L.

1988-01-01

This paper describes the comparative results obtained from digital and hybrid simulation studies on a variable speed wind generator interconnected to the utility grid. The wind generator is a vertical-axis Darrieus type coupled to a synchronous machine by a gear-box; the synchronous machine is connected to the AC utility grid through a static frequency converter. Digital simulation results have been obtained using CSMP software; these results are compared with those obtained from a real-time hybrid simulator that in turn uses a part of the IREQ HVDC simulator. The agreement between hybrid and digital simulation results is generally good. The results demonstrate that the digital simulation reproduces the dynamic behavior of the system in a satisfactory manner and thus constitutes a valid tool for the design of the control systems of the wind generator.

Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning

PubMed Central

Liu, Zhijian; Li, Hao; Cao, Guoqing

2017-01-01

Indoor airborne culturable bacteria are sometimes harmful to human health. Therefore, a quick estimation of their concentration is particularly necessary. However, measuring the indoor microorganism concentration (e.g., bacteria) usually requires a large amount of time, economic cost, and manpower. In this paper, we aim to provide a quick solution: using knowledge-based machine learning to provide quick estimation of the concentration of indoor airborne culturable bacteria only with the inputs of several measurable indoor environmental indicators, including: indoor particulate matter (PM2.5 and PM10), temperature, relative humidity, and CO2 concentration. Our results show that a general regression neural network (GRNN) model can sufficiently provide a quick and decent estimation based on the model training and testing using an experimental database with 249 data groups. PMID:28758941

Machine rates for selected forest harvesting machines

Treesearch

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

Development of an MRI-compatible digital SiPM detector stack for simultaneous PET/MRI.

PubMed

Düppenbecker, Peter M; Weissler, Bjoern; Gebhardt, Pierre; Schug, David; Wehner, Jakob; Marsden, Paul K; Schulz, Volkmar

2016-02-01

Advances in solid-state photon detectors paved the way to combine positron emission tomography (PET) and magnetic resonance imaging (MRI) into highly integrated, truly simultaneous, hybrid imaging systems. Based on the most recent digital SiPM technology, we developed an MRI-compatible PET detector stack, intended as a building block for next generation simultaneous PET/MRI systems. Our detector stack comprises an array of 8 × 8 digital SiPM channels with 4 mm pitch using Philips Digital Photon Counting DPC 3200-22 devices, an FPGA for data acquisition, a supply voltage control system and a cooling infrastructure. This is the first detector design that allows the operation of digital SiPMs simultaneously inside an MRI system. We tested and optimized the MRI-compatibility of our detector stack on a laboratory test bench as well as in combination with a Philips Achieva 3 T MRI system. Our design clearly reduces distortions of the static magnetic field compared to a conventional design. The MRI static magnetic field causes weak and directional drift effects on voltage regulators, but has no direct impact on detector performance. MRI gradient switching initially degraded energy and timing resolution. Both distortions could be ascribed to voltage variations induced on the bias and the FPGA core voltage supply respectively. Based on these findings, we improved our detector design and our final design shows virtually no energy or timing degradations, even during heavy and continuous MRI gradient switching. In particular, we found no evidence that the performance of the DPC 3200-22 digital SiPM itself is degraded by the MRI system.

Challenges in evaluating PM concentration levels, commuting exposure, and mask efficacy in reducing PM exposure in growing, urban communities in a developing country.

PubMed

Patel, Disa; Shibata, Tomoyuki; Wilson, James; Maidin, Alimin

2016-02-01

Particulate matter (PM) contributes to an increased risk of respiratory and cardiovascular illnesses, cancer, and preterm birth complications. This project assessed PM exposure in Eastern Indonesia's largest city, where air quality has not been comprehensively monitored. We examined the efficacy of wearing masks as an individual intervention effort to reduce in-transit PM exposures. Handheld particulate counters were used to investigate ambient air quality for spatial analysis, as well as the differences in exposure to PM2.5 and PM10 (μg/m(3)) by different transportation methods [e.g. motorcycle (n=97), pete-pete (n=53), and car (n=55); note: n=1 means 1m(3) of air sample]. Mask efficacy to reduce PM exposure was evaluated [e.g. surgical masks (n=39), bandanas (n=52), and motorcycle masks (n=39)]. A Monte Carlo simulation was used to provide a range of uncertainty in exposure assessment. Overall PM10 levels (91±124 μg/m(3)) were elevated compared to the World Health Organization (WHO)'s 24-hour air quality guideline (50 μg/m(3)). While average PM2.5 levels (9±14 μg/m(3)) were below the WHO's guideline (25 μg/m(3)), measurements up to 139 μg/m(3) were observed. Compared to cars, average motorcycle and pete-pete PM exposures were four and three times higher for PM2.5, and 13 and 10 times higher for PM10, respectively. Only surgical masks were consistent in lowering PM2.5 and PM10 (p<0.01). Young children (≤5) were the most vulnerable age group, and could not reach the safe dosage even when wearing surgical masks. Individual interventions can effectively reduce individual PM exposures; however, policy interventions will be needed to improve the overall air quality and create safer transportation. Copyright © 2015 Elsevier B.V. All rights reserved.

Total and water-soluble trace metal content of urban background PM 10, PM 2.5 and black smoke in Edinburgh, UK

NASA Astrophysics Data System (ADS)

Heal, Mathew R.; Hibbs, Leon R.; Agius, Raymond M.; Beverland, Iain J.

Toxicological studies have implicated trace metals in airborne particles as possible contributors to respiratory and/or cardiovascular inflammation. As part of an epidemiological study, co-located 24 h samples of PM 10, PM 2.5 and black smoke (BS) were collected for 1 year at an urban background site in Edinburgh, and each sample sequentially extracted with ultra-pure water, then concentrated HNO 3/HCl, and analysed for Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Cd and Pb. This yields a comprehensive data set for UK urban airborne trace metal. The median ( n>349) daily water-soluble metal concentration in PM 2.5 ranged from 0.05 ng m -3 for Ti to 5.1 ng m -3 for Pb; and in PM 10 from 0.18 ng m -3 for Ti to 11.7 ng m -3 for Fe. Median daily total (i.e. water+acid-extractable) metal concentration in PM 2.5 ranged from 0.3 ng m -3 for As to 27.6 ng m -3 for Fe; and in PM 10 from 0.37 ng m -3 for As to 183 ng m -3 for Fe. The PM 2.5:PM 10 ratio varied considerably with metal, from <17%, on average, for Ti and Fe, to >70% for V, As, Cd and Pb. The 11 trace metals constituted proportionally more of the PM 10-2.5 fraction than of the PM 2.5 fraction (0.9%). The proportion of water-soluble metal in each size-fraction varied considerably, from <10% water-soluble Fe and Ti in PM 10-2.5, to >50% water-soluble V, Zn, As and Cd in PM 2.5. Although Fe generally dominated the trace metal, water-soluble metal also contained significant Zn, Pb and Cu, and for all size and solubility fractions >90% of trace metal was comprised of Fe, Zn, Pb and Cu. Statistical analyses suggested three main sources: traffic; static combustion; and crustal. The association of metals with traffic (Cu, Fe, Mn, Pb, Zn) was consistent with traffic-induced non-exhaust "resuspension" rather than direct exhaust emission. Meteorology contributed to the wide variation in daily trace metal concentration. The proportion of trace metal in particles varied significantly with the air mass source and was highest on days for

Design Enhancement and Performance Examination of External Rotor Switched Flux Permanent Magnet Machine for Downhole Application

NASA Astrophysics Data System (ADS)

Kumar, R.; Sulaiman, E.; Soomro, H. A.; Jusoh, L. I.; Bahrim, F. S.; Omar, M. F.

2017-08-01

The recent change in innovation and employments of high-temperature magnets, permanent magnet flux switching machine (PMFSM) has turned out to be one of the suitable contenders for seaward boring, however, less intended for downhole because of high atmospheric temperature. Subsequently, this extensive review manages the design enhancement and performance examination of external rotor PMFSM for the downhole application. Preparatory, the essential design parameters required for machine configuration are computed numerically. At that point, the design enhancement strategy is actualized through deterministic technique. At last, preliminary and refined execution of the machine is contrasted and as a consequence, the yield torque is raised from 16.39Nm to 33.57Nm while depreciating the cogging torque and PM weight up to 1.77Nm and 0.79kg, individually. In this manner, it is inferred that purposed enhanced design of 12slot-22pole with external rotor is convenient for the downhole application.

Impact of diurnal variability and meteorological factors on the PM2.5 - AOD relationship: Implications for PM2.5 remote sensing.

PubMed

Guo, Jianping; Xia, Feng; Zhang, Yong; Liu, Huan; Li, Jing; Lou, Mengyun; He, Jing; Yan, Yan; Wang, Fu; Min, Min; Zhai, Panmao

2017-02-01

PM 2.5 retrieval from space is still challenging due to the elusive relationship between PM 2.5 and aerosol optical depth (AOD), which is further complicated by meteorological factors. In this work, we investigated the diurnal cycle of PM 2.5 in China, using ground-based PM measurements obtained at 226 sites of China Atmosphere Watch Network during the period of January 2013 to December 2015. Results showed that nearly half of the sites witnessed a PM 2.5 maximum in the morning, in contrast to the least frequent occurrence (5%) in the afternoon when strong solar radiation received at the surface results in rapid vertical diffusion of aerosols and thus lower mass concentration. PM 2.5 tends to peak equally in the morning and evening in North China Plain (NCP) with an amplitude of nearly twice or three times that in the Pearl River Delta (PRD), whereas the morning PM 2.5 peak dominates in Yangtze River Delta (YRD) with a magnitude lying between those of NCP and PRD. The gridded correlation maps reveal varying correlations around each PM 2.5 site, depending on the locations and seasons. Concerning the impact of aerosol diurnal variation on the correlation, the averaging schemes of PM 2.5 using 3-h, 5-h, and 24-h time windows tend to have larger R biases, compared with the scheme of 1-h time window, indicating diurnal variation of aerosols plays a significant role in the establishment of explicit correlation between PM 2.5 and AOD. In addition, high cloud fraction and relative humidity tend to weaken the correlation, regardless of geographical location. Therefore, the impact of meteorology could be one of the most plausible alternatives in explaining the varying R values observed, due to its non-negligible effect on MODIS AOD retrievals. Our findings have implications for PM 2.5 remote sensing, as long as the aerosol diurnal cycle, along with meteorology, are explicitly considered in the future. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining.

PubMed

Salehi, Mojtaba; Bahreininejad, Ardeshir

2011-08-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining

PubMed Central

Salehi, Mojtaba

2010-01-01

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously. PMID:21845020

Analytical Modeling of a Novel Transverse Flux Machine for Direct Drive Wind Turbine Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasan, IIftekhar; Husain, Tausif; Uddin, Md Wasi

2015-09-02

This paper presents a nonlinear analytical model of a novel double sided flux concentrating Transverse Flux Machine (TFM) based on the Magnetic Equivalent Circuit (MEC) model. The analytical model uses a series-parallel combination of flux tubes to predict the flux paths through different parts of the machine including air gaps, permanent magnets (PM), stator, and rotor. The two-dimensional MEC model approximates the complex three-dimensional flux paths of the TFM and includes the effects of magnetic saturation. The model is capable of adapting to any geometry which makes it a good alternative for evaluating prospective designs of TFM as compared tomore » finite element solvers which are numerically intensive and require more computation time. A single phase, 1 kW, 400 rpm machine is analytically modeled and its resulting flux distribution, no-load EMF and torque, verified with Finite Element Analysis (FEA). The results are found to be in agreement with less than 5% error, while reducing the computation time by 25 times.« less

Estimation of the direct and indirect impacts of fireworks on the physicochemical characteristics of atmospheric PM10 and PM2.5

NASA Astrophysics Data System (ADS)

Tian, Y. Z.; Wang, J.; Peng, X.; Shi, G. L.; Feng, Y. C.

2014-09-01

To quantify the total, direct and indirect impacts of fireworks individually, size-resolved PM samples were collected before, during and after a Chinese folk festival (Chinese New Year) in a megacity in China. Through chemical analysis and morphological characterisation, a strong influence of fireworks on the physicochemical characteristics of PM10 and PM2.5 was observed. The concentrations of many species exhibited an increasing trend during the heavy-firework period, especially for K+, Mg2+ and Cr; the results of the non-sea-salt ions demonstrated an anthropogenic influence on K+ and Mg2+. Then, source apportionment was conducted by receptor models and peak analysis (PA). The total influence of the fireworks was quantified by positive matrix factorisation (PMF), showing that the fireworks contributed higher fractions (23.40% for PM10 and 29.66% for PM2.5) during the heavy-firework period than during the light-firework period (4.28% for PM10 and 7.18% for PM2.5). The profiles of the total fireworks obtained by two independent methods (PMF and peak analysis) were consistent, with higher abundances of K+, Al, Si, Ca and organic carbon (OC). Finally, the individual contributions of the direct and indirect impacts of fireworks were quantified by chemical mass balance (CMB). The percentage contributions of resuspended dust, biomass combustion and direct fireworks were 36.8 ± 8.37%, 14.1 ± 2.82% and 44.4 ± 8.26%, respectively, for PM10 and 34.9 ± 4.19%, 16.6 ± 3.05% and 52.5 ± 9.69%, respectively, for PM2.5, in terms of the total fireworks. The quantification of the total, direct and indirect impacts of fireworks in the ambient PM gives a original contribution for understanding the physicochemical characteristics and mechanisms of such high-intensity anthropogenic activities.

EEG-based driver fatigue detection using hybrid deep generic model.

PubMed

Phyo Phyo San; Sai Ho Ling; Rifai Chai; Tran, Yvonne; Craig, Ashley; Hung Nguyen

2016-08-01

Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.

40 CFR 1065.395 - Inertial PM balance verifications.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 32 2010-07-01 2010-07-01 false Inertial PM balance verifications... Inertial PM balance verifications. This section describes how to verify the performance of an inertial PM balance. (a) Independent verification. Have the balance manufacturer (or a representative approved by the...

40 CFR 1065.395 - Inertial PM balance verifications.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 33 2011-07-01 2011-07-01 false Inertial PM balance verifications... Inertial PM balance verifications. This section describes how to verify the performance of an inertial PM balance. (a) Independent verification. Have the balance manufacturer (or a representative approved by the...

Assessing the potential of surface-immobilized molecular logic machines for integration with solid state technology.

PubMed

Dunn, Katherine E; Trefzer, Martin A; Johnson, Steven; Tyrrell, Andy M

2016-08-01

Molecular computation with DNA has great potential for low power, highly parallel information processing in a biological or biochemical context. However, significant challenges remain for the field of DNA computation. New technology is needed to allow multiplexed label-free readout and to enable regulation of molecular state without addition of new DNA strands. These capabilities could be provided by hybrid bioelectronic systems in which biomolecular computing is integrated with conventional electronics through immobilization of DNA machines on the surface of electronic circuitry. Here we present a quantitative experimental analysis of a surface-immobilized OR gate made from DNA and driven by strand displacement. The purpose of our work is to examine the performance of a simple representative surface-immobilized DNA logic machine, to provide valuable information for future work on hybrid bioelectronic systems involving DNA devices. We used a quartz crystal microbalance to examine a DNA monolayer containing approximately 5×10(11)gatescm(-2), with an inter-gate separation of approximately 14nm, and we found that the ensemble of gates took approximately 6min to switch. The gates could be switched repeatedly, but the switching efficiency was significantly degraded on the second and subsequent cycles when the binding site for the input was near to the surface. Otherwise, the switching efficiency could be 80% or better, and the power dissipated by the ensemble of gates during switching was approximately 0.1nWcm(-2), which is orders of magnitude less than the power dissipated during switching of an equivalent array of transistors. We propose an architecture for hybrid DNA-electronic systems in which information can be stored and processed, either in series or in parallel, by a combination of molecular machines and conventional electronics. In this architecture, information can flow freely and in both directions between the solution-phase and the underlying electronics

Hybrid Interferometric/Dispersive Atomic Spectroscopy For Nuclear Materials Analysis

NASA Astrophysics Data System (ADS)

Morgan, Phyllis K.

Laser-induced breakdown spectroscopy (LIBS) is an optical emission spectroscopy technique that holds promise for detection and rapid analysis of elements relevant for nuclear safeguards and nonproliferation, including the measurement of isotope ratios. One important application of LIBS is the measurement of uranium enrichment (235U/238U), which requires high spectral resolution (e.g., 25 pm for the 424.437 nm U II line). Measuring uranium enrichment is important in nuclear nonproliferation and safeguards because the uranium highly enriched in the 235U isotope can be used to construct nuclear weapons. High-resolution dispersive spectrometers necessary for such measurements are typically bulky and expensive. A hybrid interferometric/dispersive spectrometer prototype, which consists of an inexpensive, compact Fabry-Perot etalon integrated with a low to moderate resolution Czerny-Turner spectrometer, was assembled for making high-resolution measurements of nuclear materials in a laboratory setting. To more fully take advantage of this low-cost, compact hybrid spectrometer, a mathematical reconstruction technique was developed to accurately reconstruct relative line strengths from complex spectral patterns with high resolution. Measurement of the mercury 313.1555/313.1844 nm doublet from a mercury-argon lamp yielded a spectral line intensity ratio of 0.682, which agrees well with an independent measurement by an echelle spectrometer and previously reported values. The hybrid instrument was used in LIBS measurements and achieved the resolution needed for isotopic selectivity of LIBS of uranium in ambient air. The samples used were a natural uranium foil (0.7% of 235U) and a uranium foil highly enriched in 235U to 93%. Both samples were provided by the Penn State University's Breazeale Nuclear Reactor. The enrichment of the uranium foils was verified using a high-purity germanium detector and dedicated software for multi-group spectral analysis. Uranium spectral line

CHANGES IN OPERATING PROCEDURES FOR AEROSOL CONCENTRATION UNIFORMITY FOR PM2.5 AND PM10 SAMPLER TESTING

EPA Science Inventory

This technical note documents changes in the standard operating procedures used at the Environmental Protection Agency's (U.S. EPA) aerosol testing wind tunnel facility for testing of particulate matter monitoring methods of PM2.5 and PM10. These changes are relative to the op...

Historical Trends in Pm2.5-Related Premature Mortality ...

EPA Pesticide Factsheets

Background: Air quality across the northern hemisphere over the past two decades has witnessed dramatic changes, with continuous improvement in developed countries in North America and Europe, but a contrasting sharp deterioration in developing regions of Asia. Objective: This study investigates the historical trend in the long-term exposure to PM2.5 and PM2.5-related premature mortality (PM2.5-mortality) and its response to changes in emission that occurred during 1990-2010 across the northern hemisphere. Implications for future trends in human exposure to air pollution in both developed and developing regions of the world are discussed. Methods: We employed the integrated exposure-response model developed by Health Effects Institute to estimate the PM2.5-mortality. The 1990-2010 annual-average PM2.5 concentrations were obtained from the simulations using WRF-CMAQ model. Emission mitigation efficiencies of SO2, NOx, NH3 and primary PM are estimated from the PM2.5-mortality responses to the emission variations. Results: Estimated PM2.5-mortalities in East Asia and South Asia increased by 21% and 85% respectively, from 866,000 and 578,000 in 1990, to 1,048,000 and 1,068,000 in 2010. PM2.5-mortalities in developed regions, i.e., Europe and high-income North America decreased substantially by 67% and 58% respectively. Conclusions: Over the past two decades, correlations between population and PM2.5 have become weaker in Europe and North America due to air pollu

A chronology of ratios between black smoke and PM10 and PM2.5 in the context of comparison of air pollution epidemiology concentration-response functions.

PubMed

Heal, Mathew R; Beverland, Iain J

2017-05-03

For many air pollution epidemiological studies in Europe, 'black smoke' (BS) was the only measurement available to quantify ambient particulate matter (PM), particularly for exposures prior to the mid-1990s when quantification via the PM 10 and/or PM 2.5 metrics was introduced. The aim of this work was to review historic BS and PM measurements to allow comparison of health concentration-response functions (CRF) derived using BS as the measure of exposure with CRFs derived using PM 10 or PM 2.5 . The literature was searched for quantitative information on measured ratios of BS:PM 10 , BS:PM 2.5 , and chemical composition of PM; with specific focus on the United Kingdom (UK) between 1970 and the early 2000s when BS measurements were discontinued. The average BS:PM 10 ratio in urban background air was just below unity at the start of the 1970s, decreased rapidly to ≈ 0.7 in the mid-1970s and to ≈ 0.5 at the end of the 1970s, with continued smaller declines in the 1980s, and was within the range 0.2-0.4 by the end of the 1990s. The limited data for the BS:PM 2.5 ratio suggest it equalled or exceeded unity at the start of the 1970s, declined to ≈ 0.7 by the end of the 1970s, with slower decline thereafter to a range 0.4-0.65 by the end of the 1990s. For an epidemiological study that presents a CRF BS value, the corresponding CRF PM10 value can be estimated as R BS:PM10  × CRF BS where R BS:PM10 is the BS:PM 10 concentration ratio, if the toxicity of PM 10 is assumed due only to the component quantified by a BS measurement. In the general case of some (but unknown) contribution of toxicity from non-BS components of PM 10 then CRF PM10  > R BS:PM10  × CRF BS , with CRF PM10 exceeding CRF BS if the toxicity of the other components in PM 10 is greater than the toxicity of the component to which the BS metric is sensitive. Similar analyses were applied to relationships between CRF PM2.5 and CRF BS . Application of this analysis to example

Design of Stand-Alone Hybrid Power Generation System at Brumbun Beach Tulungagung East Java

NASA Astrophysics Data System (ADS)

Rahmat, A. N.; Hidayat, M. N.; Ronilaya, F.; Setiawan, A.

2018-04-01

Indonesian government insists to optimize the use of renewable energy resources in electricity generation. One of the efforts is launching Independent Energy Village plan. This program aims to fulfill the need of electricity for isolated or remote villages in Indonesia. In order to support the penetration of renewable energy resources in electricity generation, a hybrid power generation system is developed. The simulation in this research is based on the availability of renewable energy resources in Brumbun beach, Tulungagung, East Java. Initially, the electricity was supplied through stand-alone electricity generations which are installed at each house. Hence, the use of electricity between 5 p.m. – 9 p.m. requires high operational costs. Based on the problem above, this research is conducted to design a stand-alone hybrid electricity generation system, which may consist of diesel, wind, and photovoltaic. The design is done by using HOMER software to optimize the use of electricity from renewable resources and to reduce the operation of diesel generation. The combination of renewable energy resources in electricity generation resulted in NPC of 44.680, COE of 0,268, and CO2 emissions of 0,038 % much lower than the use of diesel generator only.

Estimation of Surface Seawater Fugacity of Carbon Dioxide Using Satellite Data and Machine Learning

NASA Astrophysics Data System (ADS)

Jang, E.; Im, J.; Park, G.; Park, Y.

2016-12-01

The ocean controls the climate of Earth by absorbing and releasing CO2 through the carbon cycle. The amount of CO2 in the ocean has increased since the industrial revolution. High CO2 concentration in the ocean has a negative influence to marine organisms and reduces the ability of absorbing CO2 in the ocean. This study estimated surface seawater fugacity of CO2 (fCO2) in the East Sea of Korea using Geostationary Ocean Color Imager (GOCI) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, and Hybrid Coordinate Ocean Model (HYCOM) reanalysis data. GOCI is the world first geostationary ocean color observation satellite sensor, and it provides 8 images with 8 bands hourly per day from 9 am to 4 pm at 500m resolution. Two machine learning approaches (i.e., random forest and support vector regression) were used to model fCO2 in this study. While most of the existing studies used multiple linear regression to estimate the pressure of CO2 in the ocean, machine learning may handle more complex relationship between surface seawater fCO2 and ocean parameters in a dynamic spatiotemporal environment. Five ocean related parameters, colored dissolved organic matter (CDOM), chlorophyll-a (chla), sea surface temperature (SST), sea surface salinity (SSS), and mixed layer depth (MLD), were used as input variables. This study examined two schemes, one with GOCI-derived products and the other with MODIS-derived ones. Results show that random forest performed better than support vector regression regardless of satellite data used. The accuracy of GOCI-based estimation was higher than MODIS-based one, possibly thanks to the better spatiotemporal resolution of GOCI data. MLD was identified the most contributing parameter in estimating surface seawater fCO2 among the five ocean related parameters, which might be related with an active deep convection in the East Sea. The surface seawater fCO2 in summer was higher in general with some spatial variation than the other

Household air pollution and personal inhalation exposure to particles (TSP/PM2.5/PM1.0/PM0.25) in rural Shanxi, North China.

PubMed

Huang, Ye; Du, Wei; Chen, Yuanchen; Shen, Guofeng; Su, Shu; Lin, Nan; Shen, Huizhong; Zhu, Dan; Yuan, Chenyi; Duan, Yonghong; Liu, Junfeng; Li, Bengang; Tao, Shu

2017-12-01

Personal exposure to size-segregated particles among rural residents in Shanxi, China in summer, 2011 were investigated using portable carried samplers (N = 84). Household air pollution was simultaneously studied using stationary samplers in nine homes. Information on household fuel types, cooking activity, smoking behavior, kitchen ventilation conditions etc., were also collected and discussed. The study found that even in the summer period, the daily average concentrations of PM 2.5 and PM 1.0 in the kitchen were as high as 376 ± 573 and 288 ± 397 μg/m 3 (N = 6), that were nearly 3 times of 114 ± 81 and 97 ± 77 μg/m 3 in the bedroom (N = 8), and significantly higher than those of 64 ± 28 and 47 ± 21 μg/m 3 in the outdoor air (N = 6). The personal daily exposure to PM 2.5 and PM 1.0 were 98 ± 52 and 77 ± 47 μg/m 3 , respectively, that were lower than the concentrations in the kitchen but higher than the outdoor levels. The mass fractions of PM 2.5 in TSP were 90%, 72%, 65% and 68% on average in the kitchen, bedroom, outdoor air and personal inhalation exposure, respectively, and moreover, a majority of particles in PM 2.5 had diameters less than 1.0 μm. Calculated time-weighted average exposure based on indoor and outdoor air concentrations and time spent indoor and outdoor were positively correlated but, was ∼33% lower than the directly measured exposure. The daily exposure among those burning traditional solid fuels could be lower by ∼41% if the kitchen was equipped with an outdoor chimney, but was still 8-14% higher than those household using cleaning energies, like electricity and gas. With a ventilator in the kitchen, the exposure among the population using clean energies could be further reduced by 10-24%. Copyright © 2017 Elsevier Ltd. All rights reserved.

THE ROLE OF MICROVASCULAR THROMBOSIS IN PARTICULATE MATTER (PM) AND PM COMPONENT-INDUCED CARDIOVASCULAR EFFECTS: OXIDATIVE STRESS AS A MEDIATOR OF THROMBOSIS

EPA Science Inventory

Particulate matter (PM) exposure has been associated with increased plasma fibrinogen. We have found that Spontaneously hypertensive rats respond to PM by increasing fibrinogen and plasminogen activator inhibitor -1 at PM concentration that would cause minimal changes in healthy ...

Modeling Music Emotion Judgments Using Machine Learning Methods