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

Solving a Higgs optimization problem with quantum annealing for machine learning.

PubMed

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-18

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Solving a Higgs optimization problem with quantum annealing for machine learning

NASA Astrophysics Data System (ADS)

Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

2017-10-01

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

Ownership of Machine-Readable Bibliographic Data. Canadian Network Papers Number 5 = Propriete des Donnees Bibliographique Lisibles par Machine. Documents sur les Resaux Canadiens Numero 5.

ERIC Educational Resources Information Center

Duchesne, R. M.; And Others

Because of data ownership questions raised by the interchange and sharing of machine readable bibliographic data, this paper was prepared for the Bibliographic and Communications Network Committee of the National Library Advisory Board. Background information and definitions are followed by a review of the legal aspects relating to property and…

Implementing Machine Learning in the PCWG Tool

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

Clifton, Andrew; Ding, Yu; Stuart, Peter

The Power Curve Working Group (www.pcwg.org) is an ad-hoc industry-led group to investigate the performance of wind turbines in real-world conditions. As part of ongoing experience-sharing exercises, machine learning has been proposed as a possible way to predict turbine performance. This presentation provides some background information about machine learning and how it might be implemented in the PCWG exercises.

18. Photocopy of Photograph (From Bethlehem Shipbuilding Ltd., New York, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

18. Photocopy of Photograph (From Bethlehem Shipbuilding Ltd., New York, New York, 1919) C.1918 PHOTOGRAPH OF CONSTRUCTION WORK IN PROGRESS WITH MACHINE SHOP IN BACKGROUND - Union Iron Works Turbine Machine Shop, 2200 Webster Street, Alameda, Alameda County, CA

The New Possibilities from "Big Data" to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis.

PubMed

Kruse, Christian

2018-06-01

To review current practices and technologies within the scope of "Big Data" that can further our understanding of diabetes mellitus and osteoporosis from large volumes of data. "Big Data" techniques involving supervised machine learning, unsupervised machine learning, and deep learning image analysis are presented with examples of current literature. Supervised machine learning can allow us to better predict diabetes-induced osteoporosis and understand relative predictor importance of diabetes-affected bone tissue. Unsupervised machine learning can allow us to understand patterns in data between diabetic pathophysiology and altered bone metabolism. Image analysis using deep learning can allow us to be less dependent on surrogate predictors and use large volumes of images to classify diabetes-induced osteoporosis and predict future outcomes directly from images. "Big Data" techniques herald new possibilities to understand diabetes-induced osteoporosis and ascertain our current ability to classify, understand, and predict this condition.

Space languages

NASA Technical Reports Server (NTRS)

Hays, Dan

1987-01-01

Applications of linguistic principles to potential problems of human and machine communication in space settings are discussed. Variations in language among speakers of different backgrounds and change in language forms resulting from new experiences or reduced contact with other groups need to be considered in the design of intelligent machine systems.

Background Knowledge in Learning-Based Relation Extraction

ERIC Educational Resources Information Center

Do, Quang Xuan

2012-01-01

In this thesis, we study the importance of background knowledge in relation extraction systems. We not only demonstrate the benefits of leveraging background knowledge to improve the systems' performance but also propose a principled framework that allows one to effectively incorporate knowledge into statistical machine learning models for…

Man-Machine Communication in Remote Manipulation: Task-Oriented Supervisory Command Language (TOSC).

DTIC Science & Technology

1980-03-01

ORIENTED SUPERVISORY CONTROL SYSTEM METHODOLOGY 3-1 3.1 Overview 3-1 3.2 Background 3-3 3.2.1 General 3-3 3.2.2 Preliminary Principles of Command Language...Design 3-4 3.2.3 Preliminary Principles of Feedback Display Design 3-9 3.3 Man-Machine Communication Models 3-12 3.3.1 Background 3-12 3.3.2 Adapted...and feedback mode. The work ends with the presentation of a performance prediction model and a set of principles and guidelines, applicable to the

The Altar Machine in the Church Mother of Gangi (Palermo, Italy). Interpretation of the past uses, scientific investigation and preservation challenge

PubMed Central

2012-01-01

Background The aim of this work was to study the Altar Machine in the Church Mother of Gangi, a little town near Palermo (Italy) regarding the history, the technical manufacture, the constitutive materials and the state of preservation. The Altar Machine was dated back to the second half of the 18th century; it is constituted by carved and painted wood, a complex system of winch and pulleys allows move various statues and parts of the Machine in accordance with the baroque scenography machineries. Results The observation and survey of the mechanisms allowed formulate hypothesis on a more ancient mode of operation of the Altar Machine. Laboratory analysis revealed the presence of many superimposed layers constituted by several different materials (protein binders, siccative oils, natural terpene resins, shellac, calcium carbonate, gypsum, lead white, brass, zinc white, iron oxides) and different wood species employed for the original and restoration elements of the Machine. This is due to a continuous usage of the object that has got a demo-ethno-anthropological significance. Microclimate monitoring (relative humidity RH and temperature T) put in evidence that most of the data fall outside the tolerance intervals, i.e. the RH and T limits defined by the international standards. In particular, T values were generally high (out of the tolerance range) but they appeared to be quite constant; on the other hand RH values fell almost always inside the tolerance area but they often exhibited dangerous variations. Conclusions The characterization of the constitutive materials provided useful information both to support the dating of the Machine proposed by the inscription and to obtain a base of data for a possible conservation work. The microclimate monitoring put in evidence that the temperature and relative humidity values are not always suitable to correctly preserve the artefact. The careful in situ investigation confirmed an on-going climate induced damage to the Altar Machine that, associated to the deterioration caused by its usage, may have dramatic consequences on this unique and peculiar work of art. The results of this work will have potential implications in the near future regarding a probable conservation project on the Machine. PMID:22616574

Andy Hardin with 3-D printed engine part

NASA Image and Video Library

2015-06-22

ANDY HARDIN, A PROPULSION ENGINEER AT NASA'S MARSHALL SPACE FLIGHT CENTER IN HUNTSVILLE, ALABAMA, SHOWS A 3-D PRINTED ROCKET PART MADE WITH A SELECTIVE LASER MELTING MACHINE. PARTS FOR THE SPACE LAUNCH SYSTEM'S RS-25 ROCKET ENGINE ARE BEING MADE WITH THE MACHINE IN THE BACKGROUND

National machine guarding program: Part 1. Machine safeguarding practices in small metal fabrication businesses

PubMed Central

Yamin, Samuel C.; Brosseau, Lisa M.; Xi, Min; Gordon, Robert; Most, Ivan G.; Stanley, Rodney

2015-01-01

Background Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. Methods The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine‐related hazards in 221 business. Results Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. Conclusions The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. Am. J. Ind. Med. 58:1174–1183, 2015. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc. PMID:26332060

Teaching Machines and Programmed Instruction.

ERIC Educational Resources Information Center

Kay, Harry; And Others

The various devices used in programed instruction range from the simple linear programed book to branching and skip branching programs, adaptive teaching machines, and even complex computer based systems. In order to provide a background for the would-be programer, the essential principles of each of these devices is outlined. Different ideas of…

Availability of Vending Machines and School Stores in California Schools

ERIC Educational Resources Information Center

Cisse-Egbuonye, Nafissatou; Liles, Sandy; Schmitz, Katharine E.; Kassem, Nada; Irvin, Veronica L.; Hovell, Melbourne F.

2016-01-01

Background: This study examined the availability of foods sold in vending machines and school stores in United States public and private schools, and associations of availability with students' food purchases and consumption. Methods: Descriptive analyses, chi-square tests, and Spearman product-moment correlations were conducted on data collected…

78 FR 11627 - Certain Granular Polytetrafluoroethylene Resin From Italy: Rescission of Antidumping Duty...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-19

.... SUPPLEMENTARY INFORMATION: Background Pursuant to a request by Industrial Plastics and Machine, Inc... Revocation in Part, 77 FR 59168 (September 26, 2012). \\2\\ See Letter from Industrial Plastics and Machine... estimated antidumping duties required at the time of entry, or withdrawal from warehouse, for consumption in...

Machine Trades Curriculum Guide. Michigan Trade and Industrial Education.

ERIC Educational Resources Information Center

Michigan State Univ., East Lansing. Coll. of Agriculture and Natural Resources Education Inst.

This task-based curriculum guide is intended to help secondary teachers provide relevant training for an entry-level job in machine trades. Introductory materials include background information on trade and industrial education and program goals and safety information. Descriptions follow of the construction trades program, vocational cooperative…

Machinability of lithium disilicate glass ceramic in in vitro dental diamond bur adjusting process.

PubMed

Song, Xiao-Fei; Ren, Hai-Tao; Yin, Ling

2016-01-01

Esthetic high-strength lithium disilicate glass ceramics (LDGC) are used for monolithic crowns and bridges produced in dental CAD/CAM and oral adjusting processes, which machinability affects the restorative quality. A machinability study has been made in the simulated oral clinical machining of LDGC with a dental handpiece and diamond burs, regarding the diamond tool wear and chip control, machining forces and energy, surface finish and integrity. Machining forces, speeds and energy in in vitro dental adjusting of LDGC were measured by a high-speed data acquisition and force sensor system. Machined LDGC surfaces were assessed using three-dimensional non-contact chromatic confocal optical profilometry and scanning electron microscopy (SEM). Diamond bur morphology and LDGC chip shapes were also examined using SEM. Minimum tool wear but significant LDGC chip accumulations were found. Machining forces and energy significantly depended on machining conditions (p<0.05) and were significantly higher than other glass ceramics (p<0.05). Machining speeds dropped more rapidly with increased removal rates than other glass ceramics (p<0.05). Two material machinability indices associated with the hardness, Young's modulus and fracture toughness were derived based on the normal force-removal rate relations, which ranked LDGC the most difficult to machine among glass ceramics. Surface roughness for machined LDGC was comparable for other glass ceramics. The removal mechanisms of LDGC were dominated by penetration-induced brittle fracture and shear-induced plastic deformation. Unlike most other glass ceramics, distinct intergranular and transgranular fractures of lithium disilicate crystals were found in LDGC. This research provides the fundamental data for dental clinicians on the machinability of LDGC in intraoral adjustments. Copyright © 2015 Elsevier Ltd. All rights reserved.

Studying depression using imaging and machine learning methods.

PubMed

Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

2016-01-01

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

What is the machine learning?

NASA Astrophysics Data System (ADS)

Chang, Spencer; Cohen, Timothy; Ostdiek, Bryan

2018-03-01

Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables—aided by physical intuition—that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable's discriminating power. Planing also allows the investigation of the linear versus nonlinear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn.

Trends of Occupational Fatalities Involving Machines, United States, 1992–2010

PubMed Central

Marsh, Suzanne M.; Fosbroke, David E.

2016-01-01

Background This paper describes trends of occupational machine-related fatalities from 1992–2010. We examine temporal patterns by worker demographics, machine types (e.g., stationary, mobile), and industries. Methods We analyzed fatalities from Census of Fatal Occupational Injuries data provided by the Bureau of Labor Statistics to the National Institute for Occupational Safety and Health. We used injury source to identify machine-related incidents and Poisson regression to assess trends over the 19-year period. Results There was an average annual decrease of 2.8% in overall machine-related fatality rates from 1992 through 2010. Mobile machine-related fatality rates decreased an average of 2.6% annually and stationary machine-related rates decreased an average of 3.5% annually. Groups that continued to be at high risk included older workers; self-employed; and workers in agriculture/forestry/fishing, construction, and mining. Conclusion Addressing dangers posed by tractors, excavators, and other mobile machines needs to continue. High-risk worker groups should receive targeted information on machine safety. PMID:26358658

Design Guidelines and Criteria for User/Operator Transactions with Battlefield Automated Systems. Volume 5. Background Literature

DTIC Science & Technology

1981-02-01

the machine . ARI’s efforts in this area focus on human perfor- mance problems related to interactions with command and control centers, and on issues...improvement of the user- machine interface. Lacking consistent design principles, current practice results in a fragmented and unsystematic approach to system...complexity in the user- machine interface of BAS, ARI supported this effort for develop- me:nt of an online language for Army tactical intelligence

Induced electric fields in workers near low-frequency induction heating machines.

PubMed

Kos, Bor; Valič, Blaž; Kotnik, Tadej; Gajšek, Peter

2014-04-01

Published data on occupational exposure to induction heating equipment are scarce, particularly in terms of induced quantities in the human body. This article provides some additional information by investigating exposure to two such machines-an induction furnace and an induction hardening machine. Additionally, a spatial averaging algorithm for measured fields we developed in a previous publication is tested on new data. The human model was positioned at distances where measured values of magnetic flux density were above the reference levels. All human exposure was below the basic restriction-the lower bound of the 0.1 top percentile induced electric field in the body of a worker was 0.193 V/m at 30 cm from the induction furnace. © 2013 Wiley Periodicals, Inc.

Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds.

PubMed

Gao, Mengxuan; Igata, Hideyoshi; Takeuchi, Aoi; Sato, Kaoru; Ikegaya, Yuji

2017-02-01

Various biological factors have been implicated in convulsive seizures, involving side effects of drugs. For the preclinical safety assessment of drug development, it is difficult to predict seizure-inducing side effects. Here, we introduced a machine learning-based in vitro system designed to detect seizure-inducing side effects. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices, while 14 drugs were bath-perfused at 5 different concentrations each. For each experimental condition, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs) that induced seizure-like events and identified diphenhydramine, enoxacin, strychnine and theophylline as "seizure-inducing" drugs, which indeed were reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to detect the seizure-inducing side effects of preclinical drugs. Copyright © 2017 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

MRTD: man versus machine

NASA Astrophysics Data System (ADS)

van Rheenen, Arthur D.; Taule, Petter; Thomassen, Jan Brede; Madsen, Eirik Blix

2018-04-01

We present Minimum-Resolvable Temperature Difference (MRTD) curves obtained by letting an ensemble of observers judge how many of the six four-bar patterns they can "see" in a set of images taken with different bar-to-background contrasts. The same images are analyzed using elemental signal analysis algorithms and machine-analysis based MRTD curves are obtained. We show that by adjusting the minimum required signal-to-noise ratio the machine-based MRTDs are very similar to the ones obtained with the help of the human observers.

The Armys M-1 Abrams, M-2/M-3 Bradley, and M-1126 Stryker: Background and Issues for Congress

DTIC Science & Technology

2016-04-05

smoothbore gun 1 x coaxial mounted 7.62 mm M-240 machine gun 1 x roof mounted 12.7 mm M-2 HB machine gun 1 x roof mounted 7.62 mm M-240 machine gun 12...Bradley Fighting Vehicle Table 2. Selected Basic Characteristics— M2 /3-A2 Armament 1 x turret mounted M-242 25mm “Bushmaster” chain gun 2 x turret...mounted TOW anti-tank missiles 1 x coaxial mounted 7.62 mm M-240C machine gun 8 x turret mounted smoke grenade launchers Crew M-2: 3 crew, 6

[Machine Learning-based Prediction of Seizure-inducing Action as an Adverse Drug Effect].

PubMed

Gao, Mengxuan; Sato, Motoshige; Ikegaya, Yuji

2018-01-01

　During the preclinical research period of drug development, animal testing is widely used to help screen out a drug's dangerous side effects. However, it remains difficult to predict side effects within the central nervous system. Here, we introduce a machine learning-based in vitro system designed to detect seizure-inducing side effects before clinical trial. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices that were bath-perfused with each of 14 different drugs, and at 5 different concentrations of each drug. For each of these experimental conditions, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs) that induced seizure-like events, and identified diphenhydramine, enoxacin, strychnine and theophylline as "seizure-inducing" drugs, which have indeed been reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to pre-clinically detect seizure-inducing side effects of drugs.

18. VIEW OF CRUDE ORE BINS FROM WEST. WEST CRUDE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

18. VIEW OF CRUDE ORE BINS FROM WEST. WEST CRUDE ORE BIN AND TRESTLE FROM TWO JOHNS TRAMLINE TO SOUTH, CRUDE ORE BIN IN FOREGROUND. MACHINE SHOP IN BACKGROUND. THE TRAM TO PORTLAND PASSED TO NORTH OF MACHINE SHOP. - Bald Mountain Gold Mill, Nevada Gulch at head of False Bottom Creek, Lead, Lawrence County, SD

9. GENERAL INTERIOR VIEW OF THE VERTICAL FURNACE BUILDING (PART ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

9. GENERAL INTERIOR VIEW OF THE VERTICAL FURNACE BUILDING (PART OF MACHINE SHOP No. 2). TWO FURNACES, WITH THEIR SUPPORT FRAMEWORK, ARE VISIBLE TO THE RIGHT. THE TALL STRUCTURE IN THE CENTER TOWARD THE BACKGROUND IS THE VERTICAL QUENCH TOWER. - U.S. Steel Homestead Works, Machine Shop No. 2, Along Monongahela River, Homestead, Allegheny County, PA

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation

PubMed Central

O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-01-01

Background Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. Objective The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. Methods We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. Results With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. Conclusions The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. PMID:28778851

Center background shows two fortyhorsepower directcurrent electric motors installed in ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Center background shows two forty-horsepower direct-current electric motors installed in 1904 to provide power to two drive shafts for first floor machine shops. - Thomas A. Edison Laboratories, Building No. 5, Main Street & Lakeside Avenue, West Orange, Essex County, NJ

Induction machine

DOEpatents

Owen, Whitney H.

1980-01-01

A polyphase rotary induction machine for use as a motor or generator utilizing a single rotor assembly having two series connected sets of rotor windings, a first stator winding disposed around the first rotor winding and means for controlling the current induced in one set of the rotor windings compared to the current induced in the other set of the rotor windings. The rotor windings may be wound rotor windings or squirrel cage windings.

The in-situ 3D measurement system combined with CNC machine tools

NASA Astrophysics Data System (ADS)

Zhao, Huijie; Jiang, Hongzhi; Li, Xudong; Sui, Shaochun; Tang, Limin; Liang, Xiaoyue; Diao, Xiaochun; Dai, Jiliang

2013-06-01

With the development of manufacturing industry, the in-situ 3D measurement for the machining workpieces in CNC machine tools is regarded as the new trend of efficient measurement. We introduce a 3D measurement system based on the stereovision and phase-shifting method combined with CNC machine tools, which can measure 3D profile of the machining workpieces between the key machining processes. The measurement system utilizes the method of high dynamic range fringe acquisition to solve the problem of saturation induced by specular lights reflected from shiny surfaces such as aluminum alloy workpiece or titanium alloy workpiece. We measured two workpieces of aluminum alloy on the CNC machine tools to demonstrate the effectiveness of the developed measurement system.

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

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.

European public deliberation on brain machine interface technology: five convergence seminars.

PubMed

Jebari, Karim; Hansson, Sven-Ove

2013-09-01

We present a novel procedure to engage the public in ethical deliberations on the potential impacts of brain machine interface technology. We call this procedure a convergence seminar, a form of scenario-based group discussion that is founded on the idea of hypothetical retrospection. The theoretical background of this procedure and the results of five seminars are presented.

45. WEST TO CIRCA 1900 SHEET METAL SHEAR, THE MACHINE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

45. WEST TO CIRCA 1900 SHEET METAL SHEAR, THE MACHINE USED TO CUT SHEET METAL USED IN WINDMILLS AND WATER TANKS. IN THE BACKGROUND IS THE INTERIOR WEST WALL OF THE FACTORY, ITS SHELVES BEARING WATER PUMPS, PARTS FOR PUMPS AND WATER SUPPLY EQUIPMENT, AND NEW OLD STOCK MERCHANDISE. - Kregel Windmill Company Factory, 1416 Central Avenue, Nebraska City, Otoe County, NE

LED light design method for high contrast and uniform illumination imaging in machine vision.

PubMed

Wu, Xiaojun; Gao, Guangming

2018-03-01

In machine vision, illumination is very critical to determine the complexity of the inspection algorithms. Proper lights can obtain clear and sharp images with the highest contrast and low noise between the interested object and the background, which is conducive to the target being located, measured, or inspected. Contrary to the empirically based trial-and-error convention to select the off-the-shelf LED light in machine vision, an optimization algorithm for LED light design is proposed in this paper. It is composed of the contrast optimization modeling and the uniform illumination technology for non-normal incidence (UINI). The contrast optimization model is built based on the surface reflection characteristics, e.g., the roughness, the reflective index, and light direction, etc., to maximize the contrast between the features of interest and the background. The UINI can keep the uniformity of the optimized lighting by the contrast optimization model. The simulation and experimental results demonstrate that the optimization algorithm is effective and suitable to produce images with the highest contrast and uniformity, which is very inspirational to the design of LED illumination systems in machine vision.

Machining-induced surface transformations of magnesium alloys to enhance corrosion resistance in human-like environment

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

Bruschi, Stefania; Bertolini, Rachele; Ghiotti, Andrea

We report that magnesium alloys are becoming increasingly attractive for producing temporary prosthetic devices thanks to their bioresorbable characteristics in human body. However, their poor corrosion resistance to body fluids seriously limits their applicability. In this work, machining-induced surface transformations are explored as means to enhance corrosion resistance of AZ31 magnesium alloy. Surface characteristics including topography, residual stresses, wettability, microstructures and depth of transformed layer, were analysed and correlated to in-vitro corrosion resistance. Results showed that cryogenic machining at low feed provided the most promising corrosion reduction. Finally, thorough physical characterizations gave fundamental insights into possible drivers for this enhancedmore » resistance.« less

Machining-induced surface transformations of magnesium alloys to enhance corrosion resistance in human-like environment

DOE PAGES

Bruschi, Stefania; Bertolini, Rachele; Ghiotti, Andrea; ...

2018-04-22

We report that magnesium alloys are becoming increasingly attractive for producing temporary prosthetic devices thanks to their bioresorbable characteristics in human body. However, their poor corrosion resistance to body fluids seriously limits their applicability. In this work, machining-induced surface transformations are explored as means to enhance corrosion resistance of AZ31 magnesium alloy. Surface characteristics including topography, residual stresses, wettability, microstructures and depth of transformed layer, were analysed and correlated to in-vitro corrosion resistance. Results showed that cryogenic machining at low feed provided the most promising corrosion reduction. Finally, thorough physical characterizations gave fundamental insights into possible drivers for this enhancedmore » resistance.« less

Molecular machines open cell membranes

NASA Astrophysics Data System (ADS)

García-López, Víctor; Chen, Fang; Nilewski, Lizanne G.; Duret, Guillaume; Aliyan, Amir; Kolomeisky, Anatoly B.; Robinson, Jacob T.; Wang, Gufeng; Pal, Robert; Tour, James M.

2017-08-01

Beyond the more common chemical delivery strategies, several physical techniques are used to open the lipid bilayers of cellular membranes. These include using electric and magnetic fields, temperature, ultrasound or light to introduce compounds into cells, to release molecular species from cells or to selectively induce programmed cell death (apoptosis) or uncontrolled cell death (necrosis). More recently, molecular motors and switches that can change their conformation in a controlled manner in response to external stimuli have been used to produce mechanical actions on tissue for biomedical applications. Here we show that molecular machines can drill through cellular bilayers using their molecular-scale actuation, specifically nanomechanical action. Upon physical adsorption of the molecular motors onto lipid bilayers and subsequent activation of the motors using ultraviolet light, holes are drilled in the cell membranes. We designed molecular motors and complementary experimental protocols that use nanomechanical action to induce the diffusion of chemical species out of synthetic vesicles, to enhance the diffusion of traceable molecular machines into and within live cells, to induce necrosis and to introduce chemical species into live cells. We also show that, by using molecular machines that bear short peptide addends, nanomechanical action can selectively target specific cell-surface recognition sites. Beyond the in vitro applications demonstrated here, we expect that molecular machines could also be used in vivo, especially as their design progresses to allow two-photon, near-infrared and radio-frequency activation.

Molecular machines open cell membranes.

PubMed

García-López, Víctor; Chen, Fang; Nilewski, Lizanne G; Duret, Guillaume; Aliyan, Amir; Kolomeisky, Anatoly B; Robinson, Jacob T; Wang, Gufeng; Pal, Robert; Tour, James M

2017-08-30

Beyond the more common chemical delivery strategies, several physical techniques are used to open the lipid bilayers of cellular membranes. These include using electric and magnetic fields, temperature, ultrasound or light to introduce compounds into cells, to release molecular species from cells or to selectively induce programmed cell death (apoptosis) or uncontrolled cell death (necrosis). More recently, molecular motors and switches that can change their conformation in a controlled manner in response to external stimuli have been used to produce mechanical actions on tissue for biomedical applications. Here we show that molecular machines can drill through cellular bilayers using their molecular-scale actuation, specifically nanomechanical action. Upon physical adsorption of the molecular motors onto lipid bilayers and subsequent activation of the motors using ultraviolet light, holes are drilled in the cell membranes. We designed molecular motors and complementary experimental protocols that use nanomechanical action to induce the diffusion of chemical species out of synthetic vesicles, to enhance the diffusion of traceable molecular machines into and within live cells, to induce necrosis and to introduce chemical species into live cells. We also show that, by using molecular machines that bear short peptide addends, nanomechanical action can selectively target specific cell-surface recognition sites. Beyond the in vitro applications demonstrated here, we expect that molecular machines could also be used in vivo, especially as their design progresses to allow two-photon, near-infrared and radio-frequency activation.

The Use of Point-of-Sale Machines in School Cafeterias as a Method of Parental Influence over Child Lunch Food Choices

ERIC Educational Resources Information Center

Andrepont, Emmy; Cullen, Karen W.; Taylor, Wendell C.

2011-01-01

Background: Computerized point-of-sale (POS) machine software that allows parents to place restrictions on their child's school meal accounts is available. Parents could restrict specific foods (e.g., chips), identify specific days the child can purchase extra foods, or set monetary limits. This descriptive study examines the use of parental…

Bayesian analysis of energy and count rate data for detection of low count rate radioactive sources

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

Klumpp, John

We propose a radiation detection system which generates its own discrete sampling distribution based on past measurements of background. The advantage to this approach is that it can take into account variations in background with respect to time, location, energy spectra, detector-specific characteristics (i.e. different efficiencies at different count rates and energies), etc. This would therefore be a 'machine learning' approach, in which the algorithm updates and improves its characterization of background over time. The system would have a 'learning mode,' in which it measures and analyzes background count rates, and a 'detection mode,' in which it compares measurements frommore » an unknown source against its unique background distribution. By characterizing and accounting for variations in the background, general purpose radiation detectors can be improved with little or no increase in cost. The statistical and computational techniques to perform this kind of analysis have already been developed. The necessary signal analysis can be accomplished using existing Bayesian algorithms which account for multiple channels, multiple detectors, and multiple time intervals. Furthermore, Bayesian machine-learning techniques have already been developed which, with trivial modifications, can generate appropriate decision thresholds based on the comparison of new measurements against a nonparametric sampling distribution. (authors)« less

14. View to the east up the Sugar River. The ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

14. View to the east up the Sugar River. The 1920 enclosed wooden footbridge connected the Chain Machine Building to the company's power plant, pattern shop, and foundry. The Sullivan Machinery Co. Erecting Shop and Machine Shops are in the center of the photo, and the Baltimore truss bridge is visible in the background. - Sullivan Machinery Company, Main Street between Pearl & Water Streets, Claremont, Sullivan County, NH

Study on Damage Evaluation and Machinability of UD-CFRP for the Orthogonal Cutting Operation Using Scanning Acoustic Microscopy and the Finite Element Method.

PubMed

Wang, Dongyao; He, Xiaodong; Xu, Zhonghai; Jiao, Weicheng; Yang, Fan; Jiang, Long; Li, Linlin; Liu, Wenbo; Wang, Rongguo

2017-02-20

Owing to high specific strength and designability, unidirectional carbon fiber reinforced polymer (UD-CFRP) has been utilized in numerous fields to replace conventional metal materials. Post machining processes are always required for UD-CFRP to achieve dimensional tolerance and assembly specifications. Due to inhomogeneity and anisotropy, UD-CFRP differs greatly from metal materials in machining and failure mechanism. To improve the efficiency and avoid machining-induced damage, this paper undertook to study the correlations between cutting parameters, fiber orientation angle, cutting forces, and cutting-induced damage for UD-CFRP laminate. Scanning acoustic microscopy (SAM) was employed and one-/two-dimensional damage factors were then created to quantitatively characterize the damage of the laminate workpieces. According to the 3D Hashin's criteria a numerical model was further proposed in terms of the finite element method (FEM). A good agreement between simulation and experimental results was validated for the prediction and structural optimization of the UD-CFRP.

Study on Damage Evaluation and Machinability of UD-CFRP for the Orthogonal Cutting Operation Using Scanning Acoustic Microscopy and the Finite Element Method

PubMed Central

Wang, Dongyao; He, Xiaodong; Xu, Zhonghai; Jiao, Weicheng; Yang, Fan; Jiang, Long; Li, Linlin; Liu, Wenbo; Wang, Rongguo

2017-01-01

Owing to high specific strength and designability, unidirectional carbon fiber reinforced polymer (UD-CFRP) has been utilized in numerous fields to replace conventional metal materials. Post machining processes are always required for UD-CFRP to achieve dimensional tolerance and assembly specifications. Due to inhomogeneity and anisotropy, UD-CFRP differs greatly from metal materials in machining and failure mechanism. To improve the efficiency and avoid machining-induced damage, this paper undertook to study the correlations between cutting parameters, fiber orientation angle, cutting forces, and cutting-induced damage for UD-CFRP laminate. Scanning acoustic microscopy (SAM) was employed and one-/two-dimensional damage factors were then created to quantitatively characterize the damage of the laminate workpieces. According to the 3D Hashin’s criteria a numerical model was further proposed in terms of the finite element method (FEM). A good agreement between simulation and experimental results was validated for the prediction and structural optimization of the UD-CFRP. PMID:28772565

High-pressure microscopy for tracking dynamic properties of molecular machines.

PubMed

Nishiyama, Masayoshi

2017-12-01

High-pressure microscopy is one of the powerful techniques to visualize the effects of hydrostatic pressures on research targets. It could be used for monitoring the pressure-induced changes in the structure and function of molecular machines in vitro and in vivo. This review focuses on the dynamic properties of the assemblies and machines, analyzed by means of high-pressure microscopy measurement. We developed a high-pressure microscope that is optimized both for the best image formation and for the stability to hydrostatic pressure up to 150 MPa. Application of pressure could change polymerization and depolymerization processes of the microtubule cytoskeleton, suggesting a modulation of the intermolecular interaction between tubulin molecules. A novel motility assay demonstrated that high hydrostatic pressure induces counterclockwise (CCW) to clockwise (CW) reversals of the Escherichia coli flagellar motor. The present techniques could be extended to study how molecular machines in complicated systems respond to mechanical stimuli. Copyright © 2017 Elsevier B.V. All rights reserved.

Laser Induced Damage of Potassium Dihydrogen Phosphate (KDP) Optical Crystal Machined by Water Dissolution Ultra-Precision Polishing Method

PubMed Central

Gao, Hang; Wang, Xu; Guo, Dongming; Liu, Ziyuan

2018-01-01

Laser induced damage threshold (LIDT) is an important optical indicator for nonlinear Potassium Dihydrogen Phosphate (KDP) crystal used in high power laser systems. In this study, KDP optical crystals are initially machined with single point diamond turning (SPDT), followed by water dissolution ultra-precision polishing (WDUP) and then tested with 355 nm nanosecond pulsed-lasers. Power spectral density (PSD) analysis shows that WDUP process eliminates the laser-detrimental spatial frequencies band of micro-waviness on SPDT machined surface and consequently decreases its modulation effect on the laser beams. The laser test results show that LIDT of WDUP machined crystal improves and its stability has a significant increase by 72.1% compared with that of SPDT. Moreover, a subsequent ultrasonic assisted solvent cleaning process is suggested to have a positive effect on the laser performance of machined KDP crystal. Damage crater investigation indicates that the damage morphologies exhibit highly thermal explosion features of melted cores and brittle fractures of periphery material, which can be described with the classic thermal explosion model. The comparison result demonstrates that damage mechanisms for SPDT and WDUP machined crystal are the same and WDUP process reveals the real bulk laser resistance of KDP optical crystal by removing the micro-waviness and subsurface damage on SPDT machined surface. This improvement of WDUP method makes the LIDT more accurate and will be beneficial to the laser performance of KDP crystal. PMID:29534032

Reconstruction of Laser-Induced Surface Topography from Electron Backscatter Diffraction Patterns.

PubMed

Callahan, Patrick G; Echlin, McLean P; Pollock, Tresa M; De Graef, Marc

2017-08-01

We demonstrate that the surface topography of a sample can be reconstructed from electron backscatter diffraction (EBSD) patterns collected with a commercial EBSD system. This technique combines the location of the maximum background intensity with a correction from Monte Carlo simulations to determine the local surface normals at each point in an EBSD scan. A surface height map is then reconstructed from the local surface normals. In this study, a Ni sample was machined with a femtosecond laser, which causes the formation of a laser-induced periodic surface structure (LIPSS). The topography of the LIPSS was analyzed using atomic force microscopy (AFM) and reconstructions from EBSD patterns collected at 5 and 20 kV. The LIPSS consisted of a combination of low frequency waviness due to curtaining and high frequency ridges. The morphology of the reconstructed low frequency waviness and high frequency ridges matched the AFM data. The reconstruction technique does not require any modification to existing EBSD systems and so can be particularly useful for measuring topography and its evolution during in situ experiments.

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.

Means and method of balancing multi-cylinder reciprocating machines

DOEpatents

Corey, John A.; Walsh, Michael M.

1985-01-01

A virtual balancing axis arrangement is described for multi-cylinder reciprocating piston machines for effectively balancing out imbalanced forces and minimizing residual imbalance moments acting on the crankshaft of such machines without requiring the use of additional parallel-arrayed balancing shafts or complex and expensive gear arrangements. The novel virtual balancing axis arrangement is capable of being designed into multi-cylinder reciprocating piston and crankshaft machines for substantially reducing vibrations induced during operation of such machines with only minimal number of additional component parts. Some of the required component parts may be available from parts already required for operation of auxiliary equipment, such as oil and water pumps used in certain types of reciprocating piston and crankshaft machine so that by appropriate location and dimensioning in accordance with the teachings of the invention, the virtual balancing axis arrangement can be built into the machine at little or no additional cost.

17. Baltimore through truss steel bridge (1905), built by the ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

17. Baltimore through truss steel bridge (1905), built by the American Bridge Company. The bridge is 15 to 20 feet wide, with a wooden deck, and connects the Sullivan Machine Co. with the Foundry. The enclosed bridge in the background was constructed ca. 1920, and connects the Chain Machine Building with its power plant, foundry, and pattern shop. - Sullivan Machinery Company, Main Street between Pearl & Water Streets, Claremont, Sullivan County, NH

Analysis of Adhesively Bonded Ceramics Using an Asymmetric Wedge Test

DTIC Science & Technology

2008-12-01

4 Figure 2. Average crack ...flexure specimen. The flaw, indicated by the white arrow, is a subsurface semi-elliptical crack induced by surface machining damage...strength-limiting orthogonal surface machining crack in an alumina flexure specimen coated with a single layer of film adhesive. The white arrow

Cutting tool form compensation system and method

DOEpatents

Barkman, W.E.; Babelay, E.F. Jr.; Klages, E.J.

1993-10-19

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation utilizes a camera and a vision computer for gathering information at a preselected stage of a machining operation relating to the actual shape and size of the cutting edge of the cutting tool and for altering the preprogrammed path in accordance with detected variations between the actual size and shape of the cutting edge and an assumed size and shape of the cutting edge. The camera obtains an image of the cutting tool against a background so that the cutting tool and background possess contrasting light intensities, and the vision computer utilizes the contrasting light intensities of the image to locate points therein which correspond to points along the actual cutting edge. Following a series of computations involving the determining of a tool center from the points identified along the tool edge, the results of the computations are fed to the controller where the preprogrammed path is altered as aforedescribed. 9 figures.

Cutting tool form compensaton system and method

DOEpatents

Barkman, William E.; Babelay, Jr., Edwin F.; Klages, Edward J.

1993-01-01

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation utilizes a camera and a vision computer for gathering information at a preselected stage of a machining operation relating to the actual shape and size of the cutting edge of the cutting tool and for altering the preprogrammed path in accordance with detected variations between the actual size and shape of the cutting edge and an assumed size and shape of the cutting edge. The camera obtains an image of the cutting tool against a background so that the cutting tool and background possess contrasting light intensities, and the vision computer utilizes the contrasting light intensities of the image to locate points therein which correspond to points along the actual cutting edge. Following a series of computations involving the determining of a tool center from the points identified along the tool edge, the results of the computations are fed to the controller where the preprogrammed path is altered as aforedescribed.

Comparison of ultrasonic energy expenditures and corneal endothelial cell density reductions during modulated and non-modulated phacoemulsification.

PubMed

Davison, James A

2007-01-01

To compare the Legacy 20000 Advantec continuous and Infiniti hyperpulse modes (Alcon Laboratories, Fort Worth, TX) with respect to average power, machine-measured phacoemulsification time, total stopwatch real time spent within the phacoemulsification process, balanced salt solution (BSS) volume, and corneal endothelial cell density losses. A background study was done of consecutive patients operated on with the Legacy (n = 60) and Infiniti (n = 40) machines programmed with identical parameters and using the continuous mode only. A primary study of another set of consecutive cases was operated on using the Legacy (n = 87) and Infiniti (n = 94) with the same parameters, but using the hyperpulse mode during quadrant removal with the Infiniti. Measurements for each set included average power and phacoemulsification time with corneal endothelial cell densities, BSS volume, and time spent in the phacoemulsification process. Similarities were found in the background study for average power percent and average minutes of phacoemulsification time. In the primary study, similarities were found for total minutes in the phacoemulsification process, BSS usage, and ECD losses, and differences were found for average power percent (P< .001) and machine-measured phacoemulsification minutes (P< .001). The Legacy and Infiniti performed similarly in continuous mode. With the Infiniti hyperpulse mode, a total ultrasonic energy reduction of 66% was noted. The machines required the same amount of total stopwatch measured time to accomplish phacoemulsification and produced the same 5% corneal endothelial cell loss. Therefore, clinically, these two machines behave in a comparable manner relative to safety and effectiveness.

Proton Therapy

MedlinePlus

... effects of the treatment. top of page What equipment is used? Proton beam therapy uses special machines, ... tumor cells. top of page Who operates the equipment? With backgrounds in mechanical, electrical, software, hardware and ...

Design of Human-Machine Interface and altering of pelvic obliquity with RGR Trainer.

PubMed

Pietrusinski, Maciej; Unluhisarcikli, Ozer; Mavroidis, Constantinos; Cajigas, Iahn; Bonato, Paolo

2011-01-01

The Robotic Gait Rehabilitation (RGR) Trainer targets secondary gait deviations in stroke survivors undergoing rehabilitation. Using an impedance control strategy and a linear electromagnetic actuator, the device generates a force field to control pelvic obliquity through a Human-Machine Interface (i.e. a lower body exoskeleton). Herein we describe the design of the RGR Trainer Human-Machine Interface (HMI) and we demonstrate the system's ability to alter the pattern of movement of the pelvis during gait in a healthy subject. Results are shown for experiments during which we induced hip-hiking - in healthy subjects. Our findings indicate that the RGR Trainer has the ability of affecting pelvic obliquity during gait. Furthermore, we provide preliminary evidence of short-term retention of the modified pelvic obliquity pattern induced by the RGR Trainer. © 2011 IEEE

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.

Design of Human – Machine Interface and Altering of Pelvic Obliquity with RGR Trainer

PubMed Central

Pietrusinski, Maciej; Unluhisarcikli, Ozer; Mavroidis, Constantinos; Cajigas, Iahn; Bonato, Paolo

2012-01-01

The Robotic Gait Rehabilitation (RGR) Trainer targets secondary gait deviations in stroke survivors undergoing rehabilitation. Using an impedance control strategy and a linear electromagnetic actuator, the device generates a force field to control pelvic obliquity through a Human-Machine Interface (i.e. a lower body exoskeleton). Herein we describe the design of the RGR Trainer Human-Machine Interface (HMI) and we demonstrate the system’s ability to alter the pattern of movement of the pelvis during gait in a healthy subject. Results are shown for experiments during which we induced hip-hiking – in healthy subjects. Our findings indicate that the RGR Trainer has the ability of affecting pelvic obliquity during gait. Furthermore, we provide preliminary evidence of short-term retention of the modified pelvic obliquity pattern induced by the RGR Trainer. PMID:22275693

Analysis of surface integrity in machining of AISI 304 stainless steel under various cooling and cutting conditions

NASA Astrophysics Data System (ADS)

Klocke, F.; Döbbeler, B.; Lung, S.; Seelbach, T.; Jawahir, I. S.

2018-05-01

Recent studies have shown that machining under specific cooling and cutting conditions can be used to induce a nanocrystalline surface layer in the workspiece. This layer has beneficial properties, such as improved fatigue strength, wear resistance and tribological behavior. In machining, a promising approach for achieving grain refinement in the surface layer is the application of cryogenic cooling. The aim is to use the last step of the machining operation to induce the desired surface quality to save time-consuming and expensive post machining surface treatments. The material used in this study was AISI 304 stainless steel. This austenitic steel suffers from low yield strength that limits its technological applications. In this paper, liquid nitrogen (LN2) as cryogenic coolant, as well as minimum quantity lubrication (MQL), was applied and investigated. As a reference, conventional flood cooling was examined. Besides the cooling conditions, the feed rate was varied in four steps. A large rounded cutting edge radius and finishing cutting parameters were chosen to increase the mechanical load on the machined surface. The surface integrity was evaluated at both, the microstructural and the topographical levels. After turning experiments, a detailed analysis of the microstructure was carried out including the imaging of the surface layer and hardness measurements at varying depths within the machined layer. Along with microstructural investigations, different topological aspects, e.g., the surface roughness, were analyzed. It was shown that the resulting microstructure strongly depends on the cooling condition. This study also shows that it was possible to increase the micro hardness in the top surface layer significantly.

B-machine polarimeter: A telescope to measure the polarization of the cosmic microwave background

NASA Astrophysics Data System (ADS)

Williams, Brian Dean

The B-Machine Telescope is the culmination of several years of development, construction, characterization and observation. The telescope is a departure from standard polarization chopping of correlation receivers to a half wave plate technique. Typical polarimeters use a correlation receiver to chop the polarization signal to overcome the 1/f noise inherent in HEMT amplifiers. B-Machine uses a room temperature half wave plate technology to chop between polarization states and measure the polarization signature of the CMB. The telescope has a demodulated 1/f knee of 5 mHz and an average sensitivity of 1.6 mK s . This document examines the construction, characterization, observation of astronomical sources, and data set analysis of B-Machine. Preliminary power spectra and sky maps with large sky coverage for the first year data set are included.

The National Longitudinal Study of the High School Class of 1972 (NLS-72), Fifth Follow-Up (1986). Teaching Supplement Data File [machine-readable data file].

ERIC Educational Resources Information Center

National Center for Education Statistics (ED), Washington, DC.

The National Longitudinal Survey of the High School Class of 1972 (NLS-72) Teaching Supplement Data File (TSDF) is presented. Data for the machine-readable data file (MDRF) were collected via a mail questionnaire that was sent to all respondents (N=1,517) to the fifth follow-up survey who indicated that they had a teaching background or training…

Time to B. cereus about hot chocolate.

PubMed Central

Nelms, P K; Larson, O; Barnes-Josiah, D

1997-01-01

OBJECTIVE: To determine the cause of illnesses experienced by employees of a Minneapolis manufacturing plant after drinking hot chocolate bought from a vending machine and to explore the prevalence of similar vending machine-related illnesses. METHODS: The authors inspected the vending machines at the manufacturing plant where employees reported illnesses and at other locations in the city where hot chocolate beverages were sold in machines. Tests were performed on dry mix, water, and beverage samples and on machine parts. RESULTS: Laboratory analyses confirmed the presence of B. cereus in dispensed beverages at a concentration capable of causing illness (170,000 count/gm). In citywide testing of vending machines dispensing hot chocolate, 7 of the 39 licensed machines were found to be contaminated, with two contaminated machines having B. cereus levels capable of causing illness. CONCLUSIONS: Hot chocolate sold in vending machines may contain organisms capable of producing toxins that under favorable conditions, can induce illness. Such illnesses are likely to be underreported. Even low concentrations of B. cereus may be dangerous for vulnerable populations such as the aged or immunosuppressed. Periodic testing of vending machines is thus warranted. The relationship between cleaning practices and B. cereus contamination is an issue for further study. PMID:9160059

Time to B. cereus about hot chocolate.

PubMed

Nelms, P K; Larson, O; Barnes-Josiah, D

1997-01-01

To determine the cause of illnesses experienced by employees of a Minneapolis manufacturing plant after drinking hot chocolate bought from a vending machine and to explore the prevalence of similar vending machine-related illnesses. The authors inspected the vending machines at the manufacturing plant where employees reported illnesses and at other locations in the city where hot chocolate beverages were sold in machines. Tests were performed on dry mix, water, and beverage samples and on machine parts. Laboratory analyses confirmed the presence of B. cereus in dispensed beverages at a concentration capable of causing illness (170,000 count/gm). In citywide testing of vending machines dispensing hot chocolate, 7 of the 39 licensed machines were found to be contaminated, with two contaminated machines having B. cereus levels capable of causing illness. Hot chocolate sold in vending machines may contain organisms capable of producing toxins that under favorable conditions, can induce illness. Such illnesses are likely to be underreported. Even low concentrations of B. cereus may be dangerous for vulnerable populations such as the aged or immunosuppressed. Periodic testing of vending machines is thus warranted. The relationship between cleaning practices and B. cereus contamination is an issue for further study.

Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality

PubMed Central

2016-01-01

Background One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Objective Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Methods Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Results Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Conclusions Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data. PMID:27185366

4. Credit JPL. Original 4" x 5" black and white ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

4. Credit JPL. Original 4" x 5" black and white negative housed in the JPL Archives, Pasadena, California. This interior view displays the machine shop in the Administration/Shops Building (the compass angle of the view is undetermined). Looking clockwise from the lower left, the machine tools in view are a power hacksaw, a heat-treatment oven (with white gloves on top), a large hydraulic press with a tool grinder at its immediate right; along the wall in the back of the view are various unidentified machine tool attachments and a vertical milling machine. In the background, a machinist is operating a radial drilling machine, to the right of which is a small drill press. To the lower right, another machinist is operating a Pratt & Whitney engine lathe; behind the operator stand a workbench and vertical bandsaw (JPL negative no. 384-10939, 29 July 1975). - Jet Propulsion Laboratory Edwards Facility, Administration & Shops Building, Edwards Air Force Base, Boron, Kern County, CA

Efficient machining of ultra precise steel moulds with freeform surfaces

NASA Astrophysics Data System (ADS)

Bulla, B.; Robertson, D. J.; Dambon, O.; Klocke, F.

2013-09-01

Ultra precision diamond turning of hardened steel to produce optical quality surfaces can be realized by applying an ultrasonic assisted process. With this technology optical moulds used typically for injection moulding can be machined directly from steel without the requirement to overcoat the mould with a diamond machinable material such as Nickel Phosphor. This has both the advantage of increasing the mould tool lifetime and also reducing manufacture costs by dispensing with the relatively expensive plating process. This publication will present results we have obtained for generating free form moulds in hardened steel by means of ultrasonic assisted diamond turning with a vibration frequency of 80 kHz. To provide a baseline with which to characterize the system performance we perform plane cutting experiments on different steel alloys with different compositions. The baseline machining results provides us information on the surface roughness and on tool wear caused during machining and we relate these to material composition. Moving on to freeform surfaces, we will present a theoretical background to define the machine program parameters for generating free forms by applying slow slide servo machining techniques. A solution for optimal part generation is introduced which forms the basis for the freeform machining experiments. The entire process chain, from the raw material through to ultra precision machining is presented, with emphasis on maintaining surface alignment when moving a component from CNC pre-machining to final machining using ultrasonic assisted diamond turning. The free form moulds are qualified on the basis of the surface roughness measurements and a form error map comparing the machined surface with the originally defined surface. These experiments demonstrate the feasibility of efficient free form machining applying ultrasonic assisted diamond turning of hardened steel.

Traveling wire electrode increases productivity of Electrical Discharge Machining /EDM/ equipment

NASA Technical Reports Server (NTRS)

Kotora, J., Jr.; Smith, S. V.

1967-01-01

Traveling wire electrode on electrical discharge machining /EDM/ equipment reduces the time requirements for precision cutting. This device enables cutting with a minimum of lost material and without inducing stress beyond that inherent in the material. The use of wire increases accuracy and enables tighter tolerances to be maintained.

Effect of Built-Up Edge Formation during Stable State of Wear in AISI 304 Stainless Steel on Machining Performance and Surface Integrity of the Machined Part.

PubMed

Ahmed, Yassmin Seid; Fox-Rabinovich, German; Paiva, Jose Mario; Wagg, Terry; Veldhuis, Stephen Clarence

2017-10-25

During machining of stainless steels at low cutting -speeds, workpiece material tends to adhere to the cutting tool at the tool-chip interface, forming built-up edge (BUE). BUE has a great importance in machining processes; it can significantly modify the phenomenon in the cutting zone, directly affecting the workpiece surface integrity, cutting tool forces, and chip formation. The American Iron and Steel Institute (AISI) 304 stainless steel has a high tendency to form an unstable BUE, leading to deterioration of the surface quality. Therefore, it is necessary to understand the nature of the surface integrity induced during machining operations. Although many reports have been published on the effect of tool wear during machining of AISI 304 stainless steel on surface integrity, studies on the influence of the BUE phenomenon in the stable state of wear have not been investigated so far. The main goal of the present work is to investigate the close link between the BUE formation, surface integrity and cutting forces in the stable sate of wear for uncoated cutting tool during the cutting tests of AISI 304 stainless steel. The cutting parameters were chosen to induce BUE formation during machining. X-ray diffraction (XRD) method was used for measuring superficial residual stresses of the machined surface through the stable state of wear in the cutting and feed directions. In addition, surface roughness of the machined surface was investigated using the Alicona microscope and Scanning Electron Microscopy (SEM) was used to reveal the surface distortions created during the cutting process, combined with chip undersurface analyses. The investigated BUE formation during the stable state of wear showed that the BUE can cause a significant improvement in the surface integrity and cutting forces. Moreover, it can be used to compensate for tool wear through changing the tool geometry, leading to the protection of the cutting tool from wear.

Effect of Built-Up Edge Formation during Stable State of Wear in AISI 304 Stainless Steel on Machining Performance and Surface Integrity of the Machined Part

PubMed Central

Fox-Rabinovich, German; Wagg, Terry

2017-01-01

During machining of stainless steels at low cutting -speeds, workpiece material tends to adhere to the cutting tool at the tool–chip interface, forming built-up edge (BUE). BUE has a great importance in machining processes; it can significantly modify the phenomenon in the cutting zone, directly affecting the workpiece surface integrity, cutting tool forces, and chip formation. The American Iron and Steel Institute (AISI) 304 stainless steel has a high tendency to form an unstable BUE, leading to deterioration of the surface quality. Therefore, it is necessary to understand the nature of the surface integrity induced during machining operations. Although many reports have been published on the effect of tool wear during machining of AISI 304 stainless steel on surface integrity, studies on the influence of the BUE phenomenon in the stable state of wear have not been investigated so far. The main goal of the present work is to investigate the close link between the BUE formation, surface integrity and cutting forces in the stable sate of wear for uncoated cutting tool during the cutting tests of AISI 304 stainless steel. The cutting parameters were chosen to induce BUE formation during machining. X-ray diffraction (XRD) method was used for measuring superficial residual stresses of the machined surface through the stable state of wear in the cutting and feed directions. In addition, surface roughness of the machined surface was investigated using the Alicona microscope and Scanning Electron Microscopy (SEM) was used to reveal the surface distortions created during the cutting process, combined with chip undersurface analyses. The investigated BUE formation during the stable state of wear showed that the BUE can cause a significant improvement in the surface integrity and cutting forces. Moreover, it can be used to compensate for tool wear through changing the tool geometry, leading to the protection of the cutting tool from wear. PMID:29068405

Quench-Induced Stresses in AA2618 Forgings for Impellers: A Multiphysics and Multiscale Problem

NASA Astrophysics Data System (ADS)

Chobaut, Nicolas; Saelzle, Peter; Michel, Gilles; Carron, Denis; Drezet, Jean-Marie

2015-05-01

In the fabrication of heat-treatable aluminum parts such as AA2618 compressor impellers for turbochargers, solutionizing and quenching are key steps to obtain the required mechanical characteristics. Fast quenching is necessary to avoid coarse precipitation as it reduces the mechanical properties obtained after heat treatment. However, fast quenching induces residual stresses that can cause unacceptable distortions during machining. Furthermore, the remaining residual stresses after final machining can lead to unfavorable stresses in service. Predicting and controlling internal stresses during the whole processing from heat treatment to final machining is therefore of particular interest to prevent negative impacts of residual stresses. This problem is multiphysics because processes such as heat transfer during quenching, precipitation phenomena, thermally induced deformations, and stress generation are interacting and need to be taken into account. The problem is also multiscale as precipitates of nanosize form during quenching at locations where the cooling rate is too low. This precipitation affects the local yield strength of the material and thus impacts the level of macroscale residual stresses. A thermomechanical model accounting for precipitation in a simple but realistic way is presented. Instead of modelling precipitation that occurs during quenching, the model parameters are identified using a limited number of tensile tests achieved after representative interrupted cooling paths in a Gleeble machine. The simulation results are compared with as-quenched residual stresses in a forging measured by neutron diffraction.

Swiss ball abdominal crunch with added elastic resistance is an effective alternative to training machines.

PubMed

Sundstrup, Emil; Jakobsen, Markus D; Andersen, Christoffer H; Jay, Kenneth; Andersen, Lars L

2012-08-01

Swiss ball training is recommended as a low intensity modality to improve joint position, posture, balance, and neural feedback. However, proper training intensity is difficult to obtain during Swiss ball exercises whereas strengthening exercises on machines usually are performed to induce high level of muscle activation. To compare muscle activation as measured by electromyography (EMG) of global core and thigh muscles during abdominal crunches performed on Swiss ball with elastic resistance or on an isotonic training machine when normalized for training intensity. 42 untrained individuals (18 men and 24 women) aged 28-67 years participated in the study. EMG activity was measured in 13 muscles during 3 repetitions with a 10 RM load during both abdominal crunches on training ball with elastic resistance and in the same movement utilizing a training machine (seated crunch, Technogym, Cesena, Italy). The order of performance of the exercises was randomized, and EMG amplitude was normalized to maximum voluntary isometric contraction (MVIC) EMG. When comparing between muscles, normalized EMG was highest in the rectus abdominis (P<0.01) and the external obliques (P<0.01). However, crunches on Swiss ball with elastic resistance showed higher activity of the rectus abdominis than crunches performed on the machine (104±3.8 vs 84±3.8% nEMG respectively, P<0.0001). By contrast, crunches performed on Swiss ball induced lower activity of the rectus femoris than crunches in training machine (27±3.7 vs 65±3.8% nEMG respectively, P<0.0001) Further, gender, age and musculoskeletal pain did not significantly influence the findings. Crunches on a Swiss ball with added elastic resistance induces high rectus abdominis activity accompanied by low hip flexor activity which could be beneficial for individuals with low back pain. In opposition, the lower rectus abdominis activity and higher rectus femoris activity observed in machine warrant caution for individuals with lumbar pain. Importantly, both men and women, younger and elderly, and individuals with and without pain benefitted equally from the exercises.

Concept Learning and Heuristic Classification in Weak-Theory Domains

DTIC Science & Technology

1990-03-01

age and noise-induced cochlear age..gt.60 noise-induced cochlear air(mild) age-induced cochlear history(noise) norma ]_ear speechpoor)acousticneuroma...Annual review of computer science. Machine Learning, 4, 1990. (to appear). [18] R.T. Duran . Concept learning with incomplete data sets. Master’s thesis

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

PubMed Central

2011-01-01

Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements. PMID:21798025

Measurement of W + bb and a search for MSSM Higgs bosons with the CMS detector at the LHC

NASA Astrophysics Data System (ADS)

O'Connor, Alexander Pinpin

Tooling used to cure composite laminates in the aerospace and automotive industries must provide a dimensionally stable geometry throughout the thermal cycle applied during the part curing process. This requires that the Coefficient of Thermal Expansion (CTE) of the tooling materials match that of the composite being cured. The traditional tooling material for production applications is a nickel alloy. Poor machinability and high material costs increase the expense of metallic tooling made from nickel alloys such as 'Invar 36' or 'Invar 42'. Currently, metallic tooling is unable to meet the needs of applications requiring rapid affordable tooling solutions. In applications where the tooling is not required to have the durability provided by metals, such as for small area repair, an opportunity exists for non-metallic tooling materials like graphite, carbon foams, composites, or ceramics and machinable glasses. Nevertheless, efficient machining of brittle, non-metallic materials is challenging due to low ductility, porosity, and high hardness. The machining of a layup tool comprises a large portion of the final cost. Achieving maximum process economy requires optimization of the machining process in the given tooling material. Therefore, machinability of the tooling material is a critical aspect of the overall cost of the tool. In this work, three commercially available, brittle/porous, non-metallic candidate tooling materials were selected, namely: (AAC) Autoclaved Aerated Concrete, CB1100 ceramic block and Cfoam carbon foam. Machining tests were conducted in order to evaluate the machinability of these materials using end milling. Chip formation, cutting forces, cutting tool wear, machining induced damage, surface quality and surface integrity were investigated using High Speed Steel (HSS), carbide, diamond abrasive and Polycrystalline Diamond (PCD) cutting tools. Cutting forces were found to be random in magnitude, which was a result of material porosity. The abrasive nature of Cfoam produced rapid tool wear when using HSS and PCD type cutting tools. However, tool wear was not significant in AAC or CB1100 regardless of the type of cutting edge. Machining induced damage was observed in the form of macro-scale chipping and fracture in combination with micro-scale cracking. Transverse rupture test results revealed significant reductions in residual strength and damage tolerance in CB1100. In contrast, AAC and Cfoam showed no correlation between machining induced damage and a reduction in surface integrity. Cutting forces in machining were modeled for all materials. Cutting force regression models were developed based on Design of Experiment and Analysis of Variance. A mechanistic cutting force model was proposed based upon conventional end milling force models and statistical distributions of material porosity. In order to validate the model, predicted cutting forces were compared to experimental results. Predicted cutting forces agreed well with experimental measurements. Furthermore, over the range of cutting conditions tested, the proposed model was shown to have comparable predictive accuracy to empirically produced regression models; greatly reducing the number of cutting tests required to simulate cutting forces. Further, this work demonstrates a key adaptation of metallic cutting force models to brittle porous material; a vital step in the research into the machining of these materials using end milling.

ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.

PubMed

Lei, Tailong; Sun, Huiyong; Kang, Yu; Zhu, Feng; Liu, Hui; Zhou, Wenfang; Wang, Zhe; Li, Dan; Li, Youyong; Hou, Tingjun

2017-11-06

Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common adverse event for medications, natural supplements, and environmental chemicals. Despite its importance, there are only a few in silico models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures. Here, we developed a series of qualitative and quantitative structure-activity relationship (QSAR) models for predicting urinary tract toxicity. In our study, the recursive feature elimination method incorporated with random forests (RFE-RF) was used for dimension reduction, and then eight machine learning approaches were used for QSAR modeling, i.e., relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), C5.0 trees, eXtreme gradient boosting (XGBoost), AdaBoost.M1, SVM boosting (SVMBoost), and RVM boosting (RVMBoost). For building classification models, the synthetic minority oversampling technique was used to handle the imbalance data set problem. Among all the machine learning approaches, SVMBoost based on the RBF kernel achieves both the best quantitative (q ext 2 = 0.845) and qualitative predictions for the test set (MCC of 0.787, AUC of 0.893, sensitivity of 89.6%, specificity of 94.1%, and global accuracy of 90.8%). The application domains were then analyzed, and all of the tested chemicals fall within the application domain coverage. We also examined the structure features of the chemicals with large prediction errors. In brief, both the regression and classification models developed by the SVMBoost approach have reliable prediction capability for assessing chemical-induced urinary tract toxicity.

Background Equatorial Astronomical Measurements Focal Plane Assembly (Refurbished HI STAR SOUTH)

DTIC Science & Technology

1984-09-01

Subassembly RPT41412 MOSFETs during assembly and test. The old and new designs are shown in Fig- ure 7. The copper webs between the first and second and...machined in the remaining webs be- tween the detector recesses and through a small hole drilled through the frame to connect the traces of all four...gold wirebond routed through a notch machined in the frame web between one of the detector recesses and the board recess. The sap- phire support

Annual Fuze Conference (54th) Held in Kansas City, Missouri on May 11-13, 2010

DTIC Science & Technology

2010-05-13

approaches to development • Lets get something straight !!! – Experimentation (A few of a kind) • Focus on answering questions (is it useful?, how does it...machines are applicable / available • How do the machines work • Where do they get their reference • What kind of tolerances are they capable of...Tactical Benefits: • Fastest way to get local reconnaissance images • Totally impervious to weather/gusts • ~ $20/round Background History New

Far infrared supplement: Catalog of infrared observations

NASA Technical Reports Server (NTRS)

Gezari, D. Y.; Schmitz, M.; Mead, J. M.

1982-01-01

The development of a new generation of orbital, airborne and ground-based infrared astronomical observatory facilities, including the infrared astronomical satellite (IRAS), the cosmic background explorer (COBE), the NASA Kuiper airborne observatory, and the NASA infrared telescope facility, intensified the need for a comprehensive, machine-readable data base and catalog of current infrared astronomical observations. The Infrared Astronomical Data Base and its principal data product, this catalog, comprise a machine-readable library of infrared (1 micrometer to 1000 micrometers) astronomical observations published in the scientific literature since 1965.

Method for providing slip energy control in permanent magnet electrical machines

DOEpatents

Hsu, John S.

2006-11-14

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.

4. VIEW OF BORING MILL IN OPERATION, operator unknown (note ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

4. VIEW OF BORING MILL IN OPERATION, operator unknown (note console in background). - Juniata Shops, Erecting Shop & Machine Shop, East of Fourth Avenue, between Fourth & Fifth Streets, Altoona, Blair County, PA

3. VIEW OF BORING MILL IN OPERATION, operator unknown (note ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

3. VIEW OF BORING MILL IN OPERATION, operator unknown (note console in background). - Juniata Shops, Erecting Shop & Machine Shop, East of Fourth Avenue, between Fourth & Fifth Streets, Altoona, Blair County, PA

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

PubMed Central

2016-01-01

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. PMID:27986644

An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

PubMed

Guvensan, M Amac; Kansiz, A Oguz; Camgoz, N Cihan; Turkmen, H Irem; Yavuz, A Gokhan; Karsligil, M Elif

2017-06-23

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

CP Violation and the Future of Flavor Physics

NASA Astrophysics Data System (ADS)

Kiesling, Christian

2009-12-01

With the nearing completion of the first-generation experiments at asymmetric e+e- colliders running at the Υ(4S) resonance ("B-Factories") a new era of high luminosity machines is at the horizon. We report here on the plans at KEK in Japan to upgrade the KEKB machine ("SuperKEKB") with the goal of achieving an instantaneous luminosity exceeding 8×1035 cm-2 s-1, which is almost two orders of magnitude higher than KEKB. Together with the machine, the Belle detector will be upgraded as well ("Belle-II"), with significant improvements to increase its background tolerance as well as improving its physics performance. The new generation of experiments is scheduled to take first data in the year 2013.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis.

PubMed

Liu, Fei; Ye, Lanhan; Peng, Jiyu; Song, Kunlin; Shen, Tingting; Zhang, Chu; He, Yong

2018-02-27

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R 2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where R c 2 and R p 2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

PubMed Central

Ye, Lanhan; Song, Kunlin; Shen, Tingting

2018-01-01

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where Rc2 and Rp2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice. PMID:29495445

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

Johnston, H; UT Southwestern Medical Center, Dallas, TX; Hilts, M

Purpose: To commission a multislice computed tomography (CT) scanner for fast and reliable readout of radiation therapy (RT) dose distributions using CT polymer gel dosimetry (PGD). Methods: Commissioning was performed for a 16-slice CT scanner using images acquired through a 1L cylinder filled with water. Additional images were collected using a single slice machine for comparison purposes. The variability in CT number associated with the anode heel effect was evaluated and used to define a new slice-by-slice background image subtraction technique. Image quality was assessed for the multislice system by comparing image noise and uniformity to that of the singlemore » slice machine. The consistency in CT number across slices acquired simultaneously using the multislice detector array was also evaluated. Finally, the variability in CT number due to increasing x-ray tube load was measured for the multislice scanner and compared to the tube load effects observed on the single slice machine. Results: Slice-by-slice background subtraction effectively removes the variability in CT number across images acquired simultaneously using the multislice scanner and is the recommended background subtraction method when using a multislice CT system. Image quality for the multislice machine was found to be comparable to that of the single slice scanner. Further study showed CT number was consistent across image slices acquired simultaneously using the multislice detector array for each detector configuration of the slice thickness examined. In addition, the multislice system was found to eliminate variations in CT number due to increasing x-ray tube load and reduce scanning time by a factor of 4 when compared to imaging a large volume using a single slice scanner. Conclusion: A multislice CT scanner has been commissioning for CT PGD, allowing images of an entire dose distribution to be acquired in a matter of minutes. Funding support provided by the Natural Sciences and Engineering Research Council of Canada (NSERC)« less

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

PubMed

Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-08-04

Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers. With the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED. The high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds. ©Jose Juan Dominguez Veiga, Martin O'Reilly, Darragh Whelan, Brian Caulfield, Tomas E Ward. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.08.2017.

Machine-related backgrounds in the SiD detector at ILC

NASA Astrophysics Data System (ADS)

Denisov, D. S.; Mokhov, N. V.; Striganov, S. I.; Kostin, M. A.; Tropin, I. S.

2006-12-01

With a multi-stage collimation system and magnetic iron spoilers in the tunnel, the background particle fluxes on the ILC detector can be substantially reduced. At the same time, beam-halo interactions with collimators and protective masks in the beam delivery system create fluxes of muons and other secondary particles which can still exceed the tolerable levels for some of the ILC sub-detectors. Results of modeling of such backgrounds in comparison to those from the e+e- interactions are presented in this paper for the SiD detector.

Burst temperature from conditional analysis in Texas Helimak and TCABR tokamak

NASA Astrophysics Data System (ADS)

Pereira, F. A. C.; Hernandez, W. A.; Toufen, D. L.; Guimarães-Filho, Z. O.; Caldas, I. L.; Gentle, K. W.

2018-04-01

The procedure to estimate the average local temperature, density, and plasma potential by conditionally selecting points of the Langmuir probe characteristic curve is revised and applied to the study of intermittent bursts in the Texas Helimak and TCABR tokamak. The improvements made allow us to distinguish the burst temperature from the turbulent background and to study burst propagation. Thus, in Texas Helimak, we identify important differences with respect to the burst temperature measured in the top and the bottom regions of the machine. While in the bottom region the burst temperatures are almost equal to the background, the bursts in the top region are hotter than the background with the temperature peak clearly shifted with respect to the density one. On the other hand, in the TCABR tokamak, we found that there is a temperature peak simultaneously with the density one. Moreover, the radial profile of bursts in the top region of Helimak and in the edge and scrape-off layer regions of TCABR shows that in both machines, there are spatial regions where the relative difference between the burst and the background temperatures is significant: up to 25% in Texas Helimak and around 50% in TCABR. However, in Texas Helimak, there are also regions where these temperatures are almost the same.

A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

PubMed

Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

2014-08-01

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Radiation and People

ERIC Educational Resources Information Center

Freilich, Florence G.

1970-01-01

Describes the development of radiation as a tool of medicine. Includes topics on history of radiation, electromagnetic spectrum, X-ray tubes, high energy machines, radioactive sources, artificial radioactivity, radioactive scanning, units, present radiation background, and effect of radiation on living tissue. (DS)

Support vector machine as a binary classifier for automated object detection in remotely sensed data

NASA Astrophysics Data System (ADS)

Wardaya, P. D.

2014-02-01

In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result.

Microstructural and hardness changes in aluminum alloy Al-7075: Correlating machining and equal channel angular pressing

NASA Astrophysics Data System (ADS)

Imbrogno, Stano; Segebade, Eric; Fellmeth, Andreas; Gerstenmeyer, Michael; Zanger, Frederik; Schulze, Volker; Umbrello, Domenico

2017-10-01

Recently, the study and understanding of surface integrity of various materials after machining is becoming one of the interpretative keys to quantify a product's quality and life cycle performance. The possibility to provide fundamental details about the mechanical response and the behavior of the affected material layers caused by thermo-mechanical loads resulting from machining operations can help the designer to produce parts with superior quality. The aim of this work is to study the experimental outcomes obtained from orthogonal cutting tests and a Severe Plastic Deformation (SPD) process known as Equal Channel Angular Pressing (ECAP) in order to find possible links regarding induced microstructural and hardness changes between machined surface layer and SPD-bulk material for Al-7075. This scientific investigation aims to establish the basis for an innovative method to study and quantify metallurgical phenomena that occur beneath the machined surface of bulk material.

Plan for conducting an international machine tool task force

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

Sutton, G.P.; McClure, E.R.; Schuman, J.F.

1978-08-28

The basic objectives of the Machine Tool Task Force (MTTF) are to characterize and summarize the state of the art of cutting machine tool technology and to identify promising areas of future R and D. These goals will be accomplished with a series of multidisciplinary teams of prominent experts and individuals experienced in the specialized technologies of machine tools or in the management of machine tool operations. Experts will be drawn from all areas of the machine tool community: machine tool users or buyer organizations, builders, and R and D establishments including universities and government laboratories, both domestic and foreign.more » A plan for accomplishing this task is presented. The area of machine tool technology has been divided into about two dozen technology subjects on which teams of one or more experts will work. These teams are, in turn, organized into four principal working groups dealing, respectively, with machine tool accuracy, mechanics, control, and management systems/utilization. Details are presented on specific subjects to be covered, the organization of the Task Force and its four working groups, and the basic approach to determining the state of the art of technology and the future directions of this technology. The planned review procedure, the potential benefits, our management approach, and the schedule, as well as the key participating personnel and their background are discussed. The initial meeting of MTTF members will be held at a plenary session on October 16 and 17, 1978, in Scottsdale, AZ. The MTTF study will culminate in a conference on September 1, 1980, in Chicago, IL, immediately preceeding the 1980 International Machine Tool Show. At this time, our results will be released to the public; a series of reports will be published in late 1980.« less

Intense X-ray machine for penetrating radiography

NASA Astrophysics Data System (ADS)

Lucht, Roy A.; Eckhouse, Shimon

Penetrating radiography has been used for many years in the nuclear weapons research programs. Infrequently penetrating radiography has been used in conventional weapons research programs. For example the Los Alamos PHERMEX machine was used to view uranium rods penetrating steel for the GAU-8 program, and the Ector machine was used to see low density regions in forming metal jets. The armor/anti-armor program at Los Alamos has created a need for an intense flash X-ray machine that can be dedicated to conventional weapons research. The Balanced Technology Initiative, through DARPA, has funded the design and construction of such a machine at Los Alamos. It will be an 8- to 10-MeV diode machine capable of delivering a dose of 500 R at 1 m with a spot size of less than 5 mm. The machine used an 87.5-stage low inductance Marx generator that charges up a 7.4-(Omega), 32-ns water line. The water line is discharged through a self-breakdown oil switch into a 12.4-(Omega) water line that rings up the voltage into the high impendance X-ray diode. A long (233-cm) vacuum drift tube is used to separate the large diameter oil-insulated diode region from the X-ray source area that may be exposed to high overpressures by the explosive experiments. The electron beam is selffocused at the target area using a low pressure background gas.

SiD Linear Collider Detector R&D, DOE Final Report

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

Brau, James E.; Demarteau, Marcel

2015-05-15

The Department of Energy’s Office of High Energy Physics supported the SiD university detector R&D projects in FY10, FY11, and FY12 with no-cost extensions through February, 2015. The R&D projects were designed to advance the SiD capabilities to address the fundamental questions of particle physics at the International Linear Collider (ILC): • What is the mechanism responsible for electroweak symmetry breaking and the generation of mass? • How do the forces unify? • Does the structure of space-time at small distances show evidence for extra dimensions? • What are the connections between the fundamental particles and forces and cosmology? Siliconmore » detectors are used extensively in SiD and are well-matched to the challenges presented by ILC physics and the ILC machine environment. They are fast, robust against machine-induced background, and capable of very fine segmentation. SiD is based on silicon tracking and silicon-tungsten sampling calorimetry, complemented by powerful pixel vertex detection, and outer hadronic calorimetry and muon detection. Radiation hard forward detectors which can be read out pulse by pulse are required. Advanced calorimetry based on a particle flow algorithm (PFA) provides excellent jet energy resolution. The 5 Tesla solenoid is outside the calorimeter to improve energy resolution. PFA calorimetry requires fine granularity for both electromagnetic and hadronic calorimeters, leading naturally to finely segmented silicon-tungsten electromagnetic calorimetry. Since silicon-tungsten calorimetry is expensive, the detector architecture is compact. Precise tracking is achieved with the large magnetic field and high precision silicon microstrips. An ancillary benefit of the large magnetic field is better control of the e⁺e⁻ pair backgrounds, permitting a smaller radius beampipe and improved impact parameter resolution. Finally, SiD is designed with a cost constraint in mind. Significant advances and new capabilities have been made and are described in this report.« less

Automation's Effect on Library Personnel.

ERIC Educational Resources Information Center

Dakshinamurti, Ganga

1985-01-01

Reports on survey studying the human-machine interface in Canadian university, public, and special libraries. Highlights include position category and educational background of 118 participants, participants' feelings toward automation, physical effects of automation, diffusion in decision making, interpersonal communication, future trends,…

View of building 11050. With view of building 11070 (See ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

View of building 11050. With view of building 11070 (See HABS No. CA 2774-B) in background. Looking southwest. - Naval Ordnance Test Station Inyokern, China Lake Pilot Plant, Machine Shop, C Street, China Lake, Kern County, CA

Implementation and Research on the Operational Use of the Mesoscale Prediction Model COAMPS in Poland

DTIC Science & Technology

2007-09-30

COAMPS model. Bogumil Jakubiak, University of Warsaw – participated in EGU General Assembly , Vienna Austria 15-20 April 2007 giving one oral and two...conditional forecast (background) error probability density function using an ensemble of the model forecast to generate background error statistics...COAMPS system on ICM machines at Warsaw University for the purpose of providing operational support to the general public using the ICM meteorological

6. VIEW LOOKING NORTHWEST FROM THE IMAGE LEFT TO THE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

6. VIEW LOOKING NORTHWEST FROM THE IMAGE LEFT TO THE IMAGE RIGHT IS THE CHARCOAL HOUSE, THE RETORT SHED IN THE BACKGROUND, THE MILL ANNEX, THE MACHINE SHOP, AND THE ELECTRIC MOTOR ROOM. THE MILL BUILDING IS IN THE BACKGROUND CENTER RIGHT AND ONE OF THE ORE DELIVERY TRESTLES EXTENDING FROM THE MILL BUILDING TO RIGHT IMAGE EDGE. - Standard Gold Mill, East of Bodie Creek, Northeast of Bodie, Bodie, Mono County, CA

Induced sadness increases persistence in a simulated slot machine task among recreational gamblers.

PubMed

Devos, Gaëtan; Clark, Luke; Maurage, Pierre; Billieux, Joël

2018-05-01

Gambling may constitute a strategy for coping with depressive mood, but a direct influence of depressive mood on gambling behaviors has never been tested via realistic experimental designs in gamblers. The current study tested whether experimentally induced sadness increases persistence on a simulated slot machine task using real monetary reinforcement in recreational gamblers. Sixty participants were randomly assigned to an experimental (sadness induction) or control (no emotional induction) condition, and then performed a slot machine task consisting of a mandatory phase followed by a persistence phase. Potential confounding variables (problem gambling symptoms, impulsivity traits, gambling cognitions) were measured to ensure that the experimental and control groups were comparable. The study showed that participants in the sadness condition displayed greater gambling persistence than control participants (p = .011). These data support the causal role of negative affect in decisions to gamble and persistence, which bears important theoretical and clinical implications. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Color line-scan technology in industrial applications

NASA Astrophysics Data System (ADS)

Lemstrom, Guy F.

1995-10-01

Color machine vision opens new possibilities for industrial on-line quality control applications. With color machine vision it's possible to detect different colors and shades, make color separation, spectroscopic applications and at the same time do measurements in the same way as with gray scale technology. These can be geometrical measurements such as dimensions, shape, texture etc. By combining these technologies in a color line scan camera, it brings the machine vision to new dimensions of realizing new applications and new areas in the machine vision business. Quality and process control requirements in the industry get more demanding every day. Color machine vision can be the solution for many simple tasks that haven't been realized with gray scale technology. The lack of detecting or measuring colors has been one reason why machine vision has not been used in quality control as much as it could have been. Color machine vision has shown a growing enthusiasm in the industrial machine vision applications. Potential areas of the industry include food, wood, mining and minerals, printing, paper, glass, plastic, recycling etc. Tasks are from simple measuring to total process and quality control. The color machine vision is not only for measuring colors. It can also be for contrast enhancement, object detection, background removing, structure detection and measuring. Color or spectral separation can be used in many different ways for working out machine vision application than before. It's only a question of how to use the benefits of having two or more data per measured pixel, instead of having only one as in case with traditional gray scale technology. There are plenty of potential applications already today that can be realized with color vision and it's going to give more performance to many traditional gray scale applications in the near future. But the most important feature is that color machine vision offers a new way of working out applications, where machine vision hasn't been applied before.

CS2 analysis in presence of non-Gaussian background noise - Effect on traditional estimators and resilience of log-envelope indicators

NASA Astrophysics Data System (ADS)

Borghesani, P.; Antoni, J.

2017-06-01

Second-order cyclostationary (CS2) analysis has become popular in the field of machine diagnostics and a series of digital signal processing techniques have been developed to extract CS2 components from the background noise. Among those techniques, squared envelope spectrum (SES) and cyclic modulation spectrum (CMS) have gained popularity thanks to their high computational efficiency and simple implementation. The effectiveness of CMS and SES has been previously quantified based on the hypothesis of Gaussian background noise and has led to statistical tests for the presence of CS2 peaks in squared envelope spectra and cyclic modulation spectra. However a recently established link of CMS with SES and of SES with kurtosis has exposed a potential weakness of those indicators in the case of highly leptokurtic background noise. This case is often present in practice when the machine is subjected to highly impulsive phenomena, either due to harsh operating conditions or to electric noise generated by power electronics and captured by the sensor. This study investigates and quantifies for the first time the effect of leptokurtic noise on the capabilities of SES and CMS, by analysing three progressively harsh situations: high kurtosis, infinite kurtosis and alpha-stable background noise (for which even first and second-order moments are not defined). Then the resilience of a recently proposed family of CS2 indicators, based on the log-envelope, is verified analytically, numerically and experimentally in the case of highly leptokurtic noise.

Efficient forced vibration reanalysis method for rotating electric machines

NASA Astrophysics Data System (ADS)

Saito, Akira; Suzuki, Hiromitsu; Kuroishi, Masakatsu; Nakai, Hideo

2015-01-01

Rotating electric machines are subject to forced vibration by magnetic force excitation with wide-band frequency spectrum that are dependent on the operating conditions. Therefore, when designing the electric machines, it is inevitable to compute the vibration response of the machines at various operating conditions efficiently and accurately. This paper presents an efficient frequency-domain vibration analysis method for the electric machines. The method enables the efficient re-analysis of the vibration response of electric machines at various operating conditions without the necessity to re-compute the harmonic response by finite element analyses. Theoretical background of the proposed method is provided, which is based on the modal reduction of the magnetic force excitation by a set of amplitude-modulated standing-waves. The method is applied to the forced response vibration of the interior permanent magnet motor at a fixed operating condition. The results computed by the proposed method agree very well with those computed by the conventional harmonic response analysis by the FEA. The proposed method is then applied to the spin-up test condition to demonstrate its applicability to various operating conditions. It is observed that the proposed method can successfully be applied to the spin-up test conditions, and the measured dominant frequency peaks in the frequency response can be well captured by the proposed approach.

Designing of interferon-gamma inducing MHC class-II binders

PubMed Central

2013-01-01

Background The generation of interferon-gamma (IFN-γ) by MHC class II activated CD4+ T helper cells play a substantial contribution in the control of infections such as caused by Mycobacterium tuberculosis. In the past, numerous methods have been developed for predicting MHC class II binders that can activate T-helper cells. Best of author’s knowledge, no method has been developed so far that can predict the type of cytokine will be secreted by these MHC Class II binders or T-helper epitopes. In this study, an attempt has been made to predict the IFN-γ inducing peptides. The main dataset used in this study contains 3705 IFN-γ inducing and 6728 non-IFN-γ inducing MHC class II binders. Another dataset called IFNgOnly contains 4483 IFN-γ inducing epitopes and 2160 epitopes that induce other cytokine except IFN-γ. In addition we have alternate dataset that contains IFN-γ inducing and equal number of random peptides. Results It was observed that the peptide length, positional conservation of residues and amino acid composition affects IFN-γ inducing capabilities of these peptides. We identified the motifs in IFN-γ inducing binders/peptides using MERCI software. Our analysis indicates that IFN-γ inducing and non-inducing peptides can be discriminated using above features. We developed models for predicting IFN-γ inducing peptides using various approaches like machine learning technique, motifs-based search, and hybrid approach. Our best model based on the hybrid approach achieved maximum prediction accuracy of 82.10% with MCC of 0.62 on main dataset. We also developed hybrid model on IFNgOnly dataset and achieved maximum accuracy of 81.39% with 0.57 MCC. Conclusion Based on this study, we have developed a webserver for predicting i) IFN-γ inducing peptides, ii) virtual screening of peptide libraries and iii) identification of IFN-γ inducing regions in antigen (http://crdd.osdd.net/raghava/ifnepitope/). Reviewers This article was reviewed by Prof Kurt Blaser, Prof Laurence Eisenlohr and Dr Manabu Sugai. PMID:24304645

An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones

PubMed Central

Guvensan, M. Amac; Kansiz, A. Oguz; Camgoz, N. Cihan; Turkmen, H. Irem; Yavuz, A. Gokhan; Karsligil, M. Elif

2017-01-01

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions. PMID:28644378

Online machining error estimation method of numerical control gear grinding machine tool based on data analysis of internal sensors

NASA Astrophysics Data System (ADS)

Zhao, Fei; Zhang, Chi; Yang, Guilin; Chen, Chinyin

2016-12-01

This paper presents an online estimation method of cutting error by analyzing of internal sensor readings. The internal sensors of numerical control (NC) machine tool are selected to avoid installation problem. The estimation mathematic model of cutting error was proposed to compute the relative position of cutting point and tool center point (TCP) from internal sensor readings based on cutting theory of gear. In order to verify the effectiveness of the proposed model, it was simulated and experimented in gear generating grinding process. The cutting error of gear was estimated and the factors which induce cutting error were analyzed. The simulation and experiments verify that the proposed approach is an efficient way to estimate the cutting error of work-piece during machining process.

Integrative relational machine-learning for understanding drug side-effect profiles

PubMed Central

2013-01-01

Background Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence. Results In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site. Conclusions Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs. PMID:23802887

10. Locomotive smoke flue coming through Roundhouse roof with gable ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

10. Locomotive smoke flue coming through Roundhouse roof with gable end of Machine Shop in background. - Central of Georgia Railway, Savannah Repair Shops & Terminal Facilities, Roundhouse, Site Bounded by West Broad, Jones, West Boundary & Hull, Savannah, Chatham County, GA

Characteristics of the Arcing Plasma Formation Effect in Spark-Assisted Chemical Engraving of Glass, Based on Machine Vision

PubMed Central

Wu, Dung-Sheng

2018-01-01

Spark-assisted chemical engraving (SACE) is a non-traditional machining technology that is used to machine electrically non-conducting materials including glass, ceramics, and quartz. The processing accuracy, machining efficiency, and reproducibility are the key factors in the SACE process. In the present study, a machine vision method is applied to monitor and estimate the status of a SACE-drilled hole in quartz glass. During the machining of quartz glass, the spring-fed tool electrode was pre-pressured on the quartz glass surface to feed the electrode that was in contact with the machining surface of the quartz glass. In situ image acquisition and analysis of the SACE drilling processes were used to analyze the captured image of the state of the spark discharge at the tip and sidewall of the electrode. The results indicated an association between the accumulative size of the SACE-induced spark area and deepness of the hole. The results indicated that the evaluated depths of the SACE-machined holes were a proportional function of the accumulative spark size with a high degree of correlation. The study proposes an innovative computer vision-based method to estimate the deepness and status of SACE-drilled holes in real time. PMID:29565303

Characteristics of the Arcing Plasma Formation Effect in Spark-Assisted Chemical Engraving of Glass, Based on Machine Vision.

PubMed

Ho, Chao-Ching; Wu, Dung-Sheng

2018-03-22

Spark-assisted chemical engraving (SACE) is a non-traditional machining technology that is used to machine electrically non-conducting materials including glass, ceramics, and quartz. The processing accuracy, machining efficiency, and reproducibility are the key factors in the SACE process. In the present study, a machine vision method is applied to monitor and estimate the status of a SACE-drilled hole in quartz glass. During the machining of quartz glass, the spring-fed tool electrode was pre-pressured on the quartz glass surface to feed the electrode that was in contact with the machining surface of the quartz glass. In situ image acquisition and analysis of the SACE drilling processes were used to analyze the captured image of the state of the spark discharge at the tip and sidewall of the electrode. The results indicated an association between the accumulative size of the SACE-induced spark area and deepness of the hole. The results indicated that the evaluated depths of the SACE-machined holes were a proportional function of the accumulative spark size with a high degree of correlation. The study proposes an innovative computer vision-based method to estimate the deepness and status of SACE-drilled holes in real time.

Ductile and brittle transition behavior of titanium alloys in ultra-precision machining.

PubMed

Yip, W S; To, S

2018-03-02

Titanium alloys are extensively applied in biomedical industries due to their excellent material properties. However, they are recognized as difficult to cut materials due to their low thermal conductivity, which induces a complexity to their deformation mechanisms and restricts precise productions. This paper presents a new observation about the removal regime of titanium alloys. The experimental results, including the chip formation, thrust force signal and surface profile, showed that there was a critical cutting distance to achieve better surface integrity of machined surface. The machined areas with better surface roughness were located before the clear transition point, defining as the ductile to brittle transition. The machined area at the brittle region displayed the fracture deformation which showed cracks on the surface edge. The relationship between depth of cut and the ductile to brittle transaction behavior of titanium alloys in ultra-precision machining(UPM) was also revealed in this study, it showed that the ductile to brittle transaction behavior of titanium alloys occurred mainly at relatively small depth of cut. The study firstly defines the ductile to brittle transition behavior of titanium alloys in UPM, contributing the information of ductile machining as an optimal machining condition for precise productions of titanium alloys.

Robust recognition of degraded machine-printed characters using complementary similarity measure and error-correction learning

NASA Astrophysics Data System (ADS)

Hagita, Norihiro; Sawaki, Minako

1995-03-01

Most conventional methods in character recognition extract geometrical features such as stroke direction, connectivity of strokes, etc., and compare them with reference patterns in a stored dictionary. Unfortunately, geometrical features are easily degraded by blurs, stains and the graphical background designs used in Japanese newspaper headlines. This noise must be removed before recognition commences, but no preprocessing method is completely accurate. This paper proposes a method for recognizing degraded characters and characters printed on graphical background designs. This method is based on the binary image feature method and uses binary images as features. A new similarity measure, called the complementary similarity measure, is used as a discriminant function. It compares the similarity and dissimilarity of binary patterns with reference dictionary patterns. Experiments are conducted using the standard character database ETL-2 which consists of machine-printed Kanji, Hiragana, Katakana, alphanumeric, an special characters. The results show that this method is much more robust against noise than the conventional geometrical feature method. It also achieves high recognition rates of over 92% for characters with textured foregrounds, over 98% for characters with textured backgrounds, over 98% for outline fonts, and over 99% for reverse contrast characters.

Machining-induced deformation in stepped specimens of PH 13-8 Mo, 18 nickel maraging steel grade 200T1 and grain-refined HP 9-4-20

NASA Technical Reports Server (NTRS)

Wigley, D. A.

1985-01-01

The results of a study to evaluate the dimensional changes created during machining and subsequent cycling to cryogenic temperatures for three different metallic alloys are presented. Experimental techniques are described and results presented for 18 Ni Grade 200 maraging steel, PH-13-8 Mo stainless steel, and Grain-refined HP 9-4-20.

Availability of vending machines and school stores in California schools

PubMed Central

Liles, Sandy; Schmitz, Katharine E.; Kassem, Nada O.F; Irvin, Veronica L; Hovell, Melbourne F.

2015-01-01

Background This study examined the availability of foods sold in vending machines and school stores in US public and private schools, and associations of availability with students' food purchases and consumption. Methods Descriptive analyses, chi-square tests, and Spearman product-moment correlations were conducted on data collected from 521 students aged 8 to15 years recruited from orthodontic offices in California. Results Vending machines were more common in private schools than in public schools, while school stores were common in both private and public schools. The food items most commonly available in both vending machines and school stores in all schools were predominately foods of minimal nutritional value (FMNV). Participant report of availability of food items in vending machines and/or school stores was significantly correlated with: (1) participant purchase of each item from those sources, except for energy drinks, milk, fruits, and vegetables; and (2) participants' friends' consumption of items at lunch, for two categories of FMNV (candy, cookies, or cake; soda or sports drinks). Conclusions Despite the Child Nutrition and WIC reauthorization Act of 2004, FMNV were still available in schools, and may be contributing to unhealthy dietary choices and ultimately to health risks. PMID:26645420

GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

PubMed Central

Zhang, Bing; Schmoyer, Denise; Kirov, Stefan; Snoddy, Jay

2004-01-01

Background Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets. Results We have created a web-based tool for data analysis and data visualization for sets of genes called GOTree Machine (GOTM). This tool was originally intended to analyze sets of co-regulated genes identified from microarray analysis but is adaptable for use with other gene sets from other high-throughput analyses. GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets. This system provides user friendly data navigation and visualization. Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study. GOTree Machine is available online at . Conclusion GOTree Machine has a broad application in functional genomic, proteomic and other high-throughput methods that generate large sets of interesting genes; its primary purpose is to help users sort for interesting patterns in gene sets. PMID:14975175

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

NASA Astrophysics Data System (ADS)

Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

2017-10-01

Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

Differential spatial activity patterns of acupuncture by a machine learning based analysis

NASA Astrophysics Data System (ADS)

You, Youbo; Bai, Lijun; Xue, Ting; Zhong, Chongguang; Liu, Zhenyu; Tian, Jie

2011-03-01

Acupoint specificity, lying at the core of the Traditional Chinese Medicine, underlies the theoretical basis of acupuncture application. However, recent studies have reported that acupuncture stimulation at nonacupoint and acupoint can both evoke similar signal intensity decreases in multiple regions. And these regions were spatially overlapped. We used a machine learning based Support Vector Machine (SVM) approach to elucidate the specific neural response pattern induced by acupuncture stimulation. Group analysis demonstrated that stimulation at two different acupoints (belong to the same nerve segment but different meridians) could elicit distinct neural response patterns. Our findings may provide evidence for acupoint specificity.

Paradigms for machine learning

NASA Technical Reports Server (NTRS)

Schlimmer, Jeffrey C.; Langley, Pat

1991-01-01

Five paradigms are described for machine learning: connectionist (neural network) methods, genetic algorithms and classifier systems, empirical methods for inducing rules and decision trees, analytic learning methods, and case-based approaches. Some dimensions are considered along with these paradigms vary in their approach to learning, and the basic methods are reviewed that are used within each framework, together with open research issues. It is argued that the similarities among the paradigms are more important than their differences, and that future work should attempt to bridge the existing boundaries. Finally, some recent developments in the field of machine learning are discussed, and their impact on both research and applications is examined.

Comparison of effects of overload on parameters and performance of samarium-cobalt and strontium-ferrite radially oriented permanent magnet brushless DC motors

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

Demerdash, N.A.; Nehl, T.W.; Nyamusa, T.A.

1985-08-01

Effects of high momentary overloads on the samarium-cobalt and strontium-ferrite permanent magnets and the magnetic field in electronically commutated brushless dc machines, as well as their impact on the associated machine parameters were studied. The effect of overload on the machine parameters, and subsequently on the machine system performance was also investigated. This was accomplished through the combined use of finite element analysis of the magnetic field in such machines, perturbation of the magnetic energies to determine machine inductances, and dynamic simulation of the performance of brushless dc machines, when energized from voltage source inverters. These effects were investigated throughmore » application of the above methods to two equivalent 15 hp brushless dc motors, one of which was built with samarium-cobalt magnets, while the other was built with strontium- ferrite magnets. For momentary overloads as high as 4.5 p.u. magnet flux reductions of 29% and 42% of the no load flux were obtained in the samarium-cobalt and strontiumferrite machines, respectively. Corresponding reductions in the line to line armature inductances of 52% and 46% of the no load values were reported for the samarium-cobalt and strontium-ferrite cases, respectively. The overload affected the profiles and magnitudes of armature induced back emfs. Subsequently, the effects of overload on machine parameters were found to have significant impact on the performance of the machine systems, where findings indicate that the samarium-cobalt unit is more suited for higher overload duties than the strontium-ferrite machine.« less

Target-Oriented High-Resolution SAR Image Formation via Semantic Information Guided Regularizations

NASA Astrophysics Data System (ADS)

Hou, Biao; Wen, Zaidao; Jiao, Licheng; Wu, Qian

2018-04-01

Sparsity-regularized synthetic aperture radar (SAR) imaging framework has shown its remarkable performance to generate a feature enhanced high resolution image, in which a sparsity-inducing regularizer is involved by exploiting the sparsity priors of some visual features in the underlying image. However, since the simple prior of low level features are insufficient to describe different semantic contents in the image, this type of regularizer will be incapable of distinguishing between the target of interest and unconcerned background clutters. As a consequence, the features belonging to the target and clutters are simultaneously affected in the generated image without concerning their underlying semantic labels. To address this problem, we propose a novel semantic information guided framework for target oriented SAR image formation, which aims at enhancing the interested target scatters while suppressing the background clutters. Firstly, we develop a new semantics-specific regularizer for image formation by exploiting the statistical properties of different semantic categories in a target scene SAR image. In order to infer the semantic label for each pixel in an unsupervised way, we moreover induce a novel high-level prior-driven regularizer and some semantic causal rules from the prior knowledge. Finally, our regularized framework for image formation is further derived as a simple iteratively reweighted $\\ell_1$ minimization problem which can be conveniently solved by many off-the-shelf solvers. Experimental results demonstrate the effectiveness and superiority of our framework for SAR image formation in terms of target enhancement and clutters suppression, compared with the state of the arts. Additionally, the proposed framework opens a new direction of devoting some machine learning strategies to image formation, which can benefit the subsequent decision making tasks.

A methodology for assessment of wind turbine noise generation

NASA Astrophysics Data System (ADS)

Kelley, N. D.; Hemphill, R. R.; McKenna, H. E.

1982-05-01

An investigation of the sources of impulsive noise generated by the operation of the Mod 1 2 MW wind turbine was performed to establish criteria for assessing the noise-producing potential of other large wind turbines. Unsteady loading of the rotors was determined to be the cause of the sound pressure, which was generally below 100 Hz. Complaints originated from people in dwellings with a room with a window facing the machine. Indoor monitoring revealed pressure traces in the 31.5 Hz band with energy densities exceeding background by about 30 dB. It was concluded that the sound pressure was conveyed by the walls acting as a diaphragm. The induced vibration coupled with human body fundamental modes to produce a feeling of whole-body vibration. Spectral analyses were made of the vibration fields of the Mod 2, a 17 m Darrieus, and a Mod OA to allow comparison with the nuisance points of the Mod 1. Sound pressure levels were found at certain frequencies which would eliminate the occurrence of acoustic pollution.

Scintillating anticoincidence detection elements design and tests with muons and protons

NASA Astrophysics Data System (ADS)

Gilliot, M.; Chabaud, J.; Baronick, J. P.; Colonges, S.; Laurent, P.

2010-09-01

Design, construction and tests of anticoincidence detection elements are presented. Initially planned to be used as active shielding parts of the anticoincidence detector of the Simbol-X mission, they are aimed to detect cosmic protons and provide veto signal against charged-particle background induced on imaging detectors. The sample is made of a scintillator plate into which grooves are machined and waveshifting fibers glued. The fibers are connected to multianode photomultiplier (PM) tubes. The tubes characteristics have been evaluated for this application. The device has been tested with atmospheric muons that deposit similar energy to that of cosmic protons thanks to a specially designed muon telescope also described in this paper. Tests have also been performed with protons of a tandem accelerator beam line. The response is on average above 10 photoelectrons, which is not complicated to detect, which allows very good detection efficiency as well as very good ability to reject noise. In addition many evolution and performance improvements appear possible.

Dynamic and static fatigue of a machinable glass ceramic

NASA Technical Reports Server (NTRS)

Magida, M. B.; Forrest, K. A.; Heslin, T. M.

1984-01-01

The dynamic and static fatigue behavior of a machinable glass ceramic was investigated to assess its susceptibility to stress corrosion-induced delayed failure. Fracture mechanics techniques were used to analyze the results so that lifetime predictions for components of this material could be made. The resistance to subcritical crack growth of this material was concluded to be only moderate and was found to be dependent on the size of its microstructure.

Reverse-micelle-induced porous pressure-sensitive rubber for wearable human-machine interfaces.

PubMed

Jung, Sungmook; Kim, Ji Hoon; Kim, Jaemin; Choi, Suji; Lee, Jongsu; Park, Inhyuk; Hyeon, Taeghwan; Kim, Dae-Hyeong

2014-07-23

A novel method to produce porous pressure-sensitive rubber is developed. For the controlled size distribution of embedded micropores, solution-based procedures using reverse micelles are adopted. The piezosensitivity of the pressure sensitive rubber is significantly increased by introducing micropores. Using this method, wearable human-machine interfaces are fabricated, which can be applied to the remote control of a robot. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Observation and analysis of pellet material del B drift on MAST

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

Garzotti, L.; Baylor, Larry R; Kochi, F.

2010-01-01

Pellet material deposited in a tokamak plasma experiences a drift towards the low field side of the torus induced by the magnetic field gradient. Plasma fuelling in ITER relies on the beneficial effect of this drift to increase the pellet deposition depth and fuelling efficiency. It is therefore important to analyse this phenomenon in present machines to improve the understanding of the del B induced drift and the accuracy of the predictions for ITER. This paper presents a detailed analysis of pellet material drift in MAST pellet injection experiments based on the unique diagnostic capabilities available on this machine andmore » compares the observations with predictions of state-of-the-art ablation and deposition codes.« less

Risk estimation using probability machines

PubMed Central

2014-01-01

Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

People counting in classroom based on video surveillance

NASA Astrophysics Data System (ADS)

Zhang, Quanbin; Huang, Xiang; Su, Juan

2014-11-01

Currently, the switches of the lights and other electronic devices in the classroom are mainly relied on manual control, as a result, many lights are on while no one or only few people in the classroom. It is important to change the current situation and control the electronic devices intelligently according to the number and the distribution of the students in the classroom, so as to reduce the considerable waste of electronic resources. This paper studies the problem of people counting in classroom based on video surveillance. As the camera in the classroom can not get the full shape contour information of bodies and the clear features information of faces, most of the classical algorithms such as the pedestrian detection method based on HOG (histograms of oriented gradient) feature and the face detection method based on machine learning are unable to obtain a satisfied result. A new kind of dual background updating model based on sparse and low-rank matrix decomposition is proposed in this paper, according to the fact that most of the students in the classroom are almost in stationary state and there are body movement occasionally. Firstly, combining the frame difference with the sparse and low-rank matrix decomposition to predict the moving areas, and updating the background model with different parameters according to the positional relationship between the pixels of current video frame and the predicted motion regions. Secondly, the regions of moving objects are determined based on the updated background using the background subtraction method. Finally, some operations including binarization, median filtering and morphology processing, connected component detection, etc. are performed on the regions acquired by the background subtraction, in order to induce the effects of the noise and obtain the number of people in the classroom. The experiment results show the validity of the algorithm of people counting.

Application of Machine Learning Algorithms to the Study of Noise Artifacts in Gravitational-Wave Data

NASA Technical Reports Server (NTRS)

Biswas, Rahul; Blackburn, Lindy L.; Cao, Junwei; Essick, Reed; Hodge, Kari Alison; Katsavounidis, Erotokritos; Kim, Kyungmin; Young-Min, Kim; Le Bigot, Eric-Olivier; Lee, Chang-Hwan;

Effects of saturation and contrast polarity on the figure-ground organization of color on gray.

PubMed

Dresp-Langley, Birgitta; Reeves, Adam

2014-01-01

Poorly saturated colors are closer to a pure gray than strongly saturated ones and, therefore, appear less "colorful."Color saturation is effectively manipulated in the visual arts for balancing conflicting sensations and moods and for inducing the perception of relative distance in the pictorial plane. While perceptual science has proven quite clearly that the luminance contrast of any hue acts as a self-sufficient cue to relative depth in visual images, the role of color saturation in such figure-ground organization has remained unclear. We presented configurations of colored inducers on gray "test" backgrounds to human observers. Luminance and saturation of the inducers was uniform on each trial, but varied across trials. We ran two separate experimental tasks. In the relative background brightness task, perceptual judgments indicated whether the apparent brightness of the gray test background contrasted with, assimilated to, or appeared equal (no effect) to that of a comparison background with the same luminance contrast. Contrast polarity and its interaction with color saturation affected response proportions for contrast, assimilation and no effect. In the figure-ground task, perceptual judgments indicated whether the inducers appeared to lie in front of, behind, or in the same depth with the background. Strongly saturated inducers produced significantly larger proportions of foreground effects indicating that these inducers stand out as figure against the background. Weakly saturated inducers produced significantly larger proportions of background effects, indicating that these inducers are perceived as lying behind the backgrounds. We infer that color saturation modulates figure-ground organization, both directly by determining relative inducer depth, and indirectly, and in interaction with contrast polarity, by affecting apparent background brightness. The results point toward a hitherto undocumented functional role of color saturation in the genesis of form, and in particular figure-ground percepts in the absence of chromatostereopsis.

Effects of saturation and contrast polarity on the figure-ground organization of color on gray

PubMed Central

Dresp-Langley, Birgitta; Reeves, Adam

2014-01-01

Poorly saturated colors are closer to a pure gray than strongly saturated ones and, therefore, appear less “colorful.”Color saturation is effectively manipulated in the visual arts for balancing conflicting sensations and moods and for inducing the perception of relative distance in the pictorial plane. While perceptual science has proven quite clearly that the luminance contrast of any hue acts as a self-sufficient cue to relative depth in visual images, the role of color saturation in such figure-ground organization has remained unclear. We presented configurations of colored inducers on gray “test” backgrounds to human observers. Luminance and saturation of the inducers was uniform on each trial, but varied across trials. We ran two separate experimental tasks. In the relative background brightness task, perceptual judgments indicated whether the apparent brightness of the gray test background contrasted with, assimilated to, or appeared equal (no effect) to that of a comparison background with the same luminance contrast. Contrast polarity and its interaction with color saturation affected response proportions for contrast, assimilation and no effect. In the figure-ground task, perceptual judgments indicated whether the inducers appeared to lie in front of, behind, or in the same depth with the background. Strongly saturated inducers produced significantly larger proportions of foreground effects indicating that these inducers stand out as figure against the background. Weakly saturated inducers produced significantly larger proportions of background effects, indicating that these inducers are perceived as lying behind the backgrounds. We infer that color saturation modulates figure-ground organization, both directly by determining relative inducer depth, and indirectly, and in interaction with contrast polarity, by affecting apparent background brightness. The results point toward a hitherto undocumented functional role of color saturation in the genesis of form, and in particular figure-ground percepts in the absence of chromatostereopsis. PMID:25339931

High-pressure coolant effect on the surface integrity of machining titanium alloy Ti-6Al-4V: a review

NASA Astrophysics Data System (ADS)

Liu, Wentao; Liu, Zhanqiang

2018-03-01

Machinability improvement of titanium alloy Ti-6Al-4V is a challenging work in academic and industrial applications owing to its low thermal conductivity, low elasticity modulus and high chemical affinity at high temperatures. Surface integrity of titanium alloys Ti-6Al-4V is prominent in estimating the quality of machined components. The surface topography (surface defects and surface roughness) and the residual stress induced by machining Ti-6Al-4V occupy pivotal roles for the sustainability of Ti-6Al-4V components. High-pressure coolant (HPC) is a potential choice in meeting the requirements for the manufacture and application of Ti-6Al-4V. This paper reviews the progress towards the improvements of Ti-6Al4V surface integrity under HPC. Various researches of surface integrity characteristics have been reported. In particularly, surface roughness, surface defects, residual stress as well as work hardening are investigated in order to evaluate the machined surface qualities. Several coolant parameters (including coolant type, coolant pressure and the injection position) deserve investigating to provide the guidance for a satisfied machined surface. The review also provides a clear roadmap for applications of HPC in machining Ti-6Al4V. Experimental studies and analysis are reviewed to better understand the surface integrity under HPC machining process. A distinct discussion has been presented regarding the limitations and highlights of the prospective for machining Ti-6Al4V under HPC.

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images.

PubMed

Joutsijoki, Henry; Haponen, Markus; Rasku, Jyrki; Aalto-Setälä, Katriina; Juhola, Martti

2016-01-01

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a k-NN classifier showing improved accuracy compared to earlier studies.

The role of hemodialysis machines dedication in reducing Hepatitis C transmission in the dialysis setting in Iran: A multicenter prospective interventional study

PubMed Central

Shamshirsaz, Alireza Abdollah; Kamgar, Mohammad; Bekheirnia, Mir Reza; Ayazi, Farzam; Hashemi, Seyed Reza; Bouzari, Navid; Habibzadeh, Mohammad Reza; Pourzahedgilani, Nima; Broumand, Varshasb; Shamshirsaz, Amirhooshang Abdollah; Moradi, Maziyar; Borghei, Mehrdad; Haghighi, Niloofar Nobakht; Broumand, Behrooz

2004-01-01

Background Hepatitis C virus (HCV) infection is a significant problem among patients undergoing maintenance hemodialysis (HD). We conducted a prospective multi-center study to evaluate the effect of dialysis machine separation on the spread of HCV infection. Methods Twelve randomly selected dialysis centers in Tehran, Iran were randomly divided into two groups; those using dedicated machines (D) for HCV infected individuals and those using non-dedicated HD machines (ND). 593 HD cases including 51 HCV positive (RT-PCR) cases and 542 HCV negative patients were enrolled in this study. The prevalence of HCV infection in the D group was 10.1% (range: 4.6%– 13.2%) and it was 7.1% (range: 4.2%–16.8%) in the ND group. During the study conduction 5 new HCV positive cases and 169 new HCV negative cases were added. In the D group, PCR positive patients were dialyzed on dedicated machines. In the ND group all patients shared the same machines. Results In the first follow-up period, the incidence of HCV infection was 1.6% and 4.7% in the D and ND group respectively (p = 0.05). In the second follow-up period, the incidence of HCV infection was 1.3% in the D group and 5.7% in the ND group (p < 0.05). Conclusions In this study the incidence of HCV in HD patients decreased by the use of dedicated HD machines for HCV infected patients. Additional studies may help to clarify the role of machine dedication in conjunction with application of universal precautions in reducing HCV transmission. PMID:15469615

Software platform virtualization in chemistry research and university teaching

PubMed Central

2009-01-01

Background Modern chemistry laboratories operate with a wide range of software applications under different operating systems, such as Windows, LINUX or Mac OS X. Instead of installing software on different computers it is possible to install those applications on a single computer using Virtual Machine software. Software platform virtualization allows a single guest operating system to execute multiple other operating systems on the same computer. We apply and discuss the use of virtual machines in chemistry research and teaching laboratories. Results Virtual machines are commonly used for cheminformatics software development and testing. Benchmarking multiple chemistry software packages we have confirmed that the computational speed penalty for using virtual machines is low and around 5% to 10%. Software virtualization in a teaching environment allows faster deployment and easy use of commercial and open source software in hands-on computer teaching labs. Conclusion Software virtualization in chemistry, mass spectrometry and cheminformatics is needed for software testing and development of software for different operating systems. In order to obtain maximum performance the virtualization software should be multi-core enabled and allow the use of multiprocessor configurations in the virtual machine environment. Server consolidation, by running multiple tasks and operating systems on a single physical machine, can lead to lower maintenance and hardware costs especially in small research labs. The use of virtual machines can prevent software virus infections and security breaches when used as a sandbox system for internet access and software testing. Complex software setups can be created with virtual machines and are easily deployed later to multiple computers for hands-on teaching classes. We discuss the popularity of bioinformatics compared to cheminformatics as well as the missing cheminformatics education at universities worldwide. PMID:20150997

75 FR 9901 - Proposed Data Collections Submitted for Public Comment and Recommendations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-04

...). Background and Brief Description Cigarettes are currently ranked as full-flavor, light or ultralight on the... smokers of cigarettes of different machine-smoked yield categories. Comparison of cigarette smoke... cigarette yield category, biomarkers of exposure, and measures of cardiovascular reactivity. The study has...

Inverse Problems in Geodynamics Using Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R. N.

2018-01-01

During the past few decades numerical studies have been widely employed to explore the style of circulation and mixing in the mantle of Earth and other planets. However, in geodynamical studies there are many properties from mineral physics, geochemistry, and petrology in these numerical models. Machine learning, as a computational statistic-related technique and a subfield of artificial intelligence, has rapidly emerged recently in many fields of sciences and engineering. We focus here on the application of supervised machine learning (SML) algorithms in predictions of mantle flow processes. Specifically, we emphasize on estimating mantle properties by employing machine learning techniques in solving an inverse problem. Using snapshots of numerical convection models as training samples, we enable machine learning models to determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at midmantle depths. Employing support vector machine algorithms, we show that SML techniques can successfully predict the magnitude of mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex geodynamic problems in mantle dynamics by employing deep learning algorithms for putting constraints on properties such as viscosity, elastic parameters, and the nature of thermal and chemical anomalies.

Retrieving the Quantitative Chemical Information at Nanoscale from Scanning Electron Microscope Energy Dispersive X-ray Measurements by Machine Learning

NASA Astrophysics Data System (ADS)

Jany, B. R.; Janas, A.; Krok, F.

2017-11-01

The quantitative composition of metal alloy nanowires on InSb(001) semiconductor surface and gold nanostructures on germanium surface is determined by blind source separation (BSS) machine learning (ML) method using non negative matrix factorization (NMF) from energy dispersive X-ray spectroscopy (EDX) spectrum image maps measured in a scanning electron microscope (SEM). The BSS method blindly decomposes the collected EDX spectrum image into three source components, which correspond directly to the X-ray signals coming from the supported metal nanostructures, bulk semiconductor signal and carbon background. The recovered quantitative composition is validated by detailed Monte Carlo simulations and is confirmed by separate cross-sectional TEM EDX measurements of the nanostructures. This shows that SEM EDX measurements together with machine learning blind source separation processing could be successfully used for the nanostructures quantitative chemical composition determination.

Molecular Dynamics Modeling and Simulation of Diamond Cutting of Cerium.

PubMed

Zhang, Junjie; Zheng, Haibing; Shuai, Maobing; Li, Yao; Yang, Yang; Sun, Tao

2017-12-01

The coupling between structural phase transformations and dislocations induces challenges in understanding the deformation behavior of metallic cerium at the nanoscale. In the present work, we elucidate the underlying mechanism of cerium under ultra-precision diamond cutting by means of molecular dynamics modeling and simulations. The molecular dynamics model of diamond cutting of cerium is established by assigning empirical potentials to describe atomic interactions and evaluating properties of two face-centered cubic cerium phases. Subsequent molecular dynamics simulations reveal that dislocation slip dominates the plastic deformation of cerium under the cutting process. In addition, the analysis based on atomic radial distribution functions demonstrates that there are trivial phase transformations from the γ-Ce to the δ-Ce occurred in both machined surface and formed chip. Following investigations on machining parameter dependence reveal the optimal machining conditions for achieving high quality of machined surface of cerium.

Molecular Dynamics Modeling and Simulation of Diamond Cutting of Cerium

NASA Astrophysics Data System (ADS)

Zhang, Junjie; Zheng, Haibing; Shuai, Maobing; Li, Yao; Yang, Yang; Sun, Tao

2017-07-01

The coupling between structural phase transformations and dislocations induces challenges in understanding the deformation behavior of metallic cerium at the nanoscale. In the present work, we elucidate the underlying mechanism of cerium under ultra-precision diamond cutting by means of molecular dynamics modeling and simulations. The molecular dynamics model of diamond cutting of cerium is established by assigning empirical potentials to describe atomic interactions and evaluating properties of two face-centered cubic cerium phases. Subsequent molecular dynamics simulations reveal that dislocation slip dominates the plastic deformation of cerium under the cutting process. In addition, the analysis based on atomic radial distribution functions demonstrates that there are trivial phase transformations from the γ-Ce to the δ-Ce occurred in both machined surface and formed chip. Following investigations on machining parameter dependence reveal the optimal machining conditions for achieving high quality of machined surface of cerium.

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis

PubMed Central

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

Background The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. Methods and finding We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. Conclusions According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction. PMID:28060903

Tempo in electronic gaming machines affects behavior among at-risk gamblers.

PubMed

Mentzoni, Rune A; Laberg, Jon Christian; Brunborg, Geir Scott; Molde, Helge; Pallesen, Ståle

2012-09-01

Background and aims Electronic gaming machines (EGM) may be a particularly addictive form of gambling, and gambling speed is believed to contribute to the addictive potential of such machines. The aim of the current study was to generate more knowledge concerning speed as a structural characteristic in gambling, by comparing the effects of three different bet-to-outcome intervals (BOI) on gamblers bet-sizes, game evaluations and illusion of control during gambling on a computer simulated slot machine. Furthermore, we investigated whether problem gambling moderates effects of BOI on gambling behavior and cognitions. Methods 62 participants played a computerized slot machine with either fast (400 ms), medium (1700 ms) or slow (3000 ms) BOI. SOGS-R was used to measure pre-existing gambling problems. Mean bet size, game evaluations and illusion of control comprised the dependent variables. Results Gambling speed had no overall effect on either mean bet size, game evaluations or illusion of control, but in the 400 ms condition, at-risk gamblers (SOGS-R score > 0) employed higher bet sizes compared to no-risk (SOGS-R score = 0) gamblers. Conclusions The findings corroborate and elaborate on previous studies and indicate that restrictions on gambling speed may serve as a harm reducing effort for at-risk gamblers.

Engineered Surface Properties of Porous Tungsten from Cryogenic Machining

NASA Astrophysics Data System (ADS)

Schoop, Julius Malte

Porous tungsten is used to manufacture dispenser cathodes due to it refractory properties. Surface porosity is critical to functional performance of dispenser cathodes because it allows for an impregnated ceramic compound to migrate to the emitting surface, lowering its work function. Likewise, surface roughness is important because it is necessary to ensure uniform wetting of the molten impregnate during high temperature service. Current industry practice to achieve surface roughness and surface porosity requirements involves the use of a plastic infiltrant during machining. After machining, the infiltrant is baked and the cathode pellet is impregnated. In this context, cryogenic machining is investigated as a substitutionary process for the current plastic infiltration process. Along with significant reductions in cycle time and resource use, surface quality of cryogenically machined un-infiltrated (as-sintered) porous tungsten has been shown to significantly outperform dry machining. The present study is focused on examining the relationship between machining parameters and cooling condition on the as-machined surface integrity of porous tungsten. The effects of cryogenic pre-cooling, rake angle, cutting speed, depth of cut and feed are all taken into consideration with respect to machining-induced surface morphology. Cermet and Polycrystalline diamond (PCD) cutting tools are used to develop high performance cryogenic machining of porous tungsten. Dry and pre-heated machining were investigated as a means to allow for ductile mode machining, yet severe tool-wear and undesirable smearing limited the feasibility of these approaches. By using modified PCD cutting tools, high speed machining of porous tungsten at cutting speeds up to 400 m/min is achieved for the first time. Beyond a critical speed, brittle fracture and built-up edge are eliminated as the result of a brittle to ductile transition. A model of critical chip thickness ( hc ) effects based on cutting force, temperature and surface roughness data is developed and used to study the deformation mechanisms of porous tungsten under different machining conditions. It is found that when hmax = hc, ductile mode machining of otherwise highly brittle porous tungsten is possible. The value of hc is approximately the same as the average ligament size of the 80% density porous tungsten workpiece.

An observational study investigating the association of ultrasonographically assessed machine milking-induced changes in teat condition and teat-end shape in dairy cows.

PubMed

Wieland, M; Virkler, P D; Borkowski, A H; Älveby, N; Wood, P; Nydam, D V

2018-06-21

Mechanical forces during machine milking induce changes in teat condition which can be differentiated into short-term and long-term changes. Machine milking-induced short-term changes in teat condition (STC) are defined as tissue responses to a single milking and have been associated with the risk of new intramammary infection. Albeit, their association with teat characteristics, such as teat-end shape, has not been investigated by rigorous methods. The primary objective was to determine the association of STC, as measured by ultrasonography, with teat-end shape. The second objective was to describe possible differences in the recovery time of teat tissue after machine milking among teats with different teat-end shapes. Holstein cows (n=128) were enrolled in an observational study, housed in free-stall pens with sand bedding and milked three times a day. Ultrasonography of the left front and right hind teat was performed after teat preparation before milking (t-1), immediately after milking (t 0) and 1, 3, 5 and 7 h after milking (t 1, t 3, t 5, t 7). The teat tissue parameters measured from ultrasound scans were teat canal length, teat-end diameter, teat-end diameter at the midpoint between the distal and proximal end of the teat canal, teat wall thickness, and teat cistern width. Teat-end shape was assessed visually and classified into three categories: pointed, flat and round. Multivariable linear regression analyses showed differences in the relative change of teat tissue parameters (compared with t-1) at t 0 among teats with different teat-end shapes, with most parameters showing the largest change for round teats. The premilking values were reached (recovery time) after 7 h in teats with a pointed teat-end shape, whereas recovery time was greater than 7 h in teats with flat and round teat-end shapes. Under the same liner and milking machine conditions, teats with a round teat-end shape had the most severe short-term changes. The results of this observational study indicated that teat-end shape may be one of the factors that contribute to the severity of STC.

Reconstructed Solar-Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-Induced Fluorescence

NASA Astrophysics Data System (ADS)

Gentine, P.; Alemohammad, S. H.

2018-04-01

Solar-induced fluorescence (SIF) observations from space have resulted in major advancements in estimating gross primary productivity (GPP). However, current SIF observations remain spatially coarse, infrequent, and noisy. Here we develop a machine learning approach using surface reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) channels to reproduce SIF normalized by clear sky surface irradiance from the Global Ozone Monitoring Experiment-2 (GOME-2). The resulting product is a proxy for ecosystem photosynthetically active radiation absorbed by chlorophyll (fAPARCh). Multiplying this new product with a MODIS estimate of photosynthetically active radiation provides a new MODIS-only reconstruction of SIF called Reconstructed SIF (RSIF). RSIF exhibits much higher seasonal and interannual correlation than the original SIF when compared with eddy covariance estimates of GPP and two reference global GPP products, especially in dry and cold regions. RSIF also reproduces intense productivity regions such as the U.S. Corn Belt contrary to typical vegetation indices and similarly to SIF.

White matter microstructure changes induced by motor skill learning utilizing a body machine interface.

PubMed

Wang, Xue; Casadio, Maura; Weber, Kenneth A; Mussa-Ivaldi, Ferdinando A; Parrish, Todd B

2014-03-01

The purpose of this study is to identify white matter microstructure changes following bilateral upper extremity motor skill training to increase our understanding of learning-induced structural plasticity and enhance clinical strategies in physical rehabilitation. Eleven healthy subjects performed two visuo-spatial motor training tasks over 9 sessions (2-3 sessions per week). Subjects controlled a cursor with bilateral simultaneous movements of the shoulders and upper arms using a body machine interface. Before the start and within 2days of the completion of training, whole brain diffusion tensor MR imaging data were acquired. Motor training increased fractional anisotropy (FA) values in the posterior and anterior limbs of the internal capsule, the corona radiata, and the body of the corpus callosum by 4.19% on average indicating white matter microstructure changes induced by activity-dependent modulation of axon number, axon diameter, or myelin thickness. These changes may underlie the functional reorganization associated with motor skill learning. Copyright © 2013 Elsevier Inc. All rights reserved.

Detail of C.W.E. Storage Shed (Bldg. 126) monorails and safety ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Detail of C.W.E. Storage Shed (Bldg. 126) monorails and safety sign for track workers. Machine Shop (Bldg. 134) is in the background - Atchison, Topeka, Santa Fe Railroad, Albuquerque Shops, C.W.E. Storage Shed, 908 Second Street, Southwest, Albuquerque, Bernalillo County, NM

Cost per case or total cost? The potential of prevention of hand injuries in young children – Retrospective and prospective studies

PubMed Central

Ljungberg, Elinor M; Carlsson, Katarina Steen; Dahlin, Lars B

2008-01-01

Background Health-care costs for hand and forearm injuries in young children are poorly documented. We examined costs in 533 children injured years 1996–2003. Methods Health-care costs and costs for lost productivity were retrospectively calculated in children from three catchment areas in Sweden. Seven case categories corresponding to alternative prevention strategies were constructed. Results Over time, diminishing number of ward days reduced the health-care cost per case. Among children, the cost of lost productivity due to parental leave was 14 percent of total cost. Fingertip injuries had low median costs but high total costs due to their frequency. Complex injuries by machine or rifle had high costs per case, and despite a low number of cases, total cost was high. Type of injury, surgery and physiotherapy sessions were associated with variations in health-care cost. Low age and ethnic background had a significant effect on number of ward days. Conclusion The costs per hand injury for children were lower compared to adults due to both lower health-care costs and to the fact that parents had comparatively short periods of absence from work. Frequent simple fingertip injuries and rare complex injuries induce high costs for society. Such costs should be related to costs for prevention of these injuries. PMID:18606018

Snack food as a modulator of human resting-state functional connectivity.

PubMed

Mendez-Torrijos, Andrea; Kreitz, Silke; Ivan, Claudiu; Konerth, Laura; Rösch, Julie; Pischetsrieder, Monika; Moll, Gunther; Kratz, Oliver; Dörfler, Arnd; Horndasch, Stefanie; Hess, Andreas

2018-04-04

To elucidate the mechanisms of how snack foods may induce non-homeostatic food intake, we used resting state functional magnetic resonance imaging (fMRI), as resting state networks can individually adapt to experience after short time exposures. In addition, we used graph theoretical analysis together with machine learning techniques (support vector machine) to identifying biomarkers that can categorize between high-caloric (potato chips) vs. low-caloric (zucchini) food stimulation. Seventeen healthy human subjects with body mass index (BMI) 19 to 27 underwent 2 different fMRI sessions where an initial resting state scan was acquired, followed by visual presentation of different images of potato chips and zucchini. There was then a 5-minute pause to ingest food (day 1=potato chips, day 3=zucchini), followed by a second resting state scan. fMRI data were further analyzed using graph theory analysis and support vector machine techniques. Potato chips vs. zucchini stimulation led to significant connectivity changes. The support vector machine was able to accurately categorize the 2 types of food stimuli with 100% accuracy. Visual, auditory, and somatosensory structures, as well as thalamus, insula, and basal ganglia were found to be important for food classification. After potato chips consumption, the BMI was associated with the path length and degree in nucleus accumbens, middle temporal gyrus, and thalamus. The results suggest that high vs. low caloric food stimulation in healthy individuals can induce significant changes in resting state networks. These changes can be detected using graph theory measures in conjunction with support vector machine. Additionally, we found that the BMI affects the response of the nucleus accumbens when high caloric food is consumed.

Error compensation for thermally induced errors on a machine tool

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

Krulewich, D.A.

1996-11-08

Heat flow from internal and external sources and the environment create machine deformations, resulting in positioning errors between the tool and workpiece. There is no industrially accepted method for thermal error compensation. A simple model has been selected that linearly relates discrete temperature measurements to the deflection. The biggest problem is how to locate the temperature sensors and to determine the number of required temperature sensors. This research develops a method to determine the number and location of temperature measurements.

Whole-body Vibration Exposure of Drill Operators in Iron Ore Mines and Role of Machine-Related, Individual, and Rock-Related Factors

PubMed Central

Chaudhary, Dhanjee Kumar; Bhattacherjee, Ashis; Patra, Aditya Kumar; Chau, Nearkasen

2015-01-01

Background This study aimed to assess the whole-body vibration (WBV) exposure among large blast hole drill machine operators with regard to the International Organization for Standardization (ISO) recommended threshold values and its association with machine- and rock-related factors and workers' individual characteristics. Methods The study population included 28 drill machine operators who had worked in four opencast iron ore mines in eastern India. The study protocol comprised the following: measurements of WBV exposure [frequency weighted root mean square (RMS) acceleration (m/s2)], machine-related data (manufacturer of machine, age of machine, seat height, thickness, and rest height) collected from mine management offices, measurements of rock hardness, uniaxial compressive strength and density, and workers' characteristics via face-to-face interviews. Results More than 90% of the operators were exposed to a higher level WBV than the ISO upper limit and only 3.6% between the lower and upper limits, mainly in the vertical axis. Bivariate correlations revealed that potential predictors of total WBV exposure were: machine manufacturer (r = 0.453, p = 0.015), age of drill (r = 0.533, p = 0.003), and hardness of rock (r = 0.561, p = 0.002). The stepwise multiple regression model revealed that the potential predictors are age of operator (regression coefficient β = −0.052, standard error SE = 0.023), manufacturer (β = 1.093, SE = 0.227), rock hardness (β = 0.045, SE = 0.018), uniaxial compressive strength (β = 0.027, SE = 0.009), and density (β = –1.135, SE = 0.235). Conclusion Prevention should include using appropriate machines to handle rock hardness, rock uniaxial compressive strength and density, and seat improvement using ergonomic approaches such as including a suspension system. PMID:26929838

An application of eddy current damping effect on single point diamond turning of titanium alloys

NASA Astrophysics Data System (ADS)

Yip, W. S.; To, S.

2017-11-01

Titanium alloys Ti6Al4V (TC4) have been popularly applied in many industries. They have superior material properties including an excellent strength-to-weight ratio and corrosion resistance. However, they are regarded as difficult to cut materials; serious tool wear, a high level of cutting vibration and low surface integrity are always involved in machining processes especially in ultra-precision machining (UPM). In this paper, a novel hybrid machining technology using an eddy current damping effect is firstly introduced in UPM to suppress machining vibration and improve the machining performance of titanium alloys. A magnetic field was superimposed on samples during single point diamond turning (SPDT) by exposing the samples in between two permanent magnets. When the titanium alloys were rotated within a magnetic field in the SPDT, an eddy current was generated through a stationary magnetic field inside the titanium alloys. An eddy current generated its own magnetic field with the opposite direction of the external magnetic field leading a repulsive force, compensating for the machining vibration induced by the turning process. The experimental results showed a remarkable improvement in cutting force variation, a significant reduction in adhesive tool wear and an extreme long chip formation in comparison to normal SPDT of titanium alloys, suggesting the enhancement of the machinability of titanium alloys using an eddy current damping effect. An eddy current damping effect was firstly introduced in the area of UPM to deliver the results of outstanding machining performance.

Summary of the Optics, IR, Injection, Operations, Reliability and Instrumentation Working Group

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

Wienands, U.; /SLAC; Funakoshi, Y.

2012-04-20

The facilities reported on are all in a fairly mature state of operation, as evidenced by the very detailed studies and correction schemes that all groups are working on. First- and higher-order aberrations are diagnosed and planned to be corrected. Very detailed beam measurements are done to get a global picture of the beam dynamics. More than other facilities the high-luminosity colliders are struggling with experimental background issues, mitigation of which is a permanent challenge. The working group dealt with a very wide rage of practical issues which limit performance of the machines and compared their techniques of operations andmore » their performance. We anticipate this to be a first attempt. In a future workshop in this series, we propose to attempt more fundamental comparisons of each machine, including design parameters. For example, DAPHNE and KEKB employ a finite crossing angle. The minimum value of {beta}*{sub y} attainable at KEKB seems to relate to this scheme. Effectiveness of compensation solenoids and turn-by-turn BPMs etc. should be examined in more detail. In the near future, CESR-C and VEPP-2000 will start their operation. We expect to hear important new experiences from these machines; in particular VEPP-2000 will be the first machine to have adopted round beams. At SLAC and KEK, next generation B Factories are being considered. It will be worthwhile to discuss the design issues of these machines based on the experiences of the existing factory machines.« less

Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain

PubMed Central

Tighe, Patrick J.; Harle, Christopher A.; Hurley, Robert W.; Aytug, Haldun; Boezaart, Andre P.; Fillingim, Roger B.

2015-01-01

Background Given their ability to process highly dimensional datasets with hundreds of variables, machine learning algorithms may offer one solution to the vexing challenge of predicting postoperative pain. Methods Here, we report on the application of machine learning algorithms to predict postoperative pain outcomes in a retrospective cohort of 8071 surgical patients using 796 clinical variables. Five algorithms were compared in terms of their ability to forecast moderate to severe postoperative pain: Least Absolute Shrinkage and Selection Operator (LASSO), gradient-boosted decision tree, support vector machine, neural network, and k-nearest neighbor, with logistic regression included for baseline comparison. Results In forecasting moderate to severe postoperative pain for postoperative day (POD) 1, the LASSO algorithm, using all 796 variables, had the highest accuracy with an area under the receiver-operating curve (ROC) of 0.704. Next, the gradient-boosted decision tree had an ROC of 0.665 and the k-nearest neighbor algorithm had an ROC of 0.643. For POD 3, the LASSO algorithm, using all variables, again had the highest accuracy, with an ROC of 0.727. Logistic regression had a lower ROC of 0.5 for predicting pain outcomes on POD 1 and 3. Conclusions Machine learning algorithms, when combined with complex and heterogeneous data from electronic medical record systems, can forecast acute postoperative pain outcomes with accuracies similar to methods that rely only on variables specifically collected for pain outcome prediction. PMID:26031220

Scaling of titanium implants entrains inflammation-induced osteolysis

PubMed Central

Eger, Michal; Sterer, Nir; Liron, Tamar; Kohavi, David; Gabet, Yankel

2017-01-01

With millions of new dental and orthopedic implants inserted annually, periprosthetic osteolysis becomes a major concern. In dentistry, peri-implantitis management includes cleaning using ultrasonic scaling. We examined whether ultrasonic scaling releases titanium particles and induces inflammation and osteolysis. Titanium discs with machined, sandblasted/acid-etched and sandblasted surfaces were subjected to ultrasonic scaling and we physically and chemically characterized the released particles. These particles induced a severe inflammatory response in macrophages and stimulated osteoclastogenesis. The number of released particles and their chemical composition and nanotopography had a significant effect on the inflammatory response. Sandblasted surfaces released the highest number of particles with the greatest nanoroughness properties. Particles from sandblasted/acid-etched discs induced a milder inflammatory response than those from sandblasted discs but a stronger inflammatory response than those from machined discs. Titanium particles were then embedded in fibrin membranes placed on mouse calvariae for 5 weeks. Using micro-CT, we observed that particles from sandblasted discs induced more osteolysis than those from sandblasted/acid-etched discs. In summary, ultrasonic scaling of titanium implants releases particles in a surface type-dependent manner and may aggravate peri-implantitis. Future studies should assess whether surface roughening affects the extent of released wear particles and aseptic loosening of orthopedic implants. PMID:28059080

Spatially Regularized Machine Learning for Task and Resting-state fMRI

PubMed Central

Song, Xiaomu; Panych, Lawrence P.; Chen, Nan-kuei

2015-01-01

Background Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades. New Method A spatially regularized support vector machine (SVM) technique was developed for the reliable brain mapping in task- and resting-state. Unlike most existing SVM-based brain mapping techniques, which implement supervised classifications of specific brain functional states or disorders, the proposed method performs a semi-supervised classification for the general brain function mapping where spatial correlation of fMRI is integrated into the SVM learning. The method can adapt to intra- and inter-subject variations induced by fMRI nonstationarity, and identify a true boundary between active and inactive voxels, or between functionally connected and unconnected voxels in a feature space. Results The method was evaluated using synthetic and experimental data at the individual and group level. Multiple features were evaluated in terms of their contributions to the spatially regularized SVM learning. Reliable mapping results in both task- and resting-state were obtained from individual subjects and at the group level. Comparison with Existing Methods A comparison study was performed with independent component analysis, general linear model, and correlation analysis methods. Experimental results indicate that the proposed method can provide a better or comparable mapping performance at the individual and group level. Conclusions The proposed method can provide accurate and reliable mapping of brain function in task- and resting-state, and is applicable to a variety of quantitative fMRI studies. PMID:26470627

Nonlocal interactions in color perception: nonlinear processing of chromatic signals from remote inducers.

PubMed

Wachtler, T; Albright, T D; Sejnowski, T J

2001-05-01

The perceived color of an object depends on the chromaticity of its immediate background. But color appearance is also influenced by remote chromaticities. To quantify these influences, the effects of remote color fields on the appearance of a fixated 2 degrees test field were measured using a forced-choice method. Changes in the appearance of the test field were induced by chromaticity changes of the background and of 2 degrees color fields not adjacent to the test field. The appearance changes induced by the color of the background corresponded to a fraction of between 0.5 and 0.95 of the cone contrast of the background change, depending on the observer. The magnitude of induction by the background color was modulated on average by 7.6% by chromaticity changes in the remote color fields. Chromaticity changes in the remote fields had virtually no inducing effect when they occurred without a change in background color. The spatial range of these chromatic interactions extended over at least 10 degrees from the fovea. They were established within the first few hundred milliseconds after the change of background color and depended only weakly on the number of inducing fields. These results may be interpreted as reflecting rapid chromatic interactions that support robustness of color vision under changing viewing conditions.

Investigation the gas film in micro scale induced error on the performance of the aerostatic spindle in ultra-precision machining

NASA Astrophysics Data System (ADS)

Chen, Dongju; Huo, Chen; Cui, Xianxian; Pan, Ri; Fan, Jinwei; An, Chenhui

2018-05-01

The objective of this work is to study the influence of error induced by gas film in micro-scale on the static and dynamic behavior of a shaft supported by the aerostatic bearings. The static and dynamic balance models of the aerostatic bearing are presented by the calculated stiffness and damping in micro scale. The static simulation shows that the deformation of aerostatic spindle system in micro scale is decreased. For the dynamic behavior, both the stiffness and damping in axial and radial directions are increased in micro scale. The experiments of the stiffness and rotation error of the spindle show that the deflection of the shaft resulting from the calculating parameters in the micro scale is very close to the deviation of the spindle system. The frequency information in transient analysis is similar to the actual test, and they are also higher than the results from the traditional case without considering micro factor. Therefore, it can be concluded that the value considering micro factor is closer to the actual work case of the aerostatic spindle system. These can provide theoretical basis for the design and machining process of machine tools.

Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques.

PubMed

Feng, Jingwen; Feng, Tong; Yang, Chengwen; Wang, Wei; Sa, Yu; Feng, Yuanming

2018-06-01

This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogenous leukemia K562 cells by cis-platinum (DDP). A newly developed technique of polarization diffraction imaging flow cytometry (p-DIFC) was performed to acquire diffraction images of the cells in three different statuses (viable, early apoptotic and late apoptotic/necrotic) after cell separation through fluorescence activated cell sorting with Annexin V-PE and SYTOX® Green double staining. The texture features of the diffraction images were extracted with in-house software based on the Gray-level co-occurrence matrix algorithm to generate datasets for cell classification with supervised machine learning method. Therefore, this new method has been verified in hydrogen peroxide induced apoptosis model of HL-60. Results show that accuracy of higher than 90% was achieved respectively in independent test datasets from each cell type based on logistic regression with ridge estimators, which indicated that p-DIFC system has a great potential in predicting and classifying cells in different stages of apoptosis.

Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.

PubMed

Vallmuur, Kirsten; Marucci-Wellman, Helen R; Taylor, Jennifer A; Lehto, Mark; Corns, Helen L; Smith, Gordon S

2016-04-01

Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database. The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of 'big injury narrative data' opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed Central

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others. PMID:28376093

Extracting laboratory test information from biomedical text

PubMed Central

Kang, Yanna Shen; Kayaalp, Mehmet

2013-01-01

Background: No previous study reported the efficacy of current natural language processing (NLP) methods for extracting laboratory test information from narrative documents. This study investigates the pathology informatics question of how accurately such information can be extracted from text with the current tools and techniques, especially machine learning and symbolic NLP methods. The study data came from a text corpus maintained by the U.S. Food and Drug Administration, containing a rich set of information on laboratory tests and test devices. Methods: The authors developed a symbolic information extraction (SIE) system to extract device and test specific information about four types of laboratory test entities: Specimens, analytes, units of measures and detection limits. They compared the performance of SIE and three prominent machine learning based NLP systems, LingPipe, GATE and BANNER, each implementing a distinct supervised machine learning method, hidden Markov models, support vector machines and conditional random fields, respectively. Results: Machine learning systems recognized laboratory test entities with moderately high recall, but low precision rates. Their recall rates were relatively higher when the number of distinct entity values (e.g., the spectrum of specimens) was very limited or when lexical morphology of the entity was distinctive (as in units of measures), yet SIE outperformed them with statistically significant margins on extracting specimen, analyte and detection limit information in both precision and F-measure. Its high recall performance was statistically significant on analyte information extraction. Conclusions: Despite its shortcomings against machine learning methods, a well-tailored symbolic system may better discern relevancy among a pile of information of the same type and may outperform a machine learning system by tapping into lexically non-local contextual information such as the document structure. PMID:24083058

Interpreting linear support vector machine models with heat map molecule coloring

PubMed Central

2011-01-01

Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor. PMID:21439031

CLIC CDR - physics and detectors: CLIC conceptual design report.

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

Berger, E.; Demarteau, M.; Repond, J.

This report forms part of the Conceptual Design Report (CDR) of the Compact LInear Collider (CLIC). The CLIC accelerator complex is described in a separate CDR volume. A third document, to appear later, will assess strategic scenarios for building and operating CLIC in successive center-of-mass energy stages. It is anticipated that CLIC will commence with operation at a few hundred GeV, giving access to precision standard-model physics like Higgs and top-quark physics. Then, depending on the physics landscape, CLIC operation would be staged in a few steps ultimately reaching the maximum 3 TeV center-of-mass energy. Such a scenario would maximizemore » the physics potential of CLIC providing new physics discovery potential over a wide range of energies and the ability to make precision measurements of possible new states previously discovered at the Large Hadron Collider (LHC). The main purpose of this document is to address the physics potential of a future multi-TeV e{sup +}e{sup -} collider based on CLIC technology and to describe the essential features of a detector that are required to deliver the full physics potential of this machine. The experimental conditions at CLIC are significantly more challenging than those at previous electron-positron colliders due to the much higher levels of beam-induced backgrounds and the 0.5 ns bunch-spacing. Consequently, a large part of this report is devoted to understanding the impact of the machine environment on the detector with the aim of demonstrating, with the example of realistic detector concepts, that high precision physics measurements can be made at CLIC. Since the impact of background increases with energy, this document concentrates on the detector requirements and physics measurements at the highest CLIC center-of-mass energy of 3 TeV. One essential output of this report is the clear demonstration that a wide range of high precision physics measurements can be made at CLIC with detectors which are challenging, but considered feasible following a realistic future R&D program.« less

Affective processes in human-automation interactions.

PubMed

Merritt, Stephanie M

2011-08-01

This study contributes to the literature on automation reliance by illuminating the influences of user moods and emotions on reliance on automated systems. Past work has focused predominantly on cognitive and attitudinal variables, such as perceived machine reliability and trust. However, recent work on human decision making suggests that affective variables (i.e., moods and emotions) are also important. Drawing from the affect infusion model, significant effects of affect are hypothesized. Furthermore, a new affectively laden attitude termed liking is introduced. Participants watched video clips selected to induce positive or negative moods, then interacted with a fictitious automated system on an X-ray screening task At five time points, important variables were assessed including trust, liking, perceived machine accuracy, user self-perceived accuracy, and reliance.These variables, along with propensity to trust machines and state affect, were integrated in a structural equation model. Happiness significantly increased trust and liking for the system throughout the task. Liking was the only variable that significantly predicted reliance early in the task. Trust predicted reliance later in the task, whereas perceived machine accuracy and user self-perceived accuracy had no significant direct effects on reliance at any time. Affective influences on automation reliance are demonstrated, suggesting that this decision-making process may be less rational and more emotional than previously acknowledged. Liking for a new system may be key to appropriate reliance, particularly early in the task. Positive affect can be easily induced and may be a lever for increasing liking.

Temperature Measurement and Numerical Prediction in Machining Inconel 718.

PubMed

Díaz-Álvarez, José; Tapetado, Alberto; Vázquez, Carmen; Miguélez, Henar

2017-06-30

Thermal issues are critical when machining Ni-based superalloy components designed for high temperature applications. The low thermal conductivity and extreme strain hardening of this family of materials results in elevated temperatures around the cutting area. This elevated temperature could lead to machining-induced damage such as phase changes and residual stresses, resulting in reduced service life of the component. Measurement of temperature during machining is crucial in order to control the cutting process, avoiding workpiece damage. On the other hand, the development of predictive tools based on numerical models helps in the definition of machining processes and the obtainment of difficult to measure parameters such as the penetration of the heated layer. However, the validation of numerical models strongly depends on the accurate measurement of physical parameters such as temperature, ensuring the calibration of the model. This paper focuses on the measurement and prediction of temperature during the machining of Ni-based superalloys. The temperature sensor was based on a fiber-optic two-color pyrometer developed for localized temperature measurements in turning of Inconel 718. The sensor is capable of measuring temperature in the range of 250 to 1200 °C. Temperature evolution is recorded in a lathe at different feed rates and cutting speeds. Measurements were used to calibrate a simplified numerical model for prediction of temperature fields during turning.

4. Contextual view to east of the Southern Pacific Railroad ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

4. Contextual view to east of the Southern Pacific Railroad Carlin Shops buildings at Carlin, Nevada. The Roundhouse Machine Shop Extension is at left, Oil House at center background, and Engine Stores Building at right (135mm lens). - Southern Pacific Railroad, Carlin Shops, Foot of Sixth Street, Carlin, Elko County, NV

3. Contextual view to south of the Southern Pacific Railroad ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

3. Contextual view to south of the Southern Pacific Railroad Carlin Shops buildings at Carlin, Nevada. The Oil House is at left, Engine Stores at center background, and Roundhouse Machine Shop Extension at right (90mm lens). - Southern Pacific Railroad, Carlin Shops, Foot of Sixth Street, Carlin, Elko County, NV

76 FR 5384 - Agency Information Collection Activities; Submission for Office of Management and Budget Review...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-31

... Request; Restaurant Menu and Vending Machine Labeling: Registration for Small Chains Under Section 4205 of... following proposed collection of information to OMB for review and clearance. I. Background Restaurant Menu... chain restaurants and similar retail food establishments (SRFE) with 20 or more locations, as well as...

Liquid-Assisted Femtosecond Laser Precision-Machining of Silica.

PubMed

Cao, Xiao-Wen; Chen, Qi-Dai; Fan, Hua; Zhang, Lei; Juodkazis, Saulius; Sun, Hong-Bo

2018-04-28

We report a systematical study on the liquid assisted femtosecond laser machining of quartz plate in water and under different etching solutions. The ablation features in liquid showed a better structuring quality and improved resolution with 1/3~1/2 smaller features as compared with those made in air. It has been demonstrated that laser induced periodic structures are present to a lesser extent when laser processed in water solutions. The redistribution of oxygen revealed a strong surface modification, which is related to the etching selectivity of laser irradiated regions. Laser ablation in KOH and HF solution showed very different morphology, which relates to the evolution of laser induced plasma on the formation of micro/nano-features in liquid. This work extends laser precision fabrication of hard materials. The mechanism of strong absorption in the regions with permittivity (epsilon) near zero is discussed.

Pre-Finishing of SiC for Optical Applications

NASA Technical Reports Server (NTRS)

Rozzi, Jay; Clavier, Odile; Gagne, John

2011-01-01

13 Manufacturing & Prototyping A method is based on two unique processing steps that are both based on deterministic machining processes using a single-point diamond turning (SPDT) machine. In the first step, a high-MRR (material removal rate) process is used to machine the part within several microns of the final geometry. In the second step, a low-MRR process is used to machine the part to near optical quality using a novel ductile regime machining (DRM) process. DRM is a deterministic machining process associated with conditions under high hydrostatic pressures and very small depths of cut. Under such conditions, using high negative-rake angle cutting tools, the high-pressure region near the tool corresponds to a plastic zone, where even a brittle material will behave in a ductile manner. In the high-MRR processing step, the objective is to remove material with a sufficiently high rate such that the process is economical, without inducing large-scale subsurface damage. A laser-assisted machining approach was evaluated whereby a CO2 laser was focused in advance of the cutting tool. While CVD (chemical vapor deposition) SiC was successfully machined with this approach, the cutting forces were substantially higher than cuts at room temperature under the same machining conditions. During the experiments, the expansion of the part and the tool due to the heating was carefully accounted for. The higher cutting forces are most likely due to a small reduction in the shear strength of the material compared with a larger increase in friction forces due to the thermal softening effect. The key advantage is that the hybrid machine approach has the potential to achieve optical quality without the need for a separate optical finishing step. Also, this method is scalable, so one can easily progress from machining 50-mm-diameter samples to the 250-mm-diameter mirror that NASA desires.

Novel jet observables from machine learning

NASA Astrophysics Data System (ADS)

Datta, Kaustuv; Larkoski, Andrew J.

2018-03-01

Previous studies have demonstrated the utility and applicability of machine learning techniques to jet physics. In this paper, we construct new observables for the discrimination of jets from different originating particles exclusively from information identified by the machine. The approach we propose is to first organize information in the jet by resolved phase space and determine the effective N -body phase space at which discrimination power saturates. This then allows for the construction of a discrimination observable from the N -body phase space coordinates. A general form of this observable can be expressed with numerous parameters that are chosen so that the observable maximizes the signal vs. background likelihood. Here, we illustrate this technique applied to discrimination of H\\to b\\overline{b} decays from massive g\\to b\\overline{b} splittings. We show that for a simple parametrization, we can construct an observable that has discrimination power comparable to, or better than, widely-used observables motivated from theory considerations. For the case of jets on which modified mass-drop tagger grooming is applied, the observable that the machine learns is essentially the angle of the dominant gluon emission off of the b\\overline{b} pair.

Automatic microseismic event picking via unsupervised machine learning

NASA Astrophysics Data System (ADS)

Chen, Yangkang

2018-01-01

Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques

PubMed Central

2018-01-01

Background Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previous studies could predict whether an individual had returned to his/her original work by four years after termination of the worker's recovery period. Methods An initial logistic regression analysis of 1,567 participants of the fourth Panel Study of Worker's Compensation Insurance yielded odds ratios. The participants were divided into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were established, and important variables of each model were identified. The predictive abilities of the different models were compared. Results The analysis showed that only earned income and company-related factors significantly affected return-to-original-work (RTOW). The random forest model showed the best accuracy among the tested machine learning models; however, the difference was not prominent. Conclusion It is possible to predict a worker's probability of RTOW using machine learning techniques with moderate accuracy. PMID:29736160

Big Data Bioinformatics

PubMed Central

GREENE, CASEY S.; TAN, JIE; UNG, MATTHEW; MOORE, JASON H.; CHENG, CHAO

2017-01-01

Recent technological advances allow for high throughput profiling of biological systems in a cost-efficient manner. The low cost of data generation is leading us to the “big data” era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this review, we introduce key concepts in the analysis of big data, including both “machine learning” algorithms as well as “unsupervised” and “supervised” examples of each. We note packages for the R programming language that are available to perform machine learning analyses. In addition to programming based solutions, we review webservers that allow users with limited or no programming background to perform these analyses on large data compendia. PMID:27908398

Big Data Bioinformatics

PubMed Central

GREENE, CASEY S.; TAN, JIE; UNG, MATTHEW; MOORE, JASON H.; CHENG, CHAO

2017-01-01

Recent technological advances allow for high throughput profiling of biological systems in a cost-efficient manner. The low cost of data generation is leading us to the “big data” era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this review, we introduce key concepts in the analysis of big data, including both “machine learning” algorithms as well as “unsupervised” and “supervised” examples of each. We note packages for the R programming language that are available to perform machine learning analyses. In addition to programming based solutions, we review webservers that allow users with limited or no programming background to perform these analyses on large data compendia. PMID:24799088

Big data bioinformatics.

PubMed

Greene, Casey S; Tan, Jie; Ung, Matthew; Moore, Jason H; Cheng, Chao

2014-12-01

Recent technological advances allow for high throughput profiling of biological systems in a cost-efficient manner. The low cost of data generation is leading us to the "big data" era. The availability of big data provides unprecedented opportunities but also raises new challenges for data mining and analysis. In this review, we introduce key concepts in the analysis of big data, including both "machine learning" algorithms as well as "unsupervised" and "supervised" examples of each. We note packages for the R programming language that are available to perform machine learning analyses. In addition to programming based solutions, we review webservers that allow users with limited or no programming background to perform these analyses on large data compendia. © 2014 Wiley Periodicals, Inc.

Tear fluid proteomics multimarkers for diabetic retinopathy screening

PubMed Central

2013-01-01

Background The aim of the project was to develop a novel method for diabetic retinopathy screening based on the examination of tear fluid biomarker changes. In order to evaluate the usability of protein biomarkers for pre-screening purposes several different approaches were used, including machine learning algorithms. Methods All persons involved in the study had diabetes. Diabetic retinopathy (DR) was diagnosed by capturing 7-field fundus images, evaluated by two independent ophthalmologists. 165 eyes were examined (from 119 patients), 55 were diagnosed healthy and 110 images showed signs of DR. Tear samples were taken from all eyes and state-of-the-art nano-HPLC coupled ESI-MS/MS mass spectrometry protein identification was performed on all samples. Applicability of protein biomarkers was evaluated by six different optimally parameterized machine learning algorithms: Support Vector Machine, Recursive Partitioning, Random Forest, Naive Bayes, Logistic Regression, K-Nearest Neighbor. Results Out of the six investigated machine learning algorithms the result of Recursive Partitioning proved to be the most accurate. The performance of the system realizing the above algorithm reached 74% sensitivity and 48% specificity. Conclusions Protein biomarkers selected and classified with machine learning algorithms alone are at present not recommended for screening purposes because of low specificity and sensitivity values. This tool can be potentially used to improve the results of image processing methods as a complementary tool in automatic or semiautomatic systems. PMID:23919537

Failure mode and effects analysis of the universal anaesthesia machine in two tertiary care hospitals in Sierra Leone

PubMed Central

Rosen, M. A.; Sampson, J. B.; Jackson, E. V.; Koka, R.; Chima, A. M.; Ogbuagu, O. U.; Marx, M. K.; Koroma, M.; Lee, B. H.

2014-01-01

Background Anaesthesia care in developed countries involves sophisticated technology and experienced providers. However, advanced machines may be inoperable or fail frequently when placed into the austere medical environment of a developing country. Failure mode and effects analysis (FMEA) is a method for engaging local staff in identifying real or potential breakdowns in processes or work systems and to develop strategies to mitigate risks. Methods Nurse anaesthetists from the two tertiary care hospitals in Freetown, Sierra Leone, participated in three sessions moderated by a human factors specialist and an anaesthesiologist. Sessions were audio recorded, and group discussion graphically mapped by the session facilitator for analysis and commentary. These sessions sought to identify potential barriers to implementing an anaesthesia machine designed for austere medical environments—the universal anaesthesia machine (UAM)—and also engaging local nurse anaesthetists in identifying potential solutions to these barriers. Results Participating Sierra Leonean clinicians identified five main categories of failure modes (resource availability, environmental issues, staff knowledge and attitudes, and workload and staffing issues) and four categories of mitigation strategies (resource management plans, engaging and educating stakeholders, peer support for new machine use, and collectively advocating for needed resources). Conclusions We identified factors that may limit the impact of a UAM and devised likely effective strategies for mitigating those risks. PMID:24833727

Progress on Thomson scattering in the Pegasus Toroidal Experiment

NASA Astrophysics Data System (ADS)

Schlossberg, D. J.; Bongard, M. W.; Fonck, R. J.; Schoenbeck, N. L.; Winz, G. R.

2013-11-01

A novel Thomson scattering system has been implemented on the Pegasus Toroidal Experiment where typical densities of 1019 m-3 and electron temperatures of 10 to 500 eV are expected. The system leverages technological advances in high-energy pulsed lasers, volume phase holographic (VPH) diffraction gratings, and gated image intensified (ICCD) cameras to provide a relatively low-maintenance, economical, robust diagnostic system. Scattering is induced by a frequency-doubled, Q-switched Nd:YAG laser (2 J at 532 nm, 7 ns FWHM pulse) directed to the plasma over a 7.7 m long beam path, and focused to < 3 mm throughout the collection region. Inter-shot beam alignment is adjustable with less than a 0.01 mm spatial resolution in the collection region. A custom lens system collects scattered photons at radii 15 cm to 85 cm from the machine's center, at ~ F/6 with 14 mm radial resolution. The initial configuration provides scattering measurements at 12 spatial locations and 12 simultaneous background measurements at adjacent locations. If plasma background subtraction proves to be insignificant, these background channels will be used as viewing channels. Each spectrometer supports 8 spatial channels and can provide 8 or more spectral bins each. The spectrometers use high-efficiency VPH transmission gratings (eff. > 80%) and fast-gated ICCDs (gate > 2 ns, Gen III intensifier) with high-throughput (F/1.8), achromatic lensing. A stray light mitigation facility has been implemented, consisting of a multi-aperture optical baffle system and a simple beam dump. Successful stray light reduction has enabled detection of scattered signal, and Rayleigh scattering has been used to provide a relative calibration. Initial temperature measurements have been made and data analysis algorithms are under development.

Prevalence of human cell material: DNA and RNA profiling of public and private objects and after activity scenarios.

PubMed

van den Berge, M; Ozcanhan, G; Zijlstra, S; Lindenbergh, A; Sijen, T

2016-03-01

Especially when minute evidentiary traces are analysed, background cell material unrelated to the crime may contribute to detectable levels in the genetic analyses. To gain understanding on the composition of human cell material residing on surfaces contributing to background traces, we performed DNA and mRNA profiling on samplings of various items. Samples were selected by considering events contributing to cell material deposits in exemplary activities (e.g. dragging a person by the trouser ankles), and can be grouped as public objects, private samples, transfer-related samples and washing machine experiments. Results show that high DNA yields do not necessarily relate to an increased number of contributors or to the detection of other cell types than skin. Background cellular material may be found on any type of public or private item. When a major contributor can be deduced in DNA profiles from private items, this can be a different person than the owner of the item. Also when a specific activity is performed and the areas of physical contact are analysed, the "perpetrator" does not necessarily represent the major contributor in the STR profile. Washing machine experiments show that transfer and persistence during laundry is limited for DNA and cell type dependent for RNA. Skin conditions such as the presence of sebum or sweat can promote DNA transfer. Results of this study, which encompasses 549 samples, increase our understanding regarding the prevalence of human cell material in background and activity scenarios. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Positional reference system for ultraprecision machining

DOEpatents

Arnold, Jones B.; Burleson, Robert R.; Pardue, Robert M.

1982-01-01

A stable positional reference system for use in improving the cutting tool-to-part contour position in numerical controlled-multiaxis metal turning machines is provided. The reference system employs a plurality of interferometers referenced to orthogonally disposed metering bars which are substantially isolated from machine strain induced position errors for monitoring the part and tool positions relative to the metering bars. A microprocessor-based control system is employed in conjunction with the plurality of position interferometers and part contour description data inputs to calculate error components for each axis of movement and output them to corresponding axis drives with appropriate scaling and error compensation. Real-time position control, operating in combination with the reference system, makes possible the positioning of the cutting points of a tool along a part locus with a substantially greater degree of accuracy than has been attained previously in the art by referencing and then monitoring only the tool motion relative to a reference position located on the machine base.

Positional reference system for ultraprecision machining

DOEpatents

Arnold, J.B.; Burleson, R.R.; Pardue, R.M.

1980-09-12

A stable positional reference system for use in improving the cutting tool-to-part contour position in numerical controlled-multiaxis metal turning machines is provided. The reference system employs a plurality of interferometers referenced to orthogonally disposed metering bars which are substantially isolated from machine strain induced position errors for monitoring the part and tool positions relative to the metering bars. A microprocessor-based control system is employed in conjunction with the plurality of positions interferometers and part contour description data input to calculate error components for each axis of movement and output them to corresponding axis driven with appropriate scaling and error compensation. Real-time position control, operating in combination with the reference system, makes possible the positioning of the cutting points of a tool along a part locus with a substantially greater degree of accuracy than has been attained previously in the art by referencing and then monitoring only the tool motion relative to a reference position located on the machine base.

Integrative relational machine-learning for understanding drug side-effect profiles.

PubMed

Bresso, Emmanuel; Grisoni, Renaud; Marchetti, Gino; Karaboga, Arnaud Sinan; Souchet, Michel; Devignes, Marie-Dominique; Smaïl-Tabbone, Malika

2013-06-26

Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence. In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site. Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.

Mathematical modelling and numerical simulation of forces in milling process

NASA Astrophysics Data System (ADS)

Turai, Bhanu Murthy; Satish, Cherukuvada; Prakash Marimuthu, K.

2018-04-01

Machining of the material by milling induces forces, which act on the work piece material, tool and which in turn act on the machining tool. The forces involved in milling process can be quantified, mathematical models help to predict these forces. A lot of research has been carried out in this area in the past few decades. The current research aims at developing a mathematical model to predict forces at different levels which arise machining of Aluminium6061 alloy. Finite element analysis was used to develop a FE model to predict the cutting forces. Simulation was done for varying cutting conditions. Different experiments was designed using Taguchi method. A L9 orthogonal array was designed and the output was measure for the different experiments. The same was used to develop the mathematical model.

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

The Smart Aerial Release Machine, a Universal System for Applying the Sterile Insect Technique

PubMed Central

Mubarqui, Ruben Leal; Perez, Rene Cano; Kladt, Roberto Angulo; Lopez, Jose Luis Zavala; Parker, Andrew; Seck, Momar Talla; Sall, Baba; Bouyer, Jérémy

2014-01-01

Background Beyond insecticides, alternative methods to control insect pests for agriculture and vectors of diseases are needed. Management strategies involving the mass-release of living control agents have been developed, including genetic control with sterile insects and biological control with parasitoids, for which aerial release of insects is often required. Aerial release in genetic control programmes often involves the use of chilled sterile insects, which can improve dispersal, survival and competitiveness of sterile males. Currently available means of aerially releasing chilled fruit flies are however insufficiently precise to ensure homogeneous distribution at low release rates and no device is available for tsetse. Methodology/Principal Findings Here we present the smart aerial release machine, a new design by the Mubarqui Company, based on the use of vibrating conveyors. The machine is controlled through Bluetooth by a tablet with Android Operating System including a completely automatic guidance and navigation system (MaxNav software). The tablet is also connected to an online relational database facilitating the preparation of flight schedules and automatic storage of flight reports. The new machine was compared with a conveyor release machine in Mexico using two fruit flies species (Anastrepha ludens and Ceratitis capitata) and we obtained better dispersal homogeneity (% of positive traps, p<0.001) for both species and better recapture rates for Anastrepha ludens (p<0.001), especially at low release densities (<1500 per ha). We also demonstrated that the machine can replace paper boxes for aerial release of tsetse in Senegal. Conclusions/Significance This technology limits damages to insects and allows a large range of release rates from 10 flies/km2 for tsetse flies up to 600 000 flies/km2 for fruit flies. The potential of this machine to release other species like mosquitoes is discussed. Plans and operating of the machine are provided to allow its use worldwide. PMID:25036274

The electrical discharge machining of ceramics

NASA Astrophysics Data System (ADS)

Trueman, Christopher Stuart

This study introduces the concept of developing a novel and rapid rough-machining methodology for spark eroding suitable ceramic compositions based on material removal by thermal shock induced spalling, as opposed to conventional melting mechanisms. The principal materials studied were TiB2 dispersion toughened SiC, and Syalon501 - a commercially available TiN toughened sialon ceramic specifically designed for spark erosion. A preliminary study was also carried out on a range of SiC:B4C composites. Machinability and material performance were assessed where appropriate using machining parameters, material removal rate tests, surface analysis, four-point flexure testing, and tool wear. The machining technologies which supported the different mechanisms of material removal were identified, and each mechanism investigated by analysis of captured debris and sectioning of the workpiece. The SiC:B4C composites were found to be spark erodible only with B4C levels above 50% (by mass), and material removal was found to be solely by melting mechanisms. A SiC:TiB2 composition with the addition of 26.5% TiB2 (by mass) was found to be more machinable than a composition with 10% TiB2 (by mass), achieving greater material removal rates owing to its higher electrical conductivity. An in-depth study of the latter (10%TiB2) SiC composition and Syalon501 revealed surprisingly robust materials. Under conventional sparking (no arcing), material was removed by combined dissociation, melting and evaporation. Syalon501 in particular behaved with a high degree of predictability, and neither material could be made to spall under conventional sparking. However, by imposing conditions which deliberately induced arcing, both compositions spalled large flakes of material (up to several hundred microns across) in the localised region of the arc-strike. Examination of captured debris and fracture facets of the spall interface revealed the existence of small "penny cracks", each characterised by the presence of a dispersed particle (of greater thermal expansion) at its centre acting as a stress- raising nucleation point under the intense thermal loading of arcing. Sub-surface cracks in the near horizontal and near-vertical planes were discovered in line with published models based on the application of a hot-spot to brittle material, and evidence of discrete crack propagation under the thermally punctuated pulses of successive sparking was identified. Similar sub-surface cracking was also confirmed in Syalon501 which had been subjected to arcing. Sectioning of the workpiece revealed shallow sub-surface cracks which followed the profile of the machined surface in the near-horizontal plane, and which often limited the extent of near-vertical cracking to the layer of material above the crack, thereby offering the potential for a reliable and fast "planning" technique in which material would be removed in shallow layers. This research has shown that the possibility exists for increased material removal rates and improved process efficiency under a spalling-based machining regime, in which layers of material are released by thermal-shock induced fracture caused by arcing. The viability of developing a new rough-machining technology for ceramics, in which material is "planed" away prior to fine surface finishing by conventional spark erosion has, therefore, been successfully demonstrated.

Ion-Induced Afterpulsing in the Neutron Multiplicity Meter's Photomultiplier Tubes

NASA Astrophysics Data System (ADS)

Nedlik, Christopher; Schnee, Richard; Bunker, Raymond; Chen, Yu; Neutron Multiplicity Meter Collaboration

2013-10-01

The nature of the dark matter in the Universe remains a mystery in modern physics. A leading candidate, Weakly Interacting Massive Particles (WIMPs), may be detectable via scattering from nuclear targets in terrestrial detectors, located underground to prevent fake signals from cosmic-ray showers. The Neutron Multiplicity Meter (NMM) is a detector capable of measuring the muon-induced neutron flux deep underground, a problematic background for WIMP detection. The NMM is a 4.4-tonne Gd-loaded water-Cherenkov detector atop a 20-kilotonne lead target in the Soudan Mine. It measures high-energy neutrons (>50 MeV) by moderating and then detecting (via Gd capture gammas) the secondary neutrons emerging from the lead following a high-energy neutron interaction. The short time scale (~10 μs) for neutron capture in Gd-loaded water enables a custom multiplicity trigger to discriminate against the dominant gamma-ray background. Despite excellent rejection of the gamma-ray-induced background, NMM neutron-candidate events are not entirely background-free. One type of background is from ion-induced afterpulsing (AP) in the four 20'' Hamamatsu R7250 photomultiplier tubes (PMTs) used to monitor the NMM's two water tanks. We show that ion-induced AP in the PMTs can mimic the NMM's low-energy neutron response, potentially biasing a candidate event's measured multiplicity. We present detailed studies of the AP in order to allow identification of AP-induced background events.

Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies

PubMed Central

Zheng, Shuai; Ghasemzadeh, Nima; Hayek, Salim S; Quyyumi, Arshed A

2017-01-01

Background Extracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedbacks for improving the extraction algorithm in real time. Objective Our goal was to provide a generic information extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results. Methods A clinical information extraction system IDEAL-X has been built on top of online machine learning. It processes one document at a time, and user interactions are recorded as feedbacks to update the learning model in real time. The updated model is used to predict values for extraction in subsequent documents. Once prediction accuracy reaches a user-acceptable threshold, the remaining documents may be batch processed. A customizable controlled vocabulary may be used to support extraction. Results Three datasets were used for experiments based on report styles: 100 cardiac catheterization procedure reports, 100 coronary angiographic reports, and 100 integrated reports—each combines history and physical report, discharge summary, outpatient clinic notes, outpatient clinic letter, and inpatient discharge medication report. Data extraction was performed by 3 methods: online machine learning, controlled vocabularies, and a combination of these. The system delivers results with F1 scores greater than 95%. Conclusions IDEAL-X adopts a unique online machine learning–based approach combined with controlled vocabularies to support data extraction for clinical reports. The system can quickly learn and improve, thus it is highly adaptable. PMID:28487265

The influence of negative training set size on machine learning-based virtual screening

PubMed Central

2014-01-01

Background The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. Results The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. Conclusions In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening. PMID:24976867

Laser Balancing

NASA Technical Reports Server (NTRS)

1981-01-01

Mechanical Technology, Incorporated developed a fully automatic laser machining process that allows more precise balancing removes metal faster, eliminates excess metal removal and other operator induced inaccuracies, and provides significant reduction in balancing time. Manufacturing costs are reduced as a result.

Integrated human-machine intelligence in space systems.

PubMed

Boy, G A

1992-07-01

This paper presents an artificial intelligence approach to integrated human-machine intelligence in space systems. It discusses the motivations for Intelligent Assistant Systems in both nominal and abnormal situations. The problem of constructing procedures is shown to be a very critical issue. In particular, keeping procedural experience in both design and operation is critical. We suggest what artificial intelligence can offer in this direction. Some crucial problems induced by this approach are discussed in detail. Finally, we analyze the various roles that would be shared by both astronauts, ground operators, and the intelligent assistant system.

Numerical analysis method for linear induction machines.

NASA Technical Reports Server (NTRS)

Elliott, D. G.

1972-01-01

A numerical analysis method has been developed for linear induction machines such as liquid metal MHD pumps and generators and linear motors. Arbitrary phase currents or voltages can be specified and the moving conductor can have arbitrary velocity and conductivity variations from point to point. The moving conductor is divided into a mesh and coefficients are calculated for the voltage induced at each mesh point by unit current at every other mesh point. Combining the coefficients with the mesh resistances yields a set of simultaneous equations which are solved for the unknown currents.

Automatic optical detection and classification of marine animals around MHK converters using machine vision

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

Brunton, Steven

Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robustmore » principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.« less

A systematic analysis of the XMM-Newton background: III. Impact of the magnetospheric environment

NASA Astrophysics Data System (ADS)

Ghizzardi, Simona; Marelli, Martino; Salvetti, David; Gastaldello, Fabio; Molendi, Silvano; De Luca, Andrea; Moretti, Alberto; Rossetti, Mariachiara; Tiengo, Andrea

2017-12-01

A detailed characterization of the particle induced background is fundamental for many of the scientific objectives of the Athena X-ray telescope, thus an adequate knowledge of the background that will be encountered by Athena is desirable. Current X-ray telescopes have shown that the intensity of the particle induced background can be highly variable. Different regions of the magnetosphere can have very different environmental conditions, which can, in principle, differently affect the particle induced background detected by the instruments. We present results concerning the influence of the magnetospheric environment on the background detected by EPIC instrument onboard XMM-Newton through the estimate of the variation of the in-Field-of-View background excess along the XMM-Newton orbit. An important contribution to the XMM background, which may affect the Athena background as well, comes from soft proton flares. Along with the flaring component a low-intensity component is also present. We find that both show modest variations in the different magnetozones and that the soft proton component shows a strong trend with the distance from Earth.

Discriminating Induced-Microearthquakes Using New Seismic Features

NASA Astrophysics Data System (ADS)

Mousavi, S. M.; Horton, S.

2016-12-01

We studied characteristics of induced-microearthquakes on the basis of the waveforms recorded on a limited number of surface receivers using machine-learning techniques. Forty features in the time, frequency, and time-frequency domains were measured on each waveform, and several techniques such as correlation-based feature selection, Artificial Neural Networks (ANNs), Logistic Regression (LR) and X-mean were used as research tools to explore the relationship between these seismic features and source parameters. The results show that spectral features have the highest correlation to source depth. Two new measurements developed as seismic features for this study, spectral centroids and 2D cross-correlations in the time-frequency domain, performed better than the common seismic measurements. These features can be used by machine learning techniques for efficient automatic classification of low energy signals recorded at one or more seismic stations. We applied the technique to 440 microearthquakes-1.7Reference: Mousavi, S.M., S.P. Horton, C. A. Langston, B. Samei, (2016) Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression, Geophys. J. Int. doi: 10.1093/gji/ggw258.

Machine Translation of Public Health Materials From English to Chinese: A Feasibility Study

PubMed Central

Desai, Loma

2015-01-01

Background Chinese is the second most common language spoken by limited English proficiency individuals in the United States, yet there are few public health materials available in Chinese. Previous studies have indicated that use of machine translation plus postediting by bilingual translators generated quality translations in a lower time and at a lower cost than human translations. Objective The purpose of this study was to investigate the feasibility of using machine translation (MT) tools (eg, Google Translate) followed by human postediting (PE) to produce quality Chinese translations of public health materials. Methods From state and national public health websites, we collected 60 health promotion documents that had been translated from English to Chinese through human translation. The English version of the documents were then translated to Chinese using Google Translate. The MTs were analyzed for translation errors. A subset of the MT documents was postedited by native Chinese speakers with health backgrounds. Postediting time was measured. Postedited versions were then blindly compared against human translations by bilingual native Chinese quality raters. Results The most common machine translation errors were errors of word sense (40%) and word order (22%). Posteditors corrected the MTs at a rate of approximately 41 characters per minute. Raters, blinded to the source of translation, consistently selected the human translation over the MT+PE. Initial investigation to determine the reasons for the lower quality of MT+PE indicate that poor MT quality, lack of posteditor expertise, and insufficient posteditor instructions can be barriers to producing quality Chinese translations. Conclusions Our results revealed problems with using MT tools plus human postediting for translating public health materials from English to Chinese. Additional work is needed to improve MT and to carefully design postediting processes before the MT+PE approach can be used routinely in public health practice for a variety of language pairs. PMID:27227135

Fault-Tolerant Coding for State Machines

NASA Technical Reports Server (NTRS)

Naegle, Stephanie Taft; Burke, Gary; Newell, Michael

2008-01-01

Two reliable fault-tolerant coding schemes have been proposed for state machines that are used in field-programmable gate arrays and application-specific integrated circuits to implement sequential logic functions. The schemes apply to strings of bits in state registers, which are typically implemented in practice as assemblies of flip-flop circuits. If a single-event upset (SEU, a radiation-induced change in the bit in one flip-flop) occurs in a state register, the state machine that contains the register could go into an erroneous state or could hang, by which is meant that the machine could remain in undefined states indefinitely. The proposed fault-tolerant coding schemes are intended to prevent the state machine from going into an erroneous or hang state when an SEU occurs. To ensure reliability of the state machine, the coding scheme for bits in the state register must satisfy the following criteria: 1. All possible states are defined. 2. An SEU brings the state machine to a known state. 3. There is no possibility of a hang state. 4. No false state is entered. 5. An SEU exerts no effect on the state machine. Fault-tolerant coding schemes that have been commonly used include binary encoding and "one-hot" encoding. Binary encoding is the simplest state machine encoding and satisfies criteria 1 through 3 if all possible states are defined. Binary encoding is a binary count of the state machine number in sequence; the table represents an eight-state example. In one-hot encoding, N bits are used to represent N states: All except one of the bits in a string are 0, and the position of the 1 in the string represents the state. With proper circuit design, one-hot encoding can satisfy criteria 1 through 4. Unfortunately, the requirement to use N bits to represent N states makes one-hot coding inefficient.

Using Pipelined XNOR Logic to Reduce SEU Risks in State Machines

NASA Technical Reports Server (NTRS)

Le, Martin; Zheng, Xin; Katanyoutant, Sunant

2008-01-01

Single-event upsets (SEUs) pose great threats to avionic systems state machine control logic, which are frequently used to control sequence of events and to qualify protocols. The risks of SEUs manifest in two ways: (a) the state machine s state information is changed, causing the state machine to unexpectedly transition to another state; (b) due to the asynchronous nature of SEU, the state machine's state registers become metastable, consequently causing any combinational logic associated with the metastable registers to malfunction temporarily. Effect (a) can be mitigated with methods such as triplemodular redundancy (TMR). However, effect (b) cannot be eliminated and can degrade the effectiveness of any mitigation method of effect (a). Although there is no way to completely eliminate the risk of SEU-induced errors, the risk can be made very small by use of a combination of very fast state-machine logic and error-detection logic. Therefore, one goal of two main elements of the present method is to design the fastest state-machine logic circuitry by basing it on the fastest generic state-machine design, which is that of a one-hot state machine. The other of the two main design elements is to design fast error-detection logic circuitry and to optimize it for implementation in a field-programmable gate array (FPGA) architecture: In the resulting design, the one-hot state machine is fitted with a multiple-input XNOR gate for detection of illegal states. The XNOR gate is implemented with lookup tables and with pipelines for high speed. In this method, the task of designing all the logic must be performed manually because no currently available logic synthesis software tool can produce optimal solutions of design problems of this type. However, some assistance is provided by a script, written for this purpose in the Python language (an object-oriented interpretive computer language) to automatically generate hardware description language (HDL) code from state-transition rules.

Schoolchildren's Consumption of Competitive Foods and Beverages, Excluding a la Carte

ERIC Educational Resources Information Center

Kakarala, Madhuri; Keast, Debra R.; Hoerr, Sharon

2010-01-01

Background: Competitive foods/beverages are those in school vending machines, school stores, snack bars, special sales, and items sold a la carte in the school cafeteria that compete with United States Department of Agriculture (USDA) meal program offerings. Grouping a la carte items with less nutritious items allowed in less regulated venues may…

"Your Model Is Predictive-- but Is It Useful?" Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation

ERIC Educational Resources Information Center

González-Brenes, José P.; Huang, Yun

2015-01-01

Classification evaluation metrics are often used to evaluate adaptive tutoring systems-- programs that teach and adapt to humans. Unfortunately, it is not clear how intuitive these metrics are for practitioners with little machine learning background. Moreover, our experiments suggest that existing convention for evaluating tutoring systems may…

CoCom and the Future of Conventional Arms Exports in the Former Communist Bloc

DTIC Science & Technology

1993-12-01

structure and background of each of these firms. The first example is the Godollo Machine Factory which was primarily involved in repair and renovation of...regularizedŚ Such an attitude is noteworthy because the potential for growth in the space industry is so great. Not only does Russia produce the " Energia

Potato Chips, Cookies, and Candy Oh My! Public Commentary on Proposed Rules Regulating Competitive Foods

ERIC Educational Resources Information Center

Dinour, Lauren M.; Pole, Antoinette

2017-01-01

Background: The Healthy, Hunger-Free Kids Act of 2010 authorizes the U.S. Department of Agriculture (USDA) to establish nutritional standards for all foods sold in schools participating in federally funded meal programs. These foods, known as competitive foods, are commonly found in school cafeterias, vending machines, fundraisers, and snack bars…

Development and Validation of a Learning Analytics Framework: Two Case Studies Using Support Vector Machines

ERIC Educational Resources Information Center

Ifenthaler, Dirk; Widanapathirana, Chathuranga

2014-01-01

Interest in collecting and mining large sets of educational data on student background and performance to conduct research on learning and instruction has developed as an area generally referred to as learning analytics. Higher education leaders are recognizing the value of learning analytics for improving not only learning and teaching but also…

Graphical Man/Machine Communications

DTIC Science & Technology

1972-12-01

several others. The principal accomplishments are the development of a homomorphic imane deblurring method by E. Randolph Cole, the research into human...visual modelling by Patrick Baudelair, the impressive demonstrations of background noise elimination by Neil J. Miller, and the innovation of...characteristics for the homomorphical ly estimated restoration filter in the presence of additive noise were derived, and they show that the

Impact of an Engineering Design-Based Curriculum Compared to an Inquiry-Based Curriculum on Fifth Graders' Content Learning of Simple Machines

ERIC Educational Resources Information Center

Marulcu, Ismail; Barnett, Michael

2016-01-01

Background: Elementary Science Education is struggling with multiple challenges. National and State test results confirm the need for deeper understanding in elementary science education. Moreover, national policy statements and researchers call for increased exposure to engineering and technology in elementary science education. The basic…

Profits, Commercial Food Supplier Involvement, and School Vending Machine Snack Food Availability: Implications for Implementing the New Competitive Foods Rule

ERIC Educational Resources Information Center

Terry-McElrath, Yvonne M.; Hood, Nancy E.; Colabianchi, Natalie; O'Malley, Patrick M.; Johnston, Lloyd D.

2014-01-01

Background: The 2013-2014 school year involved preparation for implementing the new US Department of Agriculture (USDA) competitive foods nutrition standards. An awareness of associations between commercial supplier involvement, food vending practices, and food vending item availability may assist schools in preparing for the new standards.…

Enhancement of runaway production by resonant magnetic perturbation on J-TEXT

NASA Astrophysics Data System (ADS)

Chen, Z. Y.; Huang, D. W.; Izzo, V. A.; Tong, R. H.; Jiang, Z. H.; Hu, Q. M.; Wei, Y. N.; Yan, W.; Rao, B.; Wang, S. Y.; Ma, T. K.; Li, S. C.; Yang, Z. J.; Ding, D. H.; Wang, Z. J.; Zhang, M.; Zhuang, G.; Pan, Y.; J-TEXT Team

2016-07-01

The suppression of runaways following disruptions is key for the safe operation of ITER. The massive gas injection (MGI) has been developed to mitigate heat loads, electromagnetic forces and runaway electrons (REs) during disruptions. However, MGI may not completely prevent the generation of REs during disruptions on ITER. Resonant magnetic perturbation (RMP) has been applied to suppress runaway generation during disruptions on several machines. It was found that strong RMP results in the enhancement of runaway production instead of runaway suppression on J-TEXT. The runaway current was about 50% pre-disruption plasma current in argon induced reference disruptions. With moderate RMP, the runway current decreased to below 30% pre-disruption plasma current. The runaway current plateaus reach 80% of the pre-disruptive current when strong RMP was applied. Strong RMP may induce large size magnetic islands that could confine more runaway seed during disruptions. This has important implications for runaway suppression on large machines.

CD-REST: a system for extracting chemical-induced disease relation in literature.

PubMed

Xu, Jun; Wu, Yonghui; Zhang, Yaoyun; Wang, Jingqi; Lee, Hee-Jin; Xu, Hua

2016-01-01

Mining chemical-induced disease relations embedded in the vast biomedical literature could facilitate a wide range of computational biomedical applications, such as pharmacovigilance. The BioCreative V organized a Chemical Disease Relation (CDR) Track regarding chemical-induced disease relation extraction from biomedical literature in 2015. We participated in all subtasks of this challenge. In this article, we present our participation system Chemical Disease Relation Extraction SysTem (CD-REST), an end-to-end system for extracting chemical-induced disease relations in biomedical literature. CD-REST consists of two main components: (1) a chemical and disease named entity recognition and normalization module, which employs the Conditional Random Fields algorithm for entity recognition and a Vector Space Model-based approach for normalization; and (2) a relation extraction module that classifies both sentence-level and document-level candidate drug-disease pairs by support vector machines. Our system achieved the best performance on the chemical-induced disease relation extraction subtask in the BioCreative V CDR Track, demonstrating the effectiveness of our proposed machine learning-based approaches for automatic extraction of chemical-induced disease relations in biomedical literature. The CD-REST system provides web services using HTTP POST request. The web services can be accessed fromhttp://clinicalnlptool.com/cdr The online CD-REST demonstration system is available athttp://clinicalnlptool.com/cdr/cdr.html. Database URL:http://clinicalnlptool.com/cdr;http://clinicalnlptool.com/cdr/cdr.html. © The Author(s) 2016. Published by Oxford University Press.

Defining and Testing the Influence of Servo System Response on Machine Tool Compliance

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

Hopkins, D J

2004-03-24

Compliance can be defined as the measurement of displacement per unit of force applied e.g. nano-meters per Newton (m/N). Compliance is the reciprocal of stiffness. High stiffness means low compliance and visa versa. It is an important factor in machine tool characteristics because it reflects the ability of the machine axis to maintain a desired position as it encounters a force or torque. Static compliance is a measurement made with a constant force applied e.g. the average depth of cut. Dynamic compliance is a measurement made as a function of frequency, e.g. a fast too servo (FTS) that applies amore » varying cutting force or load, interrupted cuts and external disturbances such as ground vibrations or air conditioning induced forces on the machine. Compliance can be defined for both a linear and rotary axis of a machine tool. However, to properly define compliance for a rotary axis, the axis must allow a commanded angular position. Note that this excludes velocity only axes. In this paper, several factors are discussed that affect compliance but emphasis is placed on how the machine servo system plays a key role in compliance at low to mid frequency regions. The paper discusses several techniques for measuring compliance and provides examples of results from these measurements.« less

Temperature Measurement and Numerical Prediction in Machining Inconel 718

PubMed Central

Tapetado, Alberto; Vázquez, Carmen; Miguélez, Henar

2017-01-01

Thermal issues are critical when machining Ni-based superalloy components designed for high temperature applications. The low thermal conductivity and extreme strain hardening of this family of materials results in elevated temperatures around the cutting area. This elevated temperature could lead to machining-induced damage such as phase changes and residual stresses, resulting in reduced service life of the component. Measurement of temperature during machining is crucial in order to control the cutting process, avoiding workpiece damage. On the other hand, the development of predictive tools based on numerical models helps in the definition of machining processes and the obtainment of difficult to measure parameters such as the penetration of the heated layer. However, the validation of numerical models strongly depends on the accurate measurement of physical parameters such as temperature, ensuring the calibration of the model. This paper focuses on the measurement and prediction of temperature during the machining of Ni-based superalloys. The temperature sensor was based on a fiber-optic two-color pyrometer developed for localized temperature measurements in turning of Inconel 718. The sensor is capable of measuring temperature in the range of 250 to 1200 °C. Temperature evolution is recorded in a lathe at different feed rates and cutting speeds. Measurements were used to calibrate a simplified numerical model for prediction of temperature fields during turning. PMID:28665312

An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms.

PubMed

Amaral, Jorge L M; Lopes, Agnaldo J; Jansen, José M; Faria, Alvaro C D; Melo, Pedro L

2013-12-01

The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

The modulating impact of illumination and background radiation on 8 Hz-induced infrasound effect on physicochemical properties of physiolagical solution.

PubMed

Baghdasaryan, Naira; Mikayelyan, Yerazik; Barseghyan, Sedrak; Dadasyan, Erna; Ayrapetyan, Sinerik

2012-12-01

At present, when the level of background ionizing radiation is increasing in a number of world locations, the problem of the study of biological effect of high background radiation becomes one of the extremely important global problems in modern life sciences. The modern research in biophysics proved that water is a most essential target, through which the biological effects of ionizing and non-ionizing radiations are realized. Therefore, there is no doubt about the strong dependency of non-ionizing radiation-induced effect on the level of background radiation. Findings have shown that illumination and background radiation have a strong modulation effect on infrasound-induced impacts on water physicochemical properties, which could also have appropriate effect on living organisms.

Core Muscle Activity, Exercise Preference, and Perceived Exertion during Core Exercise with Elastic Resistance versus Machine.

PubMed

Vinstrup, Jonas; Sundstrup, Emil; Brandt, Mikkel; Jakobsen, Markus D; Calatayud, Joaquin; Andersen, Lars L

2015-01-01

Objectives. To investigate core muscle activity, exercise preferences, and perceived exertion during two selected core exercises performed with elastic resistance versus a conventional training machine. Methods. 17 untrained men aged 26-67 years participated in surface electromyography (EMG) measurements of five core muscles during torso-twists performed from left to right with elastic resistance and in the machine, respectively. The order of the exercises was randomized and each exercise consisted of 3 repetitions performed at a 10 RM load. EMG amplitude was normalized (nEMG) to maximum voluntary isometric contraction (MVC). Results. A higher right erector spinae activity in the elastic exercise compared with the machine exercise (50% [95% CI 36-64] versus 32% [95% CI 18-46] nEMG) was found. By contrast, the machine exercise, compared with the elastic exercise, showed higher left external oblique activity (77% [95% CI 64-90] versus 54% [95% CI 40-67] nEMG). For the rectus abdominis, right external oblique, and left erector spinae muscles there were no significant differences. Furthermore, 76% preferred the torso-twist with elastic resistance over the machine exercise. Perceived exertion (Borg CR10) was not significantly different between machine (5.8 [95% CI 4.88-6.72]) and elastic exercise (5.7 [95% CI 4.81-6.59]). Conclusion. Torso-twists using elastic resistance showed higher activity of the erector spinae, whereas torso-twist in the machine resulted in higher activity of the external oblique. For the remaining core muscles the two training modalities induced similar muscular activation. In spite of similar perceived exertion the majority of the participants preferred the exercise using elastic resistance.

[Quantitative Analysis of Heavy Metals in Water with LIBS Based on Signal-to-Background Ratio].

PubMed

Hu, Li; Zhao, Nan-jing; Liu, Wen-qing; Fang, Li; Zhang, Da-hai; Wang, Yin; Meng, De Shuo; Yu, Yang; Ma, Ming-jun

2015-07-01

There are many influence factors in the precision and accuracy of the quantitative analysis with LIBS technology. According to approximately the same characteristics trend of background spectrum and characteristic spectrum along with the change of temperature through in-depth analysis, signal-to-background ratio (S/B) measurement and regression analysis could compensate the spectral line intensity changes caused by system parameters such as laser power, spectral efficiency of receiving. Because the measurement dates were limited and nonlinear, we used support vector machine (SVM) for regression algorithm. The experimental results showed that the method could improve the stability and the accuracy of quantitative analysis of LIBS, and the relative standard deviation and average relative error of test set respectively were 4.7% and 9.5%. Data fitting method based on signal-to-background ratio(S/B) is Less susceptible to matrix elements and background spectrum etc, and provides data processing reference for real-time online LIBS quantitative analysis technology.

An application of machine learning to the organization of institutional software repositories

NASA Technical Reports Server (NTRS)

Bailin, Sidney; Henderson, Scott; Truszkowski, Walt

1993-01-01

Software reuse has become a major goal in the development of space systems, as a recent NASA-wide workshop on the subject made clear. The Data Systems Technology Division of Goddard Space Flight Center has been working on tools and techniques for promoting reuse, in particular in the development of satellite ground support software. One of these tools is the Experiment in Libraries via Incremental Schemata and Cobweb (ElvisC). ElvisC applies machine learning to the problem of organizing a reusable software component library for efficient and reliable retrieval. In this paper we describe the background factors that have motivated this work, present the design of the system, and evaluate the results of its application.

A new AMS facility based on a Cockcroft-Walton type 1 MV tandetron at IFIN-HH Magurele, Romania

NASA Astrophysics Data System (ADS)

Stan-Sion, C.; Enachescu, M.; Ghita, D. G.; Calinescu, C. I.; Petre, A.; Mosu, D. V.; Klein, M.

2014-01-01

A 1 MV AMS machine was recently installed in the National Institute for Physics and Nuclear Engineering IFIN-HH, Bucharest Romania. It is the second AMS facility at IFIN-HH having the goal not only to continue but mainly to enlarge the research area of this highly sensitive analyzing method. The multi-element AMS was developed by HVEE to measure 14C, 10Be, and 26Al, and 129I. The results of an acceptance test are presented and demonstrate that this machine is capable of routine 14C age dating and of measurements of other radioisotopes in terms of accuracy and precision as well as a low background level.

Comparative implementation of Handwritten and Machine written Gurmukhi text utilizing appropriate parameters

NASA Astrophysics Data System (ADS)

Kaur, Jaswinder; Jagdev, Gagandeep, Dr.

2018-01-01

Optical character recognition is concerned with the recognition of optically processed characters. The recognition is done offline after the writing or printing has been completed, unlike online recognition where the computer has to recognize the characters instantly as they are drawn. The performance of character recognition depends upon the quality of scanned documents. The preprocessing steps are used for removing low-frequency background noise and normalizing the intensity of individual scanned documents. Several filters are used for reducing certain image details and enabling an easier or faster evaluation. The primary aim of the research work is to recognize handwritten and machine written characters and differentiate them. The language opted for the research work is Punjabi Gurmukhi and tool utilized is Matlab.

Atwood's Heavy Chain

NASA Astrophysics Data System (ADS)

Beeken, Paul

2011-11-01

While perusing various websites in search of a more challenging lab for my students, I came across a number of ideas where replacing the string in an Atwood's machine with a simple ball chain like the kind found in lamp pulls created an interesting system to investigate. The replacement of the string produced a nice nonuniform acceleration, but one that my AP® students found difficult to analyze given their current math background. As the year progressed, we began to explore the importance of work and its utility in making predictions on systems that did not lend themselves to easy analysis using Newtonian mechanics. The effort made it apparent that the heavy rope Atwood's machine would make a perfect system for investigation using the lessons gained from work and energy.

Surface morphology and subsurface damaged layer of various glasses machined by 193-nm ArF excimer laser

NASA Astrophysics Data System (ADS)

Liao, Yunn-shiuan; Chen, Ying-Tung; Chao, Choung-Lii; Liu, Yih-Ming

2005-01-01

Owing to the high bonding energy, most of the glasses are removed by photo-thermal rather than photo-chemical effect when they are ablated by the 193 or 248nm excimer lasers. Typically, the machined surface is covered by re-deposited debris and the sub-surface, sometimes surface as well, is scattered with micro-cracks introduced by thermal stress generated during the process. This study aimed to investigate the nature and extent of the surface morphology and sub-surface damaged (SSD) layer induced by the laser ablation. The effects of laser parameters such as fluence, shot number and repetition rate on the morphology and SSD were discussed. An ArF excimer laser (193 nm) was used in the present study to machine glasses such as soda-lime, Zerodur and BK-7. It is found that the melt ejection and debris deposition tend to pile up higher and become denser in structure under a higher energy density, repetition rate and shot number. There are thermal stress induced lateral cracks when the debris covered top layer is etched away. Higher fluence and repetition rate tend to generate more lateral and median cracks which propagate into the substrate. The changes of mechanical properties of the SSD layer were also investigated.

A machine learning approach for automated assessment of retinal vasculature in the oxygen induced retinopathy model.

PubMed

Mazzaferri, Javier; Larrivée, Bruno; Cakir, Bertan; Sapieha, Przemyslaw; Costantino, Santiago

2018-03-02

Preclinical studies of vascular retinal diseases rely on the assessment of developmental dystrophies in the oxygen induced retinopathy rodent model. The quantification of vessel tufts and avascular regions is typically computed manually from flat mounted retinas imaged using fluorescent probes that highlight the vascular network. Such manual measurements are time-consuming and hampered by user variability and bias, thus a rapid and objective method is needed. Here, we introduce a machine learning approach to segment and characterize vascular tufts, delineate the whole vasculature network, and identify and analyze avascular regions. Our quantitative retinal vascular assessment (QuRVA) technique uses a simple machine learning method and morphological analysis to provide reliable computations of vascular density and pathological vascular tuft regions, devoid of user intervention within seconds. We demonstrate the high degree of error and variability of manual segmentations, and designed, coded, and implemented a set of algorithms to perform this task in a fully automated manner. We benchmark and validate the results of our analysis pipeline using the consensus of several manually curated segmentations using commonly used computer tools. The source code of our implementation is released under version 3 of the GNU General Public License ( https://www.mathworks.com/matlabcentral/fileexchange/65699-javimazzaf-qurva ).

Machine Learning and Data Mining Methods in Diabetes Research.

PubMed

Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

2017-01-01

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

PubMed Central

Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca

2017-01-01

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients. PMID:29904574

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study.

PubMed

Tacchella, Andrea; Romano, Silvia; Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca

2017-01-01

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

Identifying Active Travel Behaviors in Challenging Environments Using GPS, Accelerometers, and Machine Learning Algorithms

PubMed Central

Ellis, Katherine; Godbole, Suneeta; Marshall, Simon; Lanckriet, Gert; Staudenmayer, John; Kerr, Jacqueline

2014-01-01

Background: Active travel is an important area in physical activity research, but objective measurement of active travel is still difficult. Automated methods to measure travel behaviors will improve research in this area. In this paper, we present a supervised machine learning method for transportation mode prediction from global positioning system (GPS) and accelerometer data. Methods: We collected a dataset of about 150 h of GPS and accelerometer data from two research assistants following a protocol of prescribed trips consisting of five activities: bicycling, riding in a vehicle, walking, sitting, and standing. We extracted 49 features from 1-min windows of this data. We compared the performance of several machine learning algorithms and chose a random forest algorithm to classify the transportation mode. We used a moving average output filter to smooth the output predictions over time. Results: The random forest algorithm achieved 89.8% cross-validated accuracy on this dataset. Adding the moving average filter to smooth output predictions increased the cross-validated accuracy to 91.9%. Conclusion: Machine learning methods are a viable approach for automating measurement of active travel, particularly for measuring travel activities that traditional accelerometer data processing methods misclassify, such as bicycling and vehicle travel. PMID:24795875

Aspects of image recognition in Vivid Technologies' dual-energy x-ray system for explosives detection

NASA Astrophysics Data System (ADS)

Eilbert, Richard F.; Krug, Kristoph D.

1993-04-01

The Vivid Rapid Explosives Detection Systems is a true dual energy x-ray machine employing precision x-ray data acquisition in combination with unique algorithms and massive computation capability. Data from the system's 960 detectors is digitally stored and processed by powerful supermicro-computers organized as an expandable array of parallel processors. The algorithms operate on the dual energy attenuation image data to recognize and define objects in the milieu of the baggage contents. Each object is then systematically examined for a match to a specific effective atomic number, density, and mass threshold. Material properties are determined by comparing the relative attenuations of the 75 kVp and 150 kVp beams and electronically separating the object from its local background. Other heuristic algorithms search for specific configurations and provide additional information. The machine automatically detects explosive materials and identifies bomb components in luggage with high specificity and throughput, X-ray dose is comparable to that of current airport x-ray machines. The machine is also configured to find heroin, cocaine, and US currency by selecting appropriate settings on-site. Since January 1992, production units have been operationally deployed at U.S. and European airports for improved screening of checked baggage.

Classifying smoking urges via machine learning

PubMed Central

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-01-01

Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms’ performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. PMID:28110725

Automated Composites Processing Technology: Film Module

NASA Technical Reports Server (NTRS)

Hulcher, A. Bruce

2004-01-01

NASA's Marshall Space Flight Center (MSFC) has developed a technology that combines a film/adhesive laydown module with fiber placement technology to enable the processing of composite prepreg tow/tape and films, foils or adhesives on the same placement machine. The development of this technology grew out of NASA's need for lightweight, permeation-resistant cryogenic propellant tanks. Autoclave processing of high performance composites results in thermally-induced stresses due to differences in the coefficients of thermal expansion of the fiber and matrix resin components. These stresses, together with the reduction in temperature due to cryogen storage, tend to initiate microcracking within the composite tank wall. One way in which to mitigate this problem is to introduce a thin, crack-resistant polymer film or foil into the tank wall. Investigation into methods to automate the processing of thin film or foil materials into composites led to the development of this technology. The concept employs an automated film supply and feed module that may be designed to fit existing fiber placement machines, or may be designed as integral equipment to new machines. This patent-pending technology can be designed such that both film and foil materials may be processed simultaneously, leading to a decrease in part build cycle time. The module may be designed having a compaction device independent of the host machine, or may utilize the host machine's compactor. The film module functions are controlled by a dedicated system independent of the fiber placement machine controls. The film, foil, or adhesive is processed via pre-existing placement machine run programs, further reducing operational expense.

Stability analysis of free piston Stirling engines

NASA Astrophysics Data System (ADS)

Bégot, Sylvie; Layes, Guillaume; Lanzetta, François; Nika, Philippe

2013-03-01

This paper presents a stability analysis of a free piston Stirling engine. The model and the detailed calculation of pressures losses are exposed. Stability of the machine is studied by the observation of the eigenvalues of the model matrix. Model validation based on the comparison with NASA experimental results is described. The influence of operational and construction parameters on performance and stability issues is exposed. The results show that most parameters that are beneficial for machine power seem to induce irregular mechanical characteristics with load, suggesting that self-sustained oscillations could be difficult to maintain and control.

Are School Vending Machines Loaded with Calories and Fat: An Assessment of 106 Middle and High Schools

ERIC Educational Resources Information Center

Pasch, Keryn E.; Lytle, Leslie A.; Samuelson, Anne C.; Farbakhsh, Kian; Kubik, Martha Y.; Patnode, Carrie D.

2011-01-01

Background: The purpose of this study was to describe the extent to which vending offerings in 106 schools in the St. Paul-Minneapolis, Minnesota metropolitan area, met criteria for types of beverages, fat, and calories based on selected criteria offered by the Institute of Medicine. Methods: Schools where youth participants were attending for the…

Development of an instructional expert system for hole drilling processes

NASA Technical Reports Server (NTRS)

Al-Mutawa, Souhaila; Srinivas, Vijay; Moon, Young Bai

1990-01-01

An expert system which captures the expertise of workshop technicians in the drilling domain was developed. The expert system is aimed at novice technicians who know how to operate the machines but have not acquired the decision making skills that are gained with experience. This paper describes the domain background and the stages of development of the expert system.

Computers in Education: An Overview. Publication Number One. Software Engineering/Education Cooperative Project.

ERIC Educational Resources Information Center

Collis, Betty; Muir, Walter

The first of four major sections in this report presents an overview of the background and evolution of computer applications to learning and teaching. It begins with the early attempts toward "automated teaching" of the 1920s, and the "teaching machines" of B. F. Skinner of the 1940s through the 1960s. It then traces the…

Becky's Legacy: Personal and Professional Reflections on Loss and Hope

ERIC Educational Resources Information Center

Werth, James L., Jr.

2005-01-01

The author, a psychologist who has been specializing in end-of-life issues for over a decade, uses the death of his fiancee (Becky), following the withdrawal of a ventilator and the refusal to place her back on the machine, to discuss research and analysis of end-of-life care in the United States. After briefly discussing his own background,…

Visual Tracking Based on Extreme Learning Machine and Sparse Representation

PubMed Central

Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen

2015-01-01

The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. PMID:26506359

Calculation of the compounded uncertainty of 14C AMS measurements

NASA Astrophysics Data System (ADS)

Nadeau, Marie-Josée; Grootes, Pieter M.

2013-01-01

The correct method to calculate conventional 14C ages from the carbon isotopic ratios was summarised 35 years ago by Stuiver and Polach (1977) and is now accepted as the only method to calculate 14C ages. There is, however, no consensus regarding the treatment of AMS data, mainly of the uncertainty of the final result. The estimation and treatment of machine background, process blank, and/or in situ contamination is not uniform between laboratories, leading to differences in 14C results, mainly for older ages. As Donahue (1987) and Currie (1994), among others, mentioned, some laboratories find it important to use the scatter of several measurements as uncertainty while others prefer to use Poisson statistics. The contribution of the scatter of the standards, machine background, process blank, and in situ contamination to the uncertainty of the final 14C result is also treated in different ways. In the early years of AMS, several laboratories found it important to describe their calculation process in details. In recent years, this practise has declined. We present an overview of the calculation process for 14C AMS measurements looking at calculation practises published from the beginning of AMS until present.

Study on intelligent processing system of man-machine interactive garment frame model

NASA Astrophysics Data System (ADS)

Chen, Shuwang; Yin, Xiaowei; Chang, Ruijiang; Pan, Peiyun; Wang, Xuedi; Shi, Shuze; Wei, Zhongqian

2018-05-01

A man-machine interactive garment frame model intelligent processing system is studied in this paper. The system consists of several sensor device, voice processing module, mechanical parts and data centralized acquisition devices. The sensor device is used to collect information on the environment changes brought by the body near the clothes frame model, the data collection device is used to collect the information of the environment change induced by the sensor device, voice processing module is used for speech recognition of nonspecific person to achieve human-machine interaction, mechanical moving parts are used to make corresponding mechanical responses to the information processed by data collection device.it is connected with data acquisition device by a means of one-way connection. There is a one-way connection between sensor device and data collection device, two-way connection between data acquisition device and voice processing module. The data collection device is one-way connection with mechanical movement parts. The intelligent processing system can judge whether it needs to interact with the customer, realize the man-machine interaction instead of the current rigid frame model.

Progress in computational toxicology.

PubMed

Ekins, Sean

2014-01-01

Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed. A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods. The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data. Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications. Copyright © 2013 Elsevier Inc. All rights reserved.

Crystal Orientation Effect on the Subsurface Deformation of Monocrystalline Germanium in Nanometric Cutting.

PubMed

Lai, Min; Zhang, Xiaodong; Fang, Fengzhou

2017-12-01

Molecular dynamics simulations of nanometric cutting on monocrystalline germanium are conducted to investigate the subsurface deformation during and after nanometric cutting. The continuous random network model of amorphous germanium is established by molecular dynamics simulation, and its characteristic parameters are extracted to compare with those of the machined deformed layer. The coordination number distribution and radial distribution function (RDF) show that the machined surface presents the similar amorphous state. The anisotropic subsurface deformation is studied by nanometric cutting on the (010), (101), and (111) crystal planes of germanium, respectively. The deformed structures are prone to extend along the 110 slip system, which leads to the difference in the shape and thickness of the deformed layer on various directions and crystal planes. On machined surface, the greater thickness of subsurface deformed layer induces the greater surface recovery height. In order to get the critical thickness limit of deformed layer on machined surface of germanium, the optimized cutting direction on each crystal plane is suggested according to the relevance of the nanometric cutting to the nanoindentation.

Tribology in secondary wood machining

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

Ko, P.L.; Hawthorne, H.M.; Andiappan, J.

Secondary wood manufacturing covers a wide range of products from furniture, cabinets, doors and windows, to musical instruments. Many of these are now mass produced in sophisticated, high speed numerical controlled machines. The performance and the reliability of the tools are key to an efficient and economical manufacturing process as well as to the quality of the finished products. A program concerned with three aspects of tribology of wood machining, namely, tool wear, tool-wood friction characteristics and wood surface quality characterization, was set up in the Integrated Manufacturing Technologies Institute (IMTI) of the National Research Council of Canada. The studiesmore » include friction and wear mechanism identification and modeling, wear performance of surface-engineered tool materials, friction-induced vibration and cutting efficiency, and the influence of wear and friction on finished products. This research program underlines the importance of tribology in secondary wood manufacturing and at the same time adds new challenges to tribology research since wood is a complex, heterogeneous, material and its behavior during machining is highly sensitive to the surrounding environments and to the moisture content in the work piece.« less

Acceptability of using electronic vending machines to deliver oral rapid HIV self-testing kits: a qualitative study.

PubMed

Young, Sean D; Daniels, Joseph; Chiu, ChingChe J; Bolan, Robert K; Flynn, Risa P; Kwok, Justin; Klausner, Jeffrey D

2014-01-01

Rates of unrecognized HIV infection are significantly higher among Latino and Black men who have sex with men (MSM). Policy makers have proposed that HIV self-testing kits and new methods for delivering self-testing could improve testing uptake among minority MSM. This study sought to conduct qualitative assessments with MSM of color to determine the acceptability of using electronic vending machines to dispense HIV self-testing kits. African American and Latino MSM were recruited using a participant pool from an existing HIV prevention trial on Facebook. If participants expressed interest in using a vending machine to receive an HIV self-testing kit, they were emailed a 4-digit personal identification number (PIN) code to retrieve the test from the machine. We followed up with those who had tested to assess their willingness to participate in an interview about their experience. Twelve kits were dispensed and 8 interviews were conducted. In general, participants expressed that the vending machine was an acceptable HIV test delivery method due to its novelty and convenience. Acceptability of this delivery model for HIV testing kits was closely associated with three main factors: credibility, confidentiality, and convenience. Future research is needed to address issues, such as user-induced errors and costs, before scaling up the dispensing method.

Liquid Hydrogen Pump

DTIC Science & Technology

1964-11-01

Diagram 183 65 Hub’ess Inducer Impeller and Shroud Prior Prior to Brazing 189 66 Hubless Inducer Impeller Assembly After Brazing and Finish Machining...Cross-Section of Shrouded Hubless Indjcer Pump 195 71 Liquid Hydrogen Pump Test Site, San Tan, Arizona 197 72 Installation of Pump and Overall )est Site...speed of 300,000. It operates at a tip speed of 1260 ft per second. The impeller is a shrouded wheel designed with sufficient strength to carry the

Stroke-model-based character extraction from gray-level document images.

PubMed

Ye, X; Cheriet, M; Suen, C Y

2001-01-01

Global gray-level thresholding techniques such as Otsu's method, and local gray-level thresholding techniques such as edge-based segmentation or the adaptive thresholding method are powerful in extracting character objects from simple or slowly varying backgrounds. However, they are found to be insufficient when the backgrounds include sharply varying contours or fonts in different sizes. A stroke-model is proposed to depict the local features of character objects as double-edges in a predefined size. This model enables us to detect thin connected components selectively, while ignoring relatively large backgrounds that appear complex. Meanwhile, since the stroke width restriction is fully factored in, the proposed technique can be used to extract characters in predefined font sizes. To process large volumes of documents efficiently, a hybrid method is proposed for character extraction from various backgrounds. Using the measurement of class separability to differentiate images with simple backgrounds from those with complex backgrounds, the hybrid method can process documents with different backgrounds by applying the appropriate methods. Experiments on extracting handwriting from a check image, as well as machine-printed characters from scene images demonstrate the effectiveness of the proposed model.

Monte Carlo Simulations of Background Spectra in Integral Imager Detectors

NASA Technical Reports Server (NTRS)

Armstrong, T. W.; Colborn, B. L.; Dietz, K. L.; Ramsey, B. D.; Weisskopf, M. C.

1998-01-01

Predictions of the expected gamma-ray backgrounds in the ISGRI (CdTe) and PiCsIT (Csl) detectors on INTEGRAL due to cosmic-ray interactions and the diffuse gamma-ray background have been made using a coupled set of Monte Carlo radiation transport codes (HETC, FLUKA, EGS4, and MORSE) and a detailed, 3-D mass model of the spacecraft and detector assemblies. The simulations include both the prompt background component from induced hadronic and electromagnetic cascades and the delayed component due to emissions from induced radioactivity. Background spectra have been obtained with and without the use of active (BGO) shielding and charged particle rejection to evaluate the effectiveness of anticoincidence counting on background rejection.

Beam-induced and cosmic-ray backgrounds observed in the ATLAS detector during the LHC 2012 proton-proton running period

NASA Astrophysics Data System (ADS)

Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Abeloos, B.; Aben, R.; Abolins, M.; AbouZeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agricola, J.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Alconada Verzini, M. J.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amako, K.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amorim, A.; Amoroso, S.; Amram, N.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arduini, G.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Armitage, L. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Baca, M. J.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Baines, J. T.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balestri, T.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barranco Navarro, L.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Basye, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bedognetti, M.; Bee, C. P.; Beemster, L. J.; Beermann, T. A.; Begel, M.; Behr, J. K.; Belanger-Champagne, C.; Bell, A. S.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Belyaev, N. L.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Benhar Noccioli, E.; Benitez, J.; Benitez Garcia, J. A.; Benjamin, D. P.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Berghaus, F.; Beringer, J.; Berlendis, S.; Bernard, N. R.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertram, I. A.; Bertsche, C.; Bertsche, D.; Besjes, G. J.; Bessidskaia Bylund, O.; Bessner, M.; Besson, N.; Betancourt, C.; Bethke, S.; Bevan, A. J.; Bhimji, W.; Bianchi, R. M.; Bianchini, L.; Bianco, M.; Biebel, O.; Biedermann, D.; Bielski, R.; Biesuz, N. V.; Biglietti, M.; Bilbao De Mendizabal, J.; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biondi, S.; Bjergaard, D. M.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blanchard, J.-B.; Blanco, J. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blum, W.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Boerner, D.; Bogaerts, J. A.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bold, T.; Boldea, V.; Boldyrev, A. S.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Bortfeldt, J.; Bortoletto, D.; Bortolotto, V.; Bos, K.; Boscherini, D.; Bosman, M.; Bossio Sola, J. D.; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Breaden Madden, W. D.; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; Broughton, J. H.; Bruce, R.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruneliere, R.; Bruni, A.; Bruni, G.; Brunt, BH; Bruschi, M.; Bruscino, N.; Bryant, P.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burckhart, H.; Burdin, S.; Burgard, C. D.; Burghgrave, B.; Burka, K.; Burke, S.; Burmeister, I.; Busato, E.; Büscher, D.; Büscher, V.; Bussey, P.; Butler, J. M.; Butt, A. I.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Cabrera Urbán, S.; Caforio, D.; Cairo, V. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Caloba, L. P.; Calvet, D.; Calvet, S.; Calvet, T. P.; Camacho Toro, R.; Camarda, S.; Camarri, P.; Cameron, D.; Caminal Armadans, R.; Camincher, C.; Campana, S.; Campanelli, M.; Campoverde, A.; Canale, V.; Canepa, A.; Cano Bret, M.; Cantero, J.; Cantrill, R.; Cao, T.; Capeans Garrido, M. D. M.; Caprini, I.; Caprini, M.; Capua, M.; Caputo, R.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, T.; Carlino, G.; Carminati, L.; Caron, S.; Carquin, E.; Carrillo-Montoya, G. D.; Carter, J. R.; Carvalho, J.; Casadei, D.; Casado, M. P.; Casolino, M.; Casper, D. W.; Castaneda-Miranda, E.; Castelli, A.; Castillo Gimenez, V.; Castro, N. F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavallaro, E.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Ceradini, F.; Cerda Alberich, L.; Cerio, B. C.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cerv, M.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chalupkova, I.; Chan, S. K.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chatterjee, A.; Chau, C. C.; Chavez Barajas, C. A.; Che, S.; Cheatham, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, H.; Chen, K.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Cheng, H. C.; Cheng, H. J.; Cheng, Y.; Cheplakov, A.; Cheremushkina, E.; Cherkaoui El Moursli, R.; Chernyatin, V.; Cheu, E.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chitan, A.; Chizhov, M. V.; Choi, K.; Chomont, A. R.; Chouridou, S.; Chow, B. K. B.; Christodoulou, V.; Chromek-Burckhart, D.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Ciapetti, G.; Ciftci, A. K.; Cinca, D.; Cindro, V.; Cioara, I. A.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Ciubancan, M.; Clark, A.; Clark, B. L.; Clark, M. R.; Clark, P. J.; Clarke, R. N.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Coffey, L.; Colasurdo, L.; Cole, B.; Cole, S.; Colijn, A. P.; Collot, J.; Colombo, T.; Compostella, G.; Conde Muiño, P.; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Consorti, V.; Constantinescu, S.; Conta, C.; Conti, G.; Conventi, F.; Cooke, M.; Cooper, B. D.; Cooper-Sarkar, A. M.; Cornelissen, T.; Corradi, M.; Corriveau, F.; Corso-Radu, A.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Cranmer, K.; Crawley, S. J.; Cree, G.; Crépé-Renaudin, S.; Crescioli, F.; Cribbs, W. A.; Crispin Ortuzar, M.; Cristinziani, M.; Croft, V.; Crosetti, G.; Cuhadar Donszelmann, T.; Cummings, J.; Curatolo, M.; Cúth, J.; Cuthbert, C.; Czirr, H.; Czodrowski, P.; D'Auria, S.; D'Onofrio, M.; Da Cunha Sargedas De Sousa, M. J.; Da Via, C.; Dabrowski, W.; Dai, T.; Dale, O.; Dallaire, F.; Dallapiccola, C.; Dam, M.; Dandoy, J. R.; Dang, N. P.; Daniells, A. C.; Dann, N. S.; Danninger, M.; Dano Hoffmann, M.; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Davey, W.; David, C.; Davidek, T.; Davies, M.; Davison, P.; Davygora, Y.; Dawe, E.; Dawson, I.; Daya-Ishmukhametova, R. K.; De, K.; de Asmundis, R.; De Benedetti, A.; De Castro, S.; De Cecco, S.; De Groot, N.; de Jong, P.; De la Torre, H.; De Lorenzi, F.; De Pedis, D.; De Salvo, A.; De Sanctis, U.; De Santo, A.; De Vivie De Regie, J. B.; Dearnaley, W. J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Deigaard, I.; Del Peso, J.; Del Prete, T.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Deliyergiyev, M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; della Volpe, D.; Delmastro, M.; Delsart, P. A.; Deluca, C.; DeMarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Denysiuk, D.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; Di Ciaccio, A.; Di Ciaccio, L.; Di Clemente, W. K.; Di Donato, C.; Di Girolamo, A.; Di Girolamo, B.; Di Micco, B.; Di Nardo, R.; Di Simone, A.; Di Sipio, R.; Di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Diglio, S.; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dohmae, T.; Dolejsi, J.; Dolezal, Z.; Dolgoshein, B. A.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. 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Meyer Zu; Middleton, R. P.; Miglioranzi, S.; Mijović, L.; Mikenberg, G.; Mikestikova, M.; Mikuž, M.; Milesi, M.; Milic, A.; Miller, D. W.; Mills, C.; Milov, A.; Milstead, D. A.; Minaenko, A. A.; Minami, Y.; Minashvili, I. A.; Mincer, A. I.; Mindur, B.; Mineev, M.; Ming, Y.; Mir, L. M.; Mistry, K. P.; Mitani, T.; Mitrevski, J.; Mitsou, V. A.; Miucci, A.; Miyagawa, P. S.; Mjörnmark, J. U.; Moa, T.; Mochizuki, K.; Mohapatra, S.; Mohr, W.; Molander, S.; Moles-Valls, R.; Monden, R.; Mondragon, M. C.; Mönig, K.; Monk, J.; Monnier, E.; Montalbano, A.; Montejo Berlingen, J.; Monticelli, F.; Monzani, S.; Moore, R. W.; Morange, N.; Moreno, D.; Moreno Llácer, M.; Morettini, P.; Mori, D.; Mori, T.; Morii, M.; Morinaga, M.; Morisbak, V.; Moritz, S.; Morley, A. K.; Mornacchi, G.; Morris, J. D.; Mortensen, S. S.; Morvaj, L.; Mosidze, M.; Moss, J.; Motohashi, K.; Mount, R.; Mountricha, E.; Mouraviev, S. V.; Moyse, E. J. W.; Muanza, S.; Mudd, R. D.; Mueller, F.; Mueller, J.; Mueller, R. S. P.; Mueller, T.; Muenstermann, D.; Mullen, P.; Mullier, G. A.; Munoz Sanchez, F. J.; Murillo Quijada, J. A.; Murray, W. J.; Musheghyan, H.; Muskinja, M.; Myagkov, A. G.; Myska, M.; Nachman, B. P.; Nackenhorst, O.; Nadal, J.; Nagai, K.; Nagai, R.; Nagano, K.; Nagasaka, Y.; Nagata, K.; Nagel, M.; Nagy, E.; Nairz, A. M.; Nakahama, Y.; Nakamura, K.; Nakamura, T.; Nakano, I.; Namasivayam, H.; Naranjo Garcia, R. F.; Narayan, R.; Narrias Villar, D. I.; Naryshkin, I.; Naumann, T.; Navarro, G.; Nayyar, R.; Neal, H. A.; Nechaeva, P. Yu.; Neep, T. J.; Nef, P. D.; Negri, A.; Negrini, M.; Nektarijevic, S.; Nellist, C.; Nelson, A.; Nemecek, S.; Nemethy, P.; Nepomuceno, A. A.; Nessi, M.; Neubauer, M. S.; Neumann, M.; Neves, R. M.; Nevski, P.; Newman, P. R.; Nguyen, D. H.; Nickerson, R. B.; Nicolaidou, R.; Nicquevert, B.; Nielsen, J.; Nikiforov, A.; Nikolaenko, V.; Nikolic-Audit, I.; Nikolopoulos, K.; Nilsen, J. K.; Nilsson, P.; Ninomiya, Y.; Nisati, A.; Nisius, R.; Nobe, T.; Nodulman, L.; Nomachi, M.; Nomidis, I.; Nooney, T.; Norberg, S.; Nordberg, M.; Norjoharuddeen, N.; Novgorodova, O.; Nowak, S.; Nozaki, M.; Nozka, L.; Ntekas, K.; Nurse, E.; Nuti, F.; O'grady, F.; O'Neil, D. C.; O'Rourke, A. A.; O'Shea, V.; Oakham, F. G.; Oberlack, H.; Obermann, T.; Ocariz, J.; Ochi, A.; Ochoa, I.; Ochoa-Ricoux, J. P.; Oda, S.; Odaka, S.; Ogren, H.; Oh, A.; Oh, S. H.; Ohm, C. C.; Ohman, H.; Oide, H.; Okawa, H.; Okumura, Y.; Okuyama, T.; Olariu, A.; Oleiro Seabra, L. F.; Olivares Pino, S. A.; Oliveira Damazio, D.; Olszewski, A.; Olszowska, J.; Onofre, A.; Onogi, K.; Onyisi, P. U. E.; Oram, C. J.; Oreglia, M. J.; Oren, Y.; Orestano, D.; Orlando, N.; Orr, R. S.; Osculati, B.; Ospanov, R.; Garzon, G. Otero y.; Otono, H.; Ouchrif, M.; Ould-Saada, F.; Ouraou, A.; Oussoren, K. P.; Ouyang, Q.; Ovcharova, A.; Owen, M.; Owen, R. E.; Ozcan, V. E.; Ozturk, N.; Pachal, K.; Pacheco Pages, A.; Padilla Aranda, C.; Pagáčová, M.; Pagan Griso, S.; Paige, F.; Pais, P.; Pajchel, K.; Palacino, G.; Palestini, S.; Palka, M.; Pallin, D.; Palm, M.; Palma, A.; Panagiotopoulou, E. St.; Pandini, C. E.; Panduro Vazquez, J. G.; Pani, P.; Panitkin, S.; Pantea, D.; Paolozzi, L.; Papadopoulou, Th. D.; Papageorgiou, K.; Paramonov, A.; Paredes Hernandez, D.; Parker, A. J.; Parker, M. A.; Parker, K. A.; Parodi, F.; Parsons, J. A.; Parzefall, U.; Pascuzzi, V. R.; Pasqualucci, E.; Passaggio, S.; Pastore, F.; Pastore, Fr.; Pásztor, G.; Pataraia, S.; Patel, N. D.; Pater, J. R.; Pauly, T.; Pearce, J.; Pearson, B.; Pedersen, L. E.; Pedersen, M.; Pedraza Lopez, S.; Pedro, R.; Peleganchuk, S. V.; Pelikan, D.; Penc, O.; Peng, C.; Peng, H.; Penwell, J.; Peralva, B. S.; Perego, M. M.; Perepelitsa, D. V.; Perez Codina, E.; Perini, L.; Pernegger, H.; Perrella, S.; Peschke, R.; Peshekhonov, V. D.; Peters, K.; Peters, R. F. Y.; Petersen, B. A.; Petersen, T. C.; Petit, E.; Petridis, A.; Petridou, C.; Petroff, P.; Petrolo, E.; Petrov, M.; Petrucci, F.; Pettersson, N. E.; Peyaud, A.; Pezoa, R.; Phillips, P. W.; Piacquadio, G.; Pianori, E.; Picazio, A.; Piccaro, E.; Piccinini, M.; Pickering, M. A.; Piegaia, R.; Pilcher, J. E.; Pilkington, A. D.; Pin, A. W. J.; Pina, J.; Pinamonti, M.; Pinfold, J. L.; Pingel, A.; Pires, S.; Pirumov, H.; Pitt, M.; Plazak, L.; Pleier, M.-A.; Pleskot, V.; Plotnikova, E.; Plucinski, P.; Pluth, D.; Poettgen, R.; Poggioli, L.; Pohl, D.; Polesello, G.; Poley, A.; Policicchio, A.; Polifka, R.; Polini, A.; Pollard, C. S.; Polychronakos, V.; Pommès, K.; Pontecorvo, L.; Pope, B. G.; Popeneciu, G. A.; Popovic, D. S.; Poppleton, A.; Pospisil, S.; Potamianos, K.; Potrap, I. N.; Potter, C. J.; Potter, C. T.; Poulard, G.; Poveda, J.; Pozdnyakov, V.; Pozo Astigarraga, M. E.; Pralavorio, P.; Pranko, A.; Prell, S.; Price, D.; Price, L. E.; Primavera, M.; Prince, S.; Proissl, M.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Przybycien, M.; Puddu, D.; Puldon, D.; Purohit, M.; Puzo, P.; Qian, J.; Qin, G.; Qin, Y.; Quadt, A.; Quayle, W. B.; Queitsch-Maitland, M.; Quilty, D.; Raddum, S.; Radeka, V.; Radescu, V.; Radhakrishnan, S. K.; Radloff, P.; Rados, P.; Ragusa, F.; Rahal, G.; Raine, J. A.; Rajagopalan, S.; Rammensee, M.; Rangel-Smith, C.; Ratti, M. G.; Rauscher, F.; Rave, S.; Ravenscroft, T.; Raymond, M.; Read, A. L.; Readioff, N. P.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reeves, K.; Rehnisch, L.; Reichert, J.; Reisin, H.; Rembser, C.; Ren, H.; Rescigno, M.; Resconi, S.; Rezanova, O. L.; Reznicek, P.; Rezvani, R.; Richter, R.; Richter, S.; Richter-Was, E.; Ricken, O.; Ridel, M.; Rieck, P.; Riegel, C. J.; Rieger, J.; Rifki, O.; Rijssenbeek, M.; Rimoldi, A.; Rinaldi, L.; Ristić, B.; Ritsch, E.; Riu, I.; Rizatdinova, F.; Rizvi, E.; Rizzi, C.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J. E. M.; Robson, A.; Roda, C.; Rodina, Y.; Rodriguez Perez, A.; Rodriguez Rodriguez, D.; Roe, S.; Rogan, C. S.; RØhne, O.; Romaniouk, A.; Romano, M.; Romano Saez, S. M.; Romero Adam, E.; Rompotis, N.; Ronzani, M.; Roos, L.; Ros, E.; Rosati, S.; Rosbach, K.; Rose, P.; Rosenthal, O.; Rossetti, V.; Rossi, E.; Rossi, L. P.; Rosten, J. H. N.; Rosten, R.; Rotaru, M.; Roth, I.; Rothberg, J.; Rousseau, D.; Royon, C. R.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Rubinskiy, I.; Rud, V. I.; Rudolph, M. S.; Rühr, F.; Ruiz-Martinez, A.; Rurikova, Z.; Rusakovich, N. A.; Ruschke, A.; Russell, H. L.; Rutherfoord, J. P.; Ruthmann, N.; Ryabov, Y. F.; Rybar, M.; Rybkin, G.; Ryu, S.; Ryzhov, A.; Saavedra, A. F.; Sabato, G.; Sacerdoti, S.; Sadrozinski, H. F.-W.; Sadykov, R.; Safai Tehrani, F.; Saha, P.; Sahinsoy, M.; Saimpert, M.; Saito, T.; Sakamoto, H.; Sakurai, Y.; Salamanna, G.; Salamon, A.; Salazar Loyola, J. E.; Salek, D.; Sales De Bruin, P. H.; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sanchez, A.; Sánchez, J.; Sanchez Martinez, V.; Sandaker, H.; Sandbach, R. L.; Sander, H. G.; Sanders, M. P.; Sandhoff, M.; Sandoval, C.; Sandstroem, R.; Sankey, D. P. C.; Sannino, M.; Sansoni, A.; Santoni, C.; Santonico, R.; Santos, H.; Santoyo Castillo, I.; Sapp, K.; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sasaki, Y.; Sato, K.; Sauvage, G.; Sauvan, E.; Savage, G.; Savard, P.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. A.; Scarcella, M.; Scarfone, V.; Schaarschmidt, J.; Schacht, P.; Schaefer, D.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Scharf, V.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schneider, B.; Schnellbach, Y. J.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schorlemmer, A. L. S.; Schott, M.; Schovancova, J.; Schramm, S.; Schreyer, M.; Schuh, N.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwanenberger, C.; Schwartzman, A.; Schwarz, T. A.; Schwegler, Ph.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Schwindt, T.; Sciolla, G.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seifert, F.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Seliverstov, D. M.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shiyakova, M.; Shmeleva, A.; Shoaleh Saadi, D.; Shochet, M. J.; Shojaii, S.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sidebo, P. E.; Sidiropoulou, O.; Sidorov, D.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silverstein, S. B.; Simak, V.; Simard, O.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, D.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Sivoklokov, S. Yu.; Sjölin, J.; Sjursen, T. B.; Skinner, M. B.; Skottowe, H. P.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Slovak, R.; Smakhtin, V.; Smart, B. H.; Smestad, L.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snidero, G.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Soh, D. A.; Sokhrannyi, G.; Solans Sanchez, C. A.; Solar, M.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Son, H.; Song, H. Y.; Sood, A.; Sopczak, A.; Sopko, V.; Sorin, V.; Sosa, D.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Sperlich, D.; Spettel, F.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; St. Denis, R. D.; Stabile, A.; Stahlman, J.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanescu-Bellu, M.; Stanitzki, M. M.; Stapnes, S.; Starchenko, E. A.; Stark, G. H.; Stark, J.; Staroba, P.; Starovoitov, P.; Stärz, S.; Staszewski, R.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stillings, J. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Subramaniam, R.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Sundermann, J. E.; Suruliz, K.; Susinno, G.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Sykora, I.; Sykora, T.; Ta, D.; Taccini, C.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takai, H.; Takashima, R.; Takeda, H.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tam, J. Y. C.; Tan, K. G.; Tanaka, J.; Tanaka, R.; Tanaka, S.; Tannenwald, B. B.; Tapia Araya, S.; Tapprogge, S.; Tarem, S.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Tavares Delgado, A.; Tayalati, Y.; Taylor, A. C.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teischinger, F. A.; Teixeira-Dias, P.; Temming, K. K.; Temple, D.; Ten Kate, H.; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, E. N.; Thompson, P. D.; Thompson, R. J.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Thomson, M.; Tibbetts, M. J.; Ticse Torres, R. E.; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorov, T.; Todorova-Nova, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Torrence, E.; Torres, H.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Trefzger, T.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tudorache, A.; Tudorache, V.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turecek, D.; Turgeman, D.; Turra, R.; Turvey, A. J.; Tuts, P. M.; Tyndel, M.; Ucchielli, G.; Ueda, I.; Ueno, R.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usanova, A.; Vacavant, L.; Vacek, V.; Vachon, B.; Valderanis, C.; Valdes Santurio, E.; Valencic, N.; Valentinetti, S.; Valero, A.; Valery, L.; Valkar, S.; Vallecorsa, S.; Valls Ferrer, J. A.; Van Den Wollenberg, W.; Van Der Deijl, P. C.; van der Geer, R.; van der Graaf, H.; van Eldik, N.; van Gemmeren, P.; Van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vanguri, R.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vazeille, F.; Vazquez Schroeder, T.; Veatch, J.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vest, A.; Vetterli, M. C.; Viazlo, O.; Vichou, I.; Vickey, T.; Boeriu, O. E. Vickey; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Vigne, R.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vlasak, M.; Vogel, M.; Vokac, P.; Volpi, G.; Volpi, M.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Vykydal, Z.; Wagner, P.; Wagner, W.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, K.; Wang, R.; Wang, S. M.; Wang, T.; Wang, T.; Wang, X.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, I. J.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, S.; Weber, M. S.; Weber, S. W.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, P.; Wessels, M.; Wetter, J.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A.; White, M. J.; White, R.; White, S.; Whiteson, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wittkowski, J.; Wollstadt, S. J.; Wolter, M. W.; Wolters, H.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wozniak, K. W.; Wu, M.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xu, D.; Xu, L.; Yabsley, B.; Yacoob, S.; Yakabe, R.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Yau Wong, K. H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yen, A. L.; Yildirim, E.; Yorita, K.; Yoshida, R.; Yoshihara, K.; Young, C.; Young, C. J. S.; Youssef, S.; Yu, D. R.; Yu, J.; Yu, J. M.; Yu, J.; Yuan, L.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanello, L.; Zanzi, D.; Zeitnitz, C.; Zeman, M.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zengel, K.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhong, J.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, L.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; zur Nedden, M.; Zurzolo, G.; Zwalinski, L.

2016-05-01

This paper discusses various observations on beam-induced and cosmic-ray backgrounds in the ATLAS detector during the LHC 2012 proton-proton run. Building on published results based on 2011 data, the correlations between background and residual pressure of the beam vacuum are revisited. Ghost charge evolution over 2012 and its role for backgrounds are evaluated. New methods to monitor ghost charge with beam-gas rates are presented and observations of LHC abort gap population by ghost charge are discussed in detail. Fake jets from colliding bunches and from ghost charge are analysed with improved methods, showing that ghost charge in individual radio-frequency buckets of the LHC can be resolved. Some results of two short periods of dedicated cosmic-ray background data-taking are shown; in particular cosmic-ray muon induced fake jet rates are compared to Monte Carlo simulations and to the fake jet rates from beam background. A thorough analysis of a particular LHC fill, where abnormally high background was observed, is presented. Correlations between backgrounds and beam intensity losses in special fills with very high β* are studied.

Beam-induced and cosmic-ray backgrounds observed in the ATLAS detector during the LHC 2012 proton-proton running period

DOE PAGES

Aad, G.; Abbott, B.; Abdallah, J.; ...

2016-05-20

This paper discusses various observations on beam-induced and cosmic-ray backgrounds in the ATLAS detector during the LHC 2012 proton-proton run. Building on published results based on 2011 data, the correlations between background and residual pressure of the beam vacuum are revisited. Ghost charge evolution over 2012 and its role for backgrounds are evaluated. New methods to monitor ghost charge with beam-gas rates are presented and observations of LHC abort gap population by ghost charge are discussed in detail. Fake jets from colliding bunches and from ghost charge are analysed with improved methods, showing that ghost charge in individual radio-frequency bucketsmore » of the LHC can be resolved. Some results of two short periods of dedicated cosmic-ray background data-taking are shown; in particular cosmic-ray muon induced fake jet rates are compared to Monte Carlo simulations and to the fake jet rates from beam background. A thorough analysis of a particular LHC fill, where abnormally high background was observed, is presented. Correlations between backgrounds and beam intensity losses in special fills with very high β* are studied.« less

System and method for heating ferrite magnet motors for low temperatures

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

Reddy, Patel Bhageerath; El-Refaie, Ayman Mohamed Fawzi; Huh, Kum-Kang

A system and method for heating ferrite permanent magnets in an electrical machine is disclosed. The permanent magnet machine includes a stator assembly and a rotor assembly, with a plurality of ferrite permanent magnets disposed within the stator assembly or the rotor assembly to generate a magnetic field that interacts with a stator magnetic field to produce a torque. A controller of the electrical machine is programmed to cause a primary field current to be applied to the stator windings to generate the stator magnetic field, so as to cause the rotor assembly to rotate relative to the stator assembly.more » The controller is further programmed to cause a secondary current to be applied to the stator windings to selectively generate a secondary magnetic field, the secondary magnetic field inducing eddy currents in at least one of the stator assembly and the rotor assembly to heat the ferrite permanent magnets.« 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.

Effect of Turning and Ball Burnishing on the Microstructure and Residual Stress Distribution in Stainless Steel Cold Spray Deposits

NASA Astrophysics Data System (ADS)

Sova, A.; Courbon, C.; Valiorgue, F.; Rech, J.; Bertrand, Ph.

2017-12-01

In this paper, an experimental study of influence of machining by turning and ball burnishing on the surface morphology, structure and residual stress distribution of cold spray 17-4 PH stainless steel deposits is provided. It is shown that cold spray deposits could be machined by turning under parameters closed to turning of bulk 17-4 PH stainless steel. Ball burnishing process permits to decrease surface roughness. Cross-sectional observation revealed that the turning and ball burnishing process allowed microstructure changes in the coating near-surface zone. In particular, significant particle deformation and particle boundary fragmentation is observed. Measurements of residual stresses showed that residual stresses in the as-spray deposit are compressive. After machining by turning, tensile residual stresses in the near-surface zone were induced. Further surface finishing of turned coating by ball burnishing allowed the establishment of the compressive residual stresses in the coating.

System and method for heating ferrite magnet motors for low temperatures

DOEpatents

Reddy, Patel Bhageerath; El-Refaie, Ayman Mohamed Fawzi; Huh, Kum-Kang

2017-07-04

A system and method for heating ferrite permanent magnets in an electrical machine is disclosed. The permanent magnet machine includes a stator assembly and a rotor assembly, with a plurality of ferrite permanent magnets disposed within the stator assembly or the rotor assembly to generate a magnetic field that interacts with a stator magnetic field to produce a torque. A controller of the electrical machine is programmed to cause a primary field current to be applied to the stator windings to generate the stator magnetic field, so as to cause the rotor assembly to rotate relative to the stator assembly. The controller is further programmed to cause a secondary current to be applied to the stator windings to selectively generate a secondary magnetic field, the secondary magnetic field inducing eddy currents in at least one of the stator assembly and the rotor assembly to heat the ferrite permanent magnets.

Adaptive vibration control using synchronous demodulation with machine tool controller motor commutation

DOEpatents

Hopkins, David James [Livermore, CA

2008-05-13

A control system and method for actively reducing vibration in a spindle housing caused by unbalance forces on a rotating spindle, by measuring the force-induced spindle-housing motion, determining control signals based on synchronous demodulation, and provide compensation for the measured displacement to cancel or otherwise reduce or attenuate the vibration. In particular, the synchronous demodulation technique is performed to recover a measured spindle housing displacement signal related only to the rotation of a machine tool spindle, and consequently rejects measured displacement not related to spindle motion or synchronous to a cycle of revolution. Furthermore, the controller actuates at least one voice-coil (VC) motor, to cancel the original force-induced motion, and adapts the magnitude of voice coil signal until this measured displacement signal is brought to a null. In order to adjust the signal to a null, it must have the correct phase relative to the spindle angle. The feedback phase signal is used to adjust a common (to both outputs) commutation offset register (offset relative to spindle encoder angle) to force the feedback phase signal output to a null. Once both of these feedback signals are null, the system is compensating properly for the spindle-induced motion.

Shock wave and flame front induced detonation in a rapid compression machine

NASA Astrophysics Data System (ADS)

Wang, Y.; Qi, Y.; Xiang, S.; Mével, R.; Wang, Z.

2018-05-01

The present study focuses on one mode of detonation initiation observed in a rapid compression machine (RCM). This mode is referred to as shock wave and flame front-induced detonation (SWFID). Experimental high-speed imaging and two-dimensional numerical simulations with skeletal chemistry are combined to unravel the dominant steps of detonation initiation under SWFID conditions. It is shown that the interaction between the shock wave generated by the end-gas auto-ignition and the spherical flame creates a region of high pressure and temperature which enables the acceleration of the flame front and the detonation onset. The experimental observation lacks adequate spatial and temporal resolution despite good reproducibility of the detonation onset. Based on the numerical results, phenomenological interpretation of the event within the framework of shock wave refraction indicates that the formation of a free-precursor shock wave at the transition between regular and irregular refraction may be responsible for detonation onset. The present results along with previous findings on shock wave reflection-induced detonation in the RCM indicate that super-knock occurs after the interaction of the shock wave generated by end-gas auto-ignition with the RCM walls, preignition flame, or another shock wave.

An in situ probe for on-line monitoring of cell density and viability on the basis of dark field microscopy in conjunction with image processing and supervised machine learning.

PubMed

Wei, Ning; You, Jia; Friehs, Karl; Flaschel, Erwin; Nattkemper, Tim Wilhelm

2007-08-15

Fermentation industries would benefit from on-line monitoring of important parameters describing cell growth such as cell density and viability during fermentation processes. For this purpose, an in situ probe has been developed, which utilizes a dark field illumination unit to obtain high contrast images with an integrated CCD camera. To test the probe, brewer's yeast Saccharomyces cerevisiae is chosen as the target microorganism. Images of the yeast cells in the bioreactors are captured, processed, and analyzed automatically by means of mechatronics, image processing, and machine learning. Two support vector machine based classifiers are used for separating cells from background, and for distinguishing live from dead cells afterwards. The evaluation of the in situ experiments showed strong correlation between results obtained by the probe and those by widely accepted standard methods. Thus, the in situ probe has been proved to be a feasible device for on-line monitoring of both cell density and viability with high accuracy and stability. (c) 2007 Wiley Periodicals, Inc.

Hydrogen exchange mass spectrometry of bacteriorhodopsin reveals light-induced changes in the structural dynamics of a biomolecular machine.

PubMed

Pan, Yan; Brown, Leonid; Konermann, Lars

2011-12-21

Many proteins act as molecular machines that are fuelled by a nonthermal energy source. Examples include transmembrane pumps and stator-rotor complexes. These systems undergo cyclic motions (CMs) that are being driven along a well-defined conformational trajectory. Superimposed on these CMs are thermal fluctuations (TFs) that are coupled to stochastic motions of the solvent. Here we explore whether the TFs of a molecular machine are affected by the occurrence of CMs. Bacteriorhodopsin (BR) is a light-driven proton pump that serves as a model system in this study. The function of BR is based on a photocycle that involves trans/cis isomerization of a retinal chromophore, as well as motions of transmembrane helices. Hydrogen/deuterium exchange (HDX) mass spectrometry was used to monitor the TFs of BR, focusing on the monomeric form of the protein. Comparative HDX studies were conducted under illumination and in the dark. The HDX kinetics of BR are dramatically accelerated in the presence of light. The isotope exchange rates and the number of backbone amides involved in EX2 opening transitions increase roughly 2-fold upon illumination. In contrast, light/dark control experiments on retinal-free protein produced no discernible differences. It can be concluded that the extent of TFs in BR strongly depends on photon-driven CMs. The light-induced differences in HDX behavior are ascribed to protein destabilization. Specifically, the thermodynamic stability of the dark-adapted protein is estimated to be 5.5 kJ mol(-1) under the conditions of our work. This value represents the free energy difference between the folded state F and a significantly unfolded conformer U. Illumination reduces the stability of F by 2.2 kJ mol(-1). Mechanical agitation caused by isomerization of the chromophore is transferred to the surrounding protein scaffold, and subsequently, the energy dissipates into the solvent. Light-induced retinal motions therefore act analogously to an internal heat source that promotes the occurrence of TFs. Overall, our data highlight the potential of HDX methods for probing the structural dynamics of molecular machines under "engine on" and "engine off" conditions. © 2011 American Chemical Society

Machine-learned analysis of the association of next-generation sequencing-based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin.

PubMed

Kringel, Dario; Geisslinger, Gerd; Resch, Eduard; Oertel, Bruno G; Thrun, Michael C; Heinemann, Sarah; Lötsch, Jörn

2018-03-27

Heat pain and its modulation by capsaicin varies among subjects in experimental and clinical settings. A plausible cause is a genetic component, of which TRPV1 ion channels, by their response to both heat and capsaicin, are primary candidates. However, TRPA1 channels can heterodimerize with TRPV1 channels and carry genetic variants reported to modulate heat pain sensitivity. To address the role of these candidate genes in capsaicin-induced hypersensitization to heat, pain thresholds acquired before and after topical application of capsaicin and TRPA1/TRPV1 exomic sequences derived by next-generation sequencing were assessed in n = 75 healthy volunteers and the genetic information comprised 278 loci. Gaussian mixture modeling indicated 2 phenotype groups with high or low capsaicin-induced hypersensitization to heat. Unsupervised machine learning implemented as swarm-based clustering hinted at differences in the genetic pattern between these phenotype groups. Several methods of supervised machine learning implemented as random forests, adaptive boosting, k-nearest neighbors, naive Bayes, support vector machines, and for comparison, binary logistic regression predicted the phenotype group association consistently better when based on the observed genotypes than when using a random permutation of the exomic sequences. Of note, TRPA1 variants were more important for correct phenotype group association than TRPV1 variants. This indicates a role of the TRPA1 and TRPV1 next-generation sequencing-based genetic pattern in the modulation of the individual response to heat-related pain phenotypes. When considering earlier evidence that topical capsaicin can induce neuropathy-like quantitative sensory testing patterns in healthy subjects, implications for future analgesic treatments with transient receptor potential inhibitors arise.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

A novel baseline correction method using convex optimization framework in laser-induced breakdown spectroscopy quantitative analysis

NASA Astrophysics Data System (ADS)

Yi, Cancan; Lv, Yong; Xiao, Han; Ke, Ke; Yu, Xun

2017-12-01

For laser-induced breakdown spectroscopy (LIBS) quantitative analysis technique, baseline correction is an essential part for the LIBS data preprocessing. As the widely existing cases, the phenomenon of baseline drift is generated by the fluctuation of laser energy, inhomogeneity of sample surfaces and the background noise, which has aroused the interest of many researchers. Most of the prevalent algorithms usually need to preset some key parameters, such as the suitable spline function and the fitting order, thus do not have adaptability. Based on the characteristics of LIBS, such as the sparsity of spectral peaks and the low-pass filtered feature of baseline, a novel baseline correction and spectral data denoising method is studied in this paper. The improved technology utilizes convex optimization scheme to form a non-parametric baseline correction model. Meanwhile, asymmetric punish function is conducted to enhance signal-noise ratio (SNR) of the LIBS signal and improve reconstruction precision. Furthermore, an efficient iterative algorithm is applied to the optimization process, so as to ensure the convergence of this algorithm. To validate the proposed method, the concentration analysis of Chromium (Cr),Manganese (Mn) and Nickel (Ni) contained in 23 certified high alloy steel samples is assessed by using quantitative models with Partial Least Squares (PLS) and Support Vector Machine (SVM). Because there is no prior knowledge of sample composition and mathematical hypothesis, compared with other methods, the method proposed in this paper has better accuracy in quantitative analysis, and fully reflects its adaptive ability.

Minimizing makespan in a two-stage flow shop with parallel batch-processing machines and re-entrant jobs

NASA Astrophysics Data System (ADS)

Huang, J. D.; Liu, J. J.; Chen, Q. X.; Mao, N.

2017-06-01

Against a background of heat-treatment operations in mould manufacturing, a two-stage flow-shop scheduling problem is described for minimizing makespan with parallel batch-processing machines and re-entrant jobs. The weights and release dates of jobs are non-identical, but job processing times are equal. A mixed-integer linear programming model is developed and tested with small-scale scenarios. Given that the problem is NP hard, three heuristic construction methods with polynomial complexity are proposed. The worst case of the new constructive heuristic is analysed in detail. A method for computing lower bounds is proposed to test heuristic performance. Heuristic efficiency is tested with sets of scenarios. Compared with the two improved heuristics, the performance of the new constructive heuristic is superior.

Use of a Machine Learning-Based High Content Analysis Approach to Identify Photoreceptor Neurite Promoting Molecules.

PubMed

Fuller, John A; Berlinicke, Cynthia A; Inglese, James; Zack, Donald J

2016-01-01

High content analysis (HCA) has become a leading methodology in phenotypic drug discovery efforts. Typical HCA workflows include imaging cells using an automated microscope and analyzing the data using algorithms designed to quantify one or more specific phenotypes of interest. Due to the richness of high content data, unappreciated phenotypic changes may be discovered in existing image sets using interactive machine-learning based software systems. Primary postnatal day four retinal cells from the photoreceptor (PR) labeled QRX-EGFP reporter mice were isolated, seeded, treated with a set of 234 profiled kinase inhibitors and then cultured for 1 week. The cells were imaged with an Acumen plate-based laser cytometer to determine the number and intensity of GFP-expressing, i.e. PR, cells. Wells displaying intensities and counts above threshold values of interest were re-imaged at a higher resolution with an INCell2000 automated microscope. The images were analyzed with an open source HCA analysis tool, PhenoRipper (Rajaram et al., Nat Methods 9:635-637, 2012), to identify the high GFP-inducing treatments that additionally resulted in diverse phenotypes compared to the vehicle control samples. The pyrimidinopyrimidone kinase inhibitor CHEMBL-1766490, a pan kinase inhibitor whose major known targets are p38α and the Src family member lck, was identified as an inducer of photoreceptor neuritogenesis by using the open-source HCA program PhenoRipper. This finding was corroborated using a cell-based method of image analysis that measures quantitative differences in the mean neurite length in GFP expressing cells. Interacting with data using machine learning algorithms may complement traditional HCA approaches by leading to the discovery of small molecule-induced cellular phenotypes in addition to those upon which the investigator is initially focusing.

Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation.

PubMed

Saproo, Sameer; Shih, Victor; Jangraw, David C; Sajda, Paul

2016-12-01

We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash-these failures are termed pilot induced oscillations (PIOs). We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)-anterior cingulate cortex (ACC) circuit. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation

NASA Astrophysics Data System (ADS)

Saproo, Sameer; Shih, Victor; Jangraw, David C.; Sajda, Paul

2016-12-01

Objective. We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash—these failures are termed pilot induced oscillations (PIOs). Approach. We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. Main results. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)—anterior cingulate cortex (ACC) circuit. Significance. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

The Geological Susceptibility of Induced Earthquakes in the Duvernay Play

NASA Astrophysics Data System (ADS)

Pawley, Steven; Schultz, Ryan; Playter, Tiffany; Corlett, Hilary; Shipman, Todd; Lyster, Steven; Hauck, Tyler

2018-02-01

Presently, consensus on the incorporation of induced earthquakes into seismic hazard has yet to be established. For example, the nonstationary, spatiotemporal nature of induced earthquakes is not well understood. Specific to the Western Canada Sedimentary Basin, geological bias in seismogenic activation potential has been suggested to control the spatial distribution of induced earthquakes regionally. In this paper, we train a machine learning algorithm to systemically evaluate tectonic, geomechanical, and hydrological proxies suspected to control induced seismicity. Feature importance suggests that proximity to basement, in situ stress, proximity to fossil reef margins, lithium concentration, and rate of natural seismicity are among the strongest model predictors. Our derived seismogenic potential map faithfully reproduces the current distribution of induced seismicity and is suggestive of other regions which may be prone to induced earthquakes. The refinement of induced seismicity geological susceptibility may become an important technique to identify significant underlying geological features and address induced seismic hazard forecasting issues.

Ground Reaction Force and Mechanical Differences Between the Interim Resistive Exercise Device (iRED) and Smith Machine While Performing a Squat

NASA Technical Reports Server (NTRS)

Amonette, William E.; Bentley, Jason R.; Lee, Stuart M. C.; Loehr, James A.; Schneider, Suzanne

2004-01-01

Musculoskeletal unloading in microgravity has been shown to induce losses in bone mineral density, muscle cross-sectional area, and muscle strength. Currently, an Interim Resistive Exercise Device (iRED) is being flown on board the ISS to help counteract these losses. Free weight training has shown successful positive musculoskeletal adaptations. In biomechanical research, ground reaction forces (GRF) trajectories are used to define differences between exercise devices. The purpose of this evaluation is to quantify the differences in GRF between the iRED and free weight exercise performed on a Smith machine during a squat. Due to the differences in resistance properties, inertial loading and load application to the body between the two devices, we hypothesize that subjects using iRED will produce GRF that are significantly different from the Smith machine. There will be differences in bar/harness range of motion and the time when peak GRF occurred in the ROMbar. Three male subjects performed three sets of ten squats on the iRED and on the Smith Machine on two separate days at a 2-second cadence. Statistically significant differences were found between the two devices in all measured GRF variables. Average Fz and Fx during the Smith machine squat were significantly higher than iRED. Average Fy (16.82 plus or minus.23; p less than .043) was significantly lower during the Smith machine squat. The mean descent/ascent ratio of the magnitude of the resultant force vector of all three axes for the Smith machine and iRED was 0.95 and 0.72, respectively. Also, the point at which maximum Fz occurred in the range of motion (Dzpeak) was at different locations with the two devices.

JPRS Report West Europe

DTIC Science & Technology

1988-10-06

PASOK Tendencies Identified in Light of Papandreou’s Illness 8 MILITARY FEDERAL REPUBLIC OF GERMANY Air Force Responds to New Requirements of Air-Land...tournament. Hence the need for new rules of the game. 13249/9274 GREECE Intra- PASOK Tendencies Identified in Light of Papandreou’s Illness 35210150...G. Papandreou? Machinations Start Inside PASOK as Everybody Tensely Awaits the News From London" historical background: Alexander the Great died

37. ENGINE ROOM, FROM PORT SIDE OF CONTROL CONSOLE, LOOKING ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

37. ENGINE ROOM, FROM PORT SIDE OF CONTROL CONSOLE, LOOKING TOWARDS STERN, PORT ENGINE AT RIGHT, STARBOARD ENGINE AT LEFT, BOTH ARE DIESEL ENGINES, IN BACKGROUND IS STAIRS UP TO CREWS' BERTHING, BEYONE THE STAIRS IS THE DOOR TO AFT ENGINE ROOM & MACHINE SHOP. - U.S. Coast Guard Cutter WHITE HEATH, USGS Integrated Support Command Boston, 427 Commercial Street, Boston, Suffolk County, MA

CMMI (registered trademark) for Services, Version 1.2

DTIC Science & Technology

2009-02-01

background in information technology, especially those familiar with disciplines like service - oriented architecture (SOA) or software as a service ( SaaS ). In... services , the Software Engineering Institute (SEI) has found several dimensions that an organization can focus on to improve its business. Figure...International Business Machines) and the SEI [Humphrey 1989]. Humphrey’s book, Managing the Software Process, provides a CMMI for Services Version 1.2

Critical role of foreground stimuli in perceiving visually induced self-motion (vection).

PubMed

Nakamura, S; Shimojo, S

1999-01-01

The effects of a foreground stimulus on vection (illusory perception of self-motion induced by a moving background stimulus) were examined in two experiments. The experiments reveal that the presentation of a foreground pattern with a moving background stimulus may affect vection. The foreground stimulus facilitated vection strength when it remained stationary or moved slowly in the opposite direction to that of the background stimulus. On the other hand, there was a strong inhibition of vection when the foreground stimulus moved slowly with, or quickly against, the background. These results suggest that foreground stimuli, as well as background stimuli, play an important role in perceiving self-motion.

A Cognitive Machine Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis from Restrictive Cardiomyopathy

PubMed Central

Sengupta, Partho P.; Huang, Yen-Min; Bansal, Manish; Ashrafi, Ali; Fisher, Matt; Shameer, Khader; Gall, Walt; Dudley, Joel T

2016-01-01

Background Associating a patient’s profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography (STE) data sets derived from patients with known constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Methods and Results Clinical and echocardiographic data of 50 patients with CP and 44 with RCM were used for developing an associative memory classifier (AMC) based machine learning algorithm. The STE data was normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve (AUC) of the AMC was evaluated for differentiating CP from RCM. Using only STE variables, AMC achieved a diagnostic AUC of 89·2%, which improved to 96·2% with addition of 4 echocardiographic variables. In comparison, the AUC of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63·7%, respectively. Furthermore, AMC demonstrated greater accuracy and shorter learning curves than other machine learning approaches with accuracy asymptotically approaching 90% after a training fraction of 0·3 and remaining flat at higher training fractions. Conclusions This study demonstrates feasibility of a cognitive machine learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience. PMID:27266599

Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics

NASA Astrophysics Data System (ADS)

Yu, Tao; Cai, Weiwei; Liu, Yingzheng

2018-04-01

Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics.

PubMed

Yu, Tao; Cai, Weiwei; Liu, Yingzheng

2018-04-01

Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines.

PubMed

Neftci, Emre O; Pedroni, Bruno U; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

2016-01-01

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware.

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

PubMed Central

Neftci, Emre O.; Pedroni, Bruno U.; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

2016-01-01

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware. PMID:27445650

Method and apparatus for predicting the direction of movement in machine vision

NASA Technical Reports Server (NTRS)

Lawton, Teri B. (Inventor)

1992-01-01

A computer-simulated cortical network is presented. The network is capable of computing the visibility of shifts in the direction of movement. Additionally, the network can compute the following: (1) the magnitude of the position difference between the test and background patterns; (2) localized contrast differences at different spatial scales analyzed by computing temporal gradients of the difference and sum of the outputs of paired even- and odd-symmetric bandpass filters convolved with the input pattern; and (3) the direction of a test pattern moved relative to a textured background. The direction of movement of an object in the field of view of a robotic vision system is detected in accordance with nonlinear Gabor function algorithms. The movement of objects relative to their background is used to infer the 3-dimensional structure and motion of object surfaces.

Orthogonal cutting modeling of hybrid CFRP/Ti toward specific cutting energy and induced damage analyses

NASA Astrophysics Data System (ADS)

Xu, Jinyang; El Mansori, Mohamed

2016-10-01

This paper studied the machinability of hybrid CFRP/Ti stack via the numerical approach. To this aim, an original FE model consisting of three fundamental physical constituents, i.e., CFRP phase, interface and Ti phase, was established in the Abaqus Explicit/code to construct the machining behavior of the composite-to-metal alliance. The CFRP phase was modeled as an equivalent homogeneous material (EHM) by considering its anisotropic behavior relative to the fiber orientation (θ) while the Ti alloy phase was assumed to exhibit isotropic and elastic-plastic behavior. The "interface" linking the "CFRP-to-Ti" contact boundary was physically modeled as an intermediate transition region through the concept of cohesive zone (CZ). Different constitutive laws and damage criteria were implemented to simulate the chip separation process of the bi-material system. The key cutting responses including specific cutting energy consumption, induced subsurface damage, and interface delamination were precisely addressed via the comprehensive FE analyses, and several key conclusions were drawn from this study.

Simulating and Detecting Radiation-Induced Errors for Onboard Machine Learning

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Bornstein, Benjamin; Granat, Robert; Tang, Benyang; Turmon, Michael

2009-01-01

Spacecraft processors and memory are subjected to high radiation doses and therefore employ radiation-hardened components. However, these components are orders of magnitude more expensive than typical desktop components, and they lag years behind in terms of speed and size. We have integrated algorithm-based fault tolerance (ABFT) methods into onboard data analysis algorithms to detect radiation-induced errors, which ultimately may permit the use of spacecraft memory that need not be fully hardened, reducing cost and increasing capability at the same time. We have also developed a lightweight software radiation simulator, BITFLIPS, that permits evaluation of error detection strategies in a controlled fashion, including the specification of the radiation rate and selective exposure of individual data structures. Using BITFLIPS, we evaluated our error detection methods when using a support vector machine to analyze data collected by the Mars Odyssey spacecraft. We found ABFT error detection for matrix multiplication is very successful, while error detection for Gaussian kernel computation still has room for improvement.

Molecular Basis of Mechano-Signal Transduction in Vascular Endothelial Cells

NASA Technical Reports Server (NTRS)

Jo, Hanjoong

2004-01-01

Simulated microgravity studies using a random positioning machine (RPM). One RPM machine has been built for us by Fokker Science in Netherland. Using the device, we have developed an in vitro system to examine the effect of simulated microgravity on osteoblastic bone cells. Using this system, we have carried out gene chip studies to determine the gene expression profiles of osteoblasts cultured under simulated microgravity conditions in comparison to static controls. From this study, we have identified numerous genes, some of which are expected ones inducing bone loss, but many of which are unexpected and unknown. These findings are being prepared for publications.

Fabrication of mitigation pits for improving laser damage resistance in dielectric mirrors by femtosecond laser machining

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

Wolfe, Justin E.; Qiu, S. Roger; Stolz, Christopher J.

2011-03-20

Femtosecond laser machining is used to create mitigation pits to stabilize nanosecond laser-induced damage in multilayer dielectric mirror coatings on BK7 substrates. In this paper, we characterize features and the artifacts associated with mitigation pits and further investigate the impact of pulse energy and pulse duration on pit quality and damage resistance. Our results show that these mitigation features can double the fluence-handling capability of large-aperture optical multilayer mirror coatings and further demonstrate that femtosecond laser macromachining is a promising means for fabricating mitigation geometry in multilayer coatings to increase mirror performance under high-power laser irradiation.

Straight-bladed Darrieus wind turbines - A protagonist's view

NASA Astrophysics Data System (ADS)

Migliore, P. G.

The technology development and market penetration of Darrieus and propeller-type wind turbines is addressed. Important characteristics of competing configurations are compared, and it is claimed that aerodynamic efficiency is not a distinguishing feature. Advantages of the Darrieus machine include omni-directionality and self-limitation, but propeller types require less rotor length per unit swept area. It is argued that the straight-bladed Darrieus is much simpler than the curved-bladed and should be capable of comparable aerodynamic efficiency. Some of the problems of structural design, as well as blade induced drag losses and support-arm counter torque, diminish rapidly as machine size is increased. Taper ratio has similar beneficial effects.

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection.

PubMed

Nuryani, Nuryani; Ling, Steve S H; Nguyen, H T

2012-04-01

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.

Scattered radiation doses absorbed by technicians at different distances from X-ray exposure: Experiments on prosthesis.

PubMed

Chiang, Hsien-Wen; Liu, Ya-Ling; Chen, Tou-Rong; Chen, Chun-Lon; Chiang, Hsien-Jen; Chao, Shin-Yu

2015-01-01

This work aimed to investigate the spatial distribution of scattered radiation doses induced by exposure to the portable X-ray, the C-arm machine, and to simulate the radiologist without a shield of lead clothing, radiation doses absorbed by medical staff at 2 m from the central exposure point. With the adoption of the Rando Phantom, several frequently X-rayed body parts were exposed to X-ray radiation, and the scattered radiation doses were measured by ionization chamber dosimeters at various angles from the patient. Assuming that the central point of the X-ray was located at the belly button, five detection points were distributed in the operation room at 1 m above the ground and 1-2 m from the central point horizontally. The radiation dose measured at point B was the lowest, and the scattered radiation dose absorbed by the prosthesis from the X-ray's vertical projection was 0.07 ±0.03 μGy, which was less than the background radiation levels. The Fluke biomedical model 660-5DE (400 cc) and 660-3DE (4 cc) ion chambers were used to detect air dose at a distance of approximately two meters from the central point. The AP projection radiation doses at point B was the lowest (0.07±0.03 μGy) and the radiation doses at point D was the highest (0.26±0.08 μGy) .Only taking the vertical projection into account, the radiation doses at point B was the lowest (0.52 μGy), and the radiation doses at point E was the highest (4 μGy).The PA projection radiation at point B was the lowest (0.36 μGy) and the radiation doses at point E was the highest(2.77 μGy), occupying 10-32% of the maximum doses. The maximum dose in five directions was nine times to the minimum dose. When the PX and the C-arm machine were used, the radiation doses at a distance of 2 m were attenuated to the background radiation level. The radiologist without a lead shield should stand at point B of patient's feet. Accordingly, teaching materials on radiation safety for radiological interns and clinical technicians were formulated.

Detection of low level benzene exposure in supermarket wrappers by urinary muconic acid.

PubMed

E S Johnson S Halabi G Netto G Lucier W Bechtold R Henderson

1999-01-01

Women who use the 'hot wire' and 'cool rod' machines to wrap meat in supermarkets are potentially exposed to low levels of benzene and polycyclic aromatic hydrocarbons present in fumes emitted during the thermal decomposition of the plastic used to wrap meat. In order to evaluate whether the benzene metabolite trans, trans-muconic acid (MA) can be used to monitor these low levels, we collected urine samples from supermarket workers, and assayed the urine for MA. Geometric mean after-shift MA levels were highest for subjects who used the 'hot wire' machine, i.e. > 300 ng mg-1 creatinine (Cr). The corresponding levels for subjects who used the 'cool rod' machine were similar to those for subjects who did not use either type of machine, and were much lower. These results indicate that urinary muconic acid has some potential for use in monitoring benzene exposures of less than 1 part per million (ppm). The study detected very high background MA levels (exceeding 2000 ng mg-1 Cr) in some subjects, suggesting that individuals in the general population without occupational exposure to benzene may have urinary MA levels equivalent to exposure to up to 2 ppm benzene in ambient air. However, since non-benzene sources of the metabolite cannot be completely ruled out as partially responsible for these high levels, the public health significance of this finding is not known at the moment.

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

PubMed Central

Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

2017-01-01

Background To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient’s weight kept rising in the past year). This process becomes infeasible with limited budgets. Objective This study’s goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. Methods This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care management allocation and pilot one model with care managers; and (3) perform simulations to estimate the impact of adopting Auto-ML on US patient outcomes. Results We are currently writing Auto-ML’s design document. We intend to finish our study by around the year 2022. Conclusions Auto-ML will generalize to various clinical prediction/classification problems. With minimal help from data scientists, health care researchers can use Auto-ML to quickly build high-quality models. This will boost wider use of machine learning in health care and improve patient outcomes. PMID:28851678

Difficulty understanding speech in noise by the hearing impaired: underlying causes and technological solutions.

PubMed

Healy, Eric W; Yoho, Sarah E

2016-08-01

A primary complaint of hearing-impaired individuals involves poor speech understanding when background noise is present. Hearing aids and cochlear implants often allow good speech understanding in quiet backgrounds. But hearing-impaired individuals are highly noise intolerant, and existing devices are not very effective at combating background noise. As a result, speech understanding in noise is often quite poor. In accord with the significance of the problem, considerable effort has been expended toward understanding and remedying this issue. Fortunately, our understanding of the underlying issues is reasonably good. In sharp contrast, effective solutions have remained elusive. One solution that seems promising involves a single-microphone machine-learning algorithm to extract speech from background noise. Data from our group indicate that the algorithm is capable of producing vast increases in speech understanding by hearing-impaired individuals. This paper will first provide an overview of the speech-in-noise problem and outline why hearing-impaired individuals are so noise intolerant. An overview of our approach to solving this problem will follow.

Plasma Properties of Microwave Produced Plasma in a Toroidal Device

NASA Astrophysics Data System (ADS)

Singh, Ajay; Edwards, W. F.; Held, Eric

2011-10-01

We have modified a small tokamak, STOR-1M, on loan from University of Saskatchewan, to operate as a low-temperature (~5 eV) toroidal plasma machine with externally induced toroidal magnetic fields ranging from zero to ~50 G. The plasma is produced using microwave discharges at relatively high pressures. Microwaves are produced by a kitchen microwave-oven magnetron operating at 2.45 GHz in continuous operating mode, resulting in pulses ~0.5 s in duration. Initial measurements of plasma formation in this device with and without applied magnetic fields are presented. Plasma density and temperature profiles have been measured using Langmuir probes and the magnetic field profile inside the plasma has been obtained using Hall probes. When the discharge is created with no applied toroidal magnetic field, the plasma does not fill the entire torus due to high background pressure. However, when a toroidal magnetic field is applied, the plasma flows along the applied field, filling the torus. Increasing the applied magnetic field seems to aid plasma formation - the peak density increases and the density gradient becomes steeper. Above a threshold magnetic field, the plasma develops low-frequency density oscillations due to probable excitation of flute modes in the plasma.

System technology for laser-assisted milling with tool integrated optics

NASA Astrophysics Data System (ADS)

Hermani, Jan-Patrick; Emonts, Michael; Brecher, Christian

2013-02-01

High strength metal alloys and ceramics offer a huge potential for increased efficiency (e. g. in engine components for aerospace or components for gas turbines). However, mass application is still hampered by cost- and time-consuming end-machining due to long processing times and high tool wear. Laser-induced heating shortly before machining can reduce the material strength and improve machinability significantly. The Fraunhofer IPT has developed and successfully realized a new approach for laser-assisted milling with spindle and tool integrated, co-rotating optics. The novel optical system inside the tool consists of one deflection prism to position the laser spot in front of the cutting insert and one focusing lens. Using a fiber laser with high beam quality the laser spot diameter can be precisely adjusted to the chip size. A high dynamic adaption of the laser power signal according to the engagement condition of the cutting tool was realized in order not to irradiate already machined work piece material. During the tool engagement the laser power is controlled in proportion to the current material removal rate, which has to be calculated continuously. The needed geometric values are generated by a CAD/CAM program and converted into a laser power signal by a real-time controller. The developed milling tool with integrated optics and the algorithm for laser power control enable a multi-axis laser-assisted machining of complex parts.

Acceptability of Using Electronic Vending Machines to Deliver Oral Rapid HIV Self-Testing Kits: A Qualitative Study

PubMed Central

Young, Sean D.; Daniels, Joseph; Chiu, ChingChe J.; Bolan, Robert K.; Flynn, Risa P.; Kwok, Justin; Klausner, Jeffrey D.

2014-01-01

Introduction Rates of unrecognized HIV infection are significantly higher among Latino and Black men who have sex with men (MSM). Policy makers have proposed that HIV self-testing kits and new methods for delivering self-testing could improve testing uptake among minority MSM. This study sought to conduct qualitative assessments with MSM of color to determine the acceptability of using electronic vending machines to dispense HIV self-testing kits. Materials and Methods African American and Latino MSM were recruited using a participant pool from an existing HIV prevention trial on Facebook. If participants expressed interest in using a vending machine to receive an HIV self-testing kit, they were emailed a 4-digit personal identification number (PIN) code to retrieve the test from the machine. We followed up with those who had tested to assess their willingness to participate in an interview about their experience. Results Twelve kits were dispensed and 8 interviews were conducted. In general, participants expressed that the vending machine was an acceptable HIV test delivery method due to its novelty and convenience. Discussion Acceptability of this delivery model for HIV testing kits was closely associated with three main factors: credibility, confidentiality, and convenience. Future research is needed to address issues, such as user-induced errors and costs, before scaling up the dispensing method. PMID:25076208

Theory-Based Causal Induction

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2009-01-01

Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…

Angular approach combined to mechanical model for tool breakage detection by eddy current sensors

NASA Astrophysics Data System (ADS)

Ritou, M.; Garnier, S.; Furet, B.; Hascoet, J. Y.

2014-02-01

The paper presents a new complete approach for Tool Condition Monitoring (TCM) in milling. The aim is the early detection of small damages so that catastrophic tool failures are prevented. A versatile in-process monitoring system is introduced for reliability concerns. The tool condition is determined by estimates of the radial eccentricity of the teeth. An adequate criterion is proposed combining mechanical model of milling and angular approach.Then, a new solution is proposed for the estimate of cutting force using eddy current sensors implemented close to spindle nose. Signals are analysed in the angular domain, notably by synchronous averaging technique. Phase shifts induced by changes of machining direction are compensated. Results are compared with cutting forces measured with a dynamometer table.The proposed method is implemented in an industrial case of pocket machining operation. One of the cutting edges has been slightly damaged during the machining, as shown by a direct measurement of the tool. A control chart is established with the estimates of cutter eccentricity obtained during the machining from the eddy current sensors signals. Efficiency and reliability of the method is demonstrated by a successful detection of the damage.

Induced activation studies for the LHC upgrade to High Luminosity LHC

NASA Astrophysics Data System (ADS)

Adorisio, C.; Roesler, S.

2018-06-01

The Large Hadron Collider (LHC) will be upgraded in 2019/2020 to increase its luminosity (rate of collisions) by a factor of five beyond its design value and the integrated luminosity by a factor ten, in order to maintain scientific progress and exploit its full capacity. The novel machine configuration, called High Luminosity LHC (HL-LHC), will increase consequently the level of activation of its components. The evaluation of the radiological impact of the HL-LHC operation in the Long Straight Sections of the Insertion Region 1 (ATLAS) and Insertion Region 5 (CMS) is presented. Using the Monte Carlo code FLUKA, ambient dose equivalent rate estimations have been performed on the basis of two announced operating scenarios and using the latest available machine layout. The HL-LHC project requires new technical infrastructure with caverns and 300 m long tunnels along the Insertion Regions 1 and 5. The new underground service galleries will be accessible during the operation of the accelerator machine. The radiological risk assessment for the Civil Engineering work foreseen to start excavating the new galleries in the next LHC Long Shutdown and the radiological impact of the machine operation will be discussed.

A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms.

PubMed

Goodswen, Stephen J; Kennedy, Paul J; Ellis, John T

2013-11-02

An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets. The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally. Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory.

Collective Effects in a Diffraction Limited Storage Ring

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

Nagaoka, Ryutaro; Bane, Karl L.F.

Our paper gives an overview of collective effects that are likely to appear and possibly limit the performance in a diffraction-limited storage ring (DLSR) that stores a high-intensity ultra-low-emittance beam. Beam instabilities and other intensity-dependent effects that may significantly impact the machine performance are covered. The latter include beam-induced machine heating, Touschek scattering, intra-beam scattering, as well as incoherent tune shifts. The general trend that the efforts to achieve ultra-low emittance result in increasing the machine coupling impedance and the beam sensitivity to instability is reviewed. The nature of coupling impedance in a DLSR is described, followed by a seriesmore » of potentially dangerous beam instabilities driven by the former, such as resistive-wall, TMCI (transverse mode coupling instability), head-tail and microwave instabilities. Additionally, beam-ion and CSR (coherent synchrotron radiation) instabilities are also treated. Means to fight against collective effects such as lengthening of the bunch with passive harmonic cavities and bunch-by-bunch transverse feedback are introduced. Numerical codes developed and used to evaluate the machine coupling impedance, as well as to simulate beam instability using the former as inputs are described.« less

Expected load spectra of prototype Francis turbines in low-load operation using numerical simulations and site measurements

NASA Astrophysics Data System (ADS)

Eichhorn, M.; Taruffi, A.; Bauer, C.

2017-04-01

The operators of hydropower plants are forced to extend the existing operating ranges of their hydraulic machines to remain competitive on the energy market due to the rising amount of wind and solar power. Faster response times and a higher flexibility towards part- and low-load conditions enable a better electric grid control and assure therefore an economic operation of the power plant. The occurring disadvantage is a higher dynamic excitation of affected machine components, especially Francis turbine runners, due to pressure pulsations induced by unsteady flow phenomena (e.g. draft tube vortex ropes). Therefore, fatigue analysis becomes more important even in the design phase of the hydraulic machines to evaluate the static and dynamic load in different operating conditions and to reduce maintenance costs. An approach including a one-way coupled fluid-structure interaction has been already developed using unsteady CFD simulations and transient FEM computations. This is now applied on two Francis turbines with different specific speeds and power ranges, to obtain the load spectra of both machines. The results are compared to strain gauge measurements on the according Francis turbines to validate the overall procedure.

Development of a machine learning potential for graphene

NASA Astrophysics Data System (ADS)

Rowe, Patrick; Csányi, Gábor; Alfè, Dario; Michaelides, Angelos

2018-02-01

We present an accurate interatomic potential for graphene, constructed using the Gaussian approximation potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface, facilitating highly accurate (approaching the accuracy of ab initio methods) molecular dynamics simulations. This is achieved at a computational cost which is orders of magnitude lower than that of comparable calculations which directly invoke electronic structure methods. We evaluate the accuracy of our machine learning model alongside that of a number of popular empirical and bond-order potentials, using both experimental and ab initio data as references. We find that whilst significant discrepancies exist between the empirical interatomic potentials and the reference data—and amongst the empirical potentials themselves—the machine learning model introduced here provides exemplary performance in all of the tested areas. The calculated properties include: graphene phonon dispersion curves at 0 K (which we predict with sub-meV accuracy), phonon spectra at finite temperature, in-plane thermal expansion up to 2500 K as compared to NPT ab initio molecular dynamics simulations and a comparison of the thermally induced dispersion of graphene Raman bands to experimental observations. We have made our potential freely available online at [http://www.libatoms.org].

Collective Effects in a Diffraction Limited Storage Ring

DOE PAGES

Nagaoka, Ryutaro; Bane, Karl L.F.

2015-10-20

Our paper gives an overview of collective effects that are likely to appear and possibly limit the performance in a diffraction-limited storage ring (DLSR) that stores a high-intensity ultra-low-emittance beam. Beam instabilities and other intensity-dependent effects that may significantly impact the machine performance are covered. The latter include beam-induced machine heating, Touschek scattering, intra-beam scattering, as well as incoherent tune shifts. The general trend that the efforts to achieve ultra-low emittance result in increasing the machine coupling impedance and the beam sensitivity to instability is reviewed. The nature of coupling impedance in a DLSR is described, followed by a seriesmore » of potentially dangerous beam instabilities driven by the former, such as resistive-wall, TMCI (transverse mode coupling instability), head-tail and microwave instabilities. Additionally, beam-ion and CSR (coherent synchrotron radiation) instabilities are also treated. Means to fight against collective effects such as lengthening of the bunch with passive harmonic cavities and bunch-by-bunch transverse feedback are introduced. Numerical codes developed and used to evaluate the machine coupling impedance, as well as to simulate beam instability using the former as inputs are described.« less

Ultrasonic Device Would Open Pipe Bombs

NASA Technical Reports Server (NTRS)

El-Raheb, Michael S.; Adams, Marc A.; Zwissler, James G.

1991-01-01

Piezoelectric ultrasonic transducer, energized by frequency generator and power supply, vibrates shell of pipe bomb while hardly disturbing explosive inner material. Frequency-control circuitry senses resonance in shell and holds generator at that frequency to induce fatigue cracking in threads of end cap. In addition to disarming bombs, ultrasonically induced fatigue may have other applications. In manufacturing, replaces some machining and cutting operations. In repair of equipment, cleanly and quickly disassembles corroded parts. In demolition of buildings used to dismember steel framework safely and controllably.

Development of classification models to detect Salmonella Enteritidis and Salmonella Typhimurium found in poultry carcass rinses by visible-near infrared hyperspectral imaging

NASA Astrophysics Data System (ADS)

Seo, Young Wook; Yoon, Seung Chul; Park, Bosoon; Hinton, Arthur; Windham, William R.; Lawrence, Kurt C.

2013-05-01

Salmonella is a major cause of foodborne disease outbreaks resulting from the consumption of contaminated food products in the United States. This paper reports the development of a hyperspectral imaging technique for detecting and differentiating two of the most common Salmonella serotypes, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), from background microflora that are often found in poultry carcass rinse. Presumptive positive screening of colonies with a traditional direct plating method is a labor intensive and time consuming task. Thus, this paper is concerned with the detection of differences in spectral characteristics among the pure SE, ST, and background microflora grown on brilliant green sulfa (BGS) and xylose lysine tergitol 4 (XLT4) agar media with a spread plating technique. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora. A total of 10 classification models, including five machine learning algorithms, each without and with principal component analysis (PCA), were validated and compared to find the best model in classification accuracy. The five machine learning (classification) algorithms used in this study were Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). The average classification accuracy of all 10 models on a calibration (or training) set of the pure cultures on BGS agar plates was 98% (Kappa coefficient = 0.95) in determining the presence of SE and/or ST although it was difficult to differentiate between SE and ST. The average classification accuracy of all 10 models on a training set for ST detection on XLT4 agar was over 99% (Kappa coefficient = 0.99) although SE colonies on XLT4 agar were difficult to differentiate from background microflora. The average classification accuracy of all 10 models on a validation set of chicken carcass rinses spiked with SE or ST and incubated on BGS agar plates was 94.45% and 83.73%, without and with PCA for classification, respectively. The best performing classification model on the validation set was QDA without PCA by achieving the classification accuracy of 98.65% (Kappa coefficient=0.98). The overall best performing classification model regardless of using PCA was MD with the classification accuracy of 94.84% (Kappa coefficient=0.88) on the validation set.

A Study of Crowd Ability and its Influence on Crowdsourced Evaluation of Design Concepts

DTIC Science & Technology

2014-05-01

identifies the experts from the crowd, under the assumptions that ( 1 ) experts do exist and (2) only experts have consistent evaluations. These assumptions...for design evaluation tasks . Keywords: crowdsourcing, design evaluation, sparse evaluation ability, machine learning ∗Corresponding author. 1 ...intelligence” of a much larger crowd of people with diverse backgrounds [ 1 ]. Crowdsourced evaluation, or the delegation of an eval- uation task to a

Radiological and microwave Protection at NRL, January - December 1983

DTIC Science & Technology

1984-06-27

reduced to background. 18 Surveys with TLD badges were made on pulsed electron beam machines in Buildings 101 and A68 throughout the year. The Gamble...calibration of radiation dosimetry systems required by the Laboratory’s radiological safety program, or by other Laboratory or Navy groups. The Section...provides consultation and assistance on dosimetry problems to the Staff, Laboratory, and Navy. The Section maintains and calibrates fixed-field radiac

Event detection for car park entries by video-surveillance

NASA Astrophysics Data System (ADS)

Coquin, Didier; Tailland, Johan; Cintract, Michel

2007-10-01

Intelligent surveillance has become an important research issue due to the high cost and low efficiency of human supervisors, and machine intelligence is required to provide a solution for automated event detection. In this paper we describe a real-time system that has been used for detecting car park entries, using an adaptive background learning algorithm and two indicators representing activity and identity to overcome the difficulty of tracking objects.

Conditions for coherent-synchrotron-radiation-induced microbunching suppression in multibend beam transport or recirculation arcs

NASA Astrophysics Data System (ADS)

Tsai, C.-Y.; Di Mitri, S.; Douglas, D.; Li, R.; Tennant, C.

2017-02-01

The coherent synchrotron radiation (CSR) of a high-brightness electron beam traversing a series of dipoles, such as transport or recirculation arcs, may result in beam phase space degradation. On one hand, CSR can perturb electron transverse motion in dispersive regions along the beam line and possibly cause emittance growth. On the other hand, the CSR effect on the longitudinal beam dynamics could result in microbunching instability. For transport arcs, several schemes have been proposed to suppress the CSR-induced emittance growth. Correspondingly, a few scenarios have been introduced to suppress CSR-induced microbunching instability, which however mostly aim for linac-based machines. In this paper we provide sufficient conditions for suppression of CSR-induced microbunching instability along transport or recirculation arcs. Examples are presented with the relevant microbunching analyses carried out by our developed semianalytical Vlasov solver [C.-Y. Tsai, D. Douglas, R. Li, and C. Tennant, Linear microbunching analysis for recirculation machines, Phys. Rev. ST Accel. Beams 19, 114401 (2016), 10.1103/PhysRevAccelBeams.19.114401]. The example lattices include low-energy (˜100 MeV ) and high-energy (˜1 GeV ) recirculation arcs, and medium-energy compressor arcs. Our studies show that lattices satisfying the proposed conditions indeed have microbunching gain suppressed. Beam current dependences of maximal CSR microbunching gains are also demonstrated, which should help outline a beam line design for different scales of nominal currents. We expect this analysis can shed light on the lattice design approach that aims to control the CSR-induced microbunching.

How State Taxes and Policies Targeting Soda Consumption Modify the Association between School Vending Machines and Student Dietary Behaviors: A Cross-Sectional Analysis

PubMed Central

Taber, Daniel R.; Chriqui, Jamie F.; Vuillaume, Renee; Chaloupka, Frank J.

2014-01-01

Background Sodas are widely sold in vending machines and other school venues in the United States, particularly in high school. Research suggests that policy changes have reduced soda access, but the impact of reduced access on consumption is unclear. This study was designed to identify student, environmental, or policy characteristics that modify the associations between school vending machines and student dietary behaviors. Methods Data on school vending machine access and student diet were obtained as part of the National Youth Physical Activity and Nutrition Study (NYPANS) and linked to state-level data on soda taxes, restaurant taxes, and state laws governing the sale of soda in schools. Regression models were used to: 1) estimate associations between vending machine access and soda consumption, fast food consumption, and lunch source, and 2) determine if associations were modified by state soda taxes, restaurant taxes, laws banning in-school soda sales, or student characteristics (race/ethnicity, sex, home food access, weight loss behaviors.) Results Contrary to the hypothesis, students tended to consume 0.53 fewer servings of soda/week (95% CI: -1.17, 0.11) and consume fast food on 0.24 fewer days/week (95% CI: -0.44, -0.05) if they had in-school access to vending machines. They were also less likely to consume soda daily (23.9% vs. 27.9%, average difference = -4.02, 95% CI: -7.28, -0.76). However, these inverse associations were observed primarily among states with lower soda and restaurant tax rates (relative to general food tax rates) and states that did not ban in-school soda sales. Associations did not vary by any student characteristics except for weight loss behaviors. Conclusion Isolated changes to the school food environment may have unintended consequences unless policymakers incorporate other initiatives designed to discourage overall soda consumption. PMID:25083906

Interactive QR code beautification with full background image embedding

NASA Astrophysics Data System (ADS)

Lin, Lijian; Wu, Song; Liu, Sijiang; Jiang, Bo

2017-06-01

QR (Quick Response) code is a kind of two dimensional barcode that was first developed in automotive industry. Nowadays, QR code has been widely used in commercial applications like product promotion, mobile payment, product information management, etc. Traditional QR codes in accordance with the international standard are reliable and fast to decode, but are lack of aesthetic appearance to demonstrate visual information to customers. In this work, we present a novel interactive method to generate aesthetic QR code. By given information to be encoded and an image to be decorated as full QR code background, our method accepts interactive user's strokes as hints to remove undesired parts of QR code modules based on the support of QR code error correction mechanism and background color thresholds. Compared to previous approaches, our method follows the intention of the QR code designer, thus can achieve more user pleasant result, while keeping high machine readability.

Healthier choices in an Australian health service: a pre-post audit of an intervention to improve the nutritional value of foods and drinks in vending machines and food outlets

PubMed Central

2013-01-01

Background Vending machines and shops located within health care facilities are a source of food and drinks for staff, visitors and outpatients and they have the potential to promote healthy food and drink choices. This paper describes perceptions of parents and managers of health-service located food outlets towards the availability and labelling of healthier food options and the food and drinks offered for sale in health care facilities in Australia. It also describes the impact of an intervention to improve availability and labelling of healthier foods and drinks for sale. Methods Parents (n = 168) and food outlet managers (n = 17) were surveyed. Food and drinks for sale in health-service operated food outlets (n = 5) and vending machines (n = 90) in health care facilities in the Hunter New England region of NSW were audited pre (2007) and post (2010/11) the introduction of policy and associated support to increase the availability of healthier choices. A traffic light system was used to classify foods from least (red) to most healthy choices (green). Results Almost all (95%) parents and most (65%) food outlet managers thought food outlets on health service sites should have signs clearly showing healthy choices. Parents (90%) also thought all food outlets on health service sites should provide mostly healthy items compared to 47% of managers. The proportion of healthier beverage slots in vending machines increased from 29% to 51% at follow-up and the proportion of machines that labelled healthier drinks increased from 0 to 26%. No outlets labelled healthier items at baseline compared to 4 out of 5 after the intervention. No changes were observed in the availability or labelling of healthier food in vending machines or the availability of healthier food or drinks in food outlets. Conclusions Baseline availability and labelling of healthier food and beverage choices for sale in health care facilities was poor in spite of the support of parents and outlet managers for such initiatives. The intervention encouraged improvements in the availability and labelling of healthier drinks but not foods in vending machines. PMID:24274916

Toward FRP-Based Brain-Machine Interfaces—Single-Trial Classification of Fixation-Related Potentials

PubMed Central

Finke, Andrea; Essig, Kai; Marchioro, Giuseppe; Ritter, Helge

2016-01-01

The co-registration of eye tracking and electroencephalography provides a holistic measure of ongoing cognitive processes. Recently, fixation-related potentials have been introduced to quantify the neural activity in such bi-modal recordings. Fixation-related potentials are time-locked to fixation onsets, just like event-related potentials are locked to stimulus onsets. Compared to existing electroencephalography-based brain-machine interfaces that depend on visual stimuli, fixation-related potentials have the advantages that they can be used in free, unconstrained viewing conditions and can also be classified on a single-trial level. Thus, fixation-related potentials have the potential to allow for conceptually different brain-machine interfaces that directly interpret cortical activity related to the visual processing of specific objects. However, existing research has investigated fixation-related potentials only with very restricted and highly unnatural stimuli in simple search tasks while participant’s body movements were restricted. We present a study where we relieved many of these restrictions while retaining some control by using a gaze-contingent visual search task. In our study, participants had to find a target object out of 12 complex and everyday objects presented on a screen while the electrical activity of the brain and eye movements were recorded simultaneously. Our results show that our proposed method for the classification of fixation-related potentials can clearly discriminate between fixations on relevant, non-relevant and background areas. Furthermore, we show that our classification approach generalizes not only to different test sets from the same participant, but also across participants. These results promise to open novel avenues for exploiting fixation-related potentials in electroencephalography-based brain-machine interfaces and thus providing a novel means for intuitive human-machine interaction. PMID:26812487

Optimization of Coolant Technique Conditions for Machining A319 Aluminium Alloy Using Response Surface Method (RSM)

NASA Astrophysics Data System (ADS)

Zainal Ariffin, S.; Razlan, A.; Ali, M. Mohd; Efendee, A. M.; Rahman, M. M.

2018-03-01

Background/Objectives: The paper discusses about the optimum cutting parameters with coolant techniques condition (1.0 mm nozzle orifice, wet and dry) to optimize surface roughness, temperature and tool wear in the machining process based on the selected setting parameters. The selected cutting parameters for this study were the cutting speed, feed rate, depth of cut and coolant techniques condition. Methods/Statistical Analysis Experiments were conducted and investigated based on Design of Experiment (DOE) with Response Surface Method. The research of the aggressive machining process on aluminum alloy (A319) for automotive applications is an effort to understand the machining concept, which widely used in a variety of manufacturing industries especially in the automotive industry. Findings: The results show that the dominant failure mode is the surface roughness, temperature and tool wear when using 1.0 mm nozzle orifice, increases during machining and also can be alternative minimize built up edge of the A319. The exploration for surface roughness, productivity and the optimization of cutting speed in the technical and commercial aspects of the manufacturing processes of A319 are discussed in automotive components industries for further work Applications/Improvements: The research result also beneficial in minimizing the costs incurred and improving productivity of manufacturing firms. According to the mathematical model and equations, generated by CCD based RSM, experiments were performed and cutting coolant condition technique using size nozzle can reduces tool wear, surface roughness and temperature was obtained. Results have been analyzed and optimization has been carried out for selecting cutting parameters, shows that the effectiveness and efficiency of the system can be identified and helps to solve potential problems.

Impact of an engineering design-based curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines

NASA Astrophysics Data System (ADS)

Marulcu, Ismail; Barnett, Michael

2016-01-01

Background: Elementary Science Education is struggling with multiple challenges. National and State test results confirm the need for deeper understanding in elementary science education. Moreover, national policy statements and researchers call for increased exposure to engineering and technology in elementary science education. The basic motivation of this study is to suggest a solution to both improving elementary science education and increasing exposure to engineering and technology in it. Purpose/Hypothesis: This mixed-method study examined the impact of an engineering design-based curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines. We hypothesize that the LEGO-engineering design unit is as successful as the inquiry-based unit in terms of students' science content learning of simple machines. Design/Method: We used a mixed-methods approach to investigate our research questions; we compared the control and the experimental groups' scores from the tests and interviews by using Analysis of Covariance (ANCOVA) and compared each group's pre- and post-scores by using paired t-tests. Results: Our findings from the paired t-tests show that both the experimental and comparison groups significantly improved their scores from the pre-test to post-test on the multiple-choice, open-ended, and interview items. Moreover, ANCOVA results show that students in the experimental group, who learned simple machines with the design-based unit, performed significantly better on the interview questions. Conclusions: Our analyses revealed that the design-based Design a people mover: Simple machines unit was, if not better, as successful as the inquiry-based FOSS Levers and pulleys unit in terms of students' science content learning.

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

Benchmark studies of induced radioactivity produced in LHC materials, Part I: Specific activities.

PubMed

Brugger, M; Khater, H; Mayer, S; Prinz, A; Roesler, S; Ulrici, L; Vincke, H

2005-01-01

Samples of materials which will be used in the LHC machine for shielding and construction components were irradiated in the stray radiation field of the CERN-EU high-energy reference field facility. After irradiation, the specific activities induced in the various samples were analysed with a high-precision gamma spectrometer at various cooling times, allowing identification of isotopes with a wide range of half-lives. Furthermore, the irradiation experiment was simulated in detail with the FLUKA Monte Carlo code. A comparison of measured and calculated specific activities shows good agreement, supporting the use of FLUKA for estimating the level of induced activity in the LHC.

Improving Satellite Observation Utilization for Model Initialization with Machine Learning: An Introduction and Tackling the "Labeled Dataset" Challenge for Cyclones Around the World

NASA Astrophysics Data System (ADS)

Bonfanti, C. E.; Stewart, J.; Lee, Y. J.; Govett, M.; Trailovic, L.; Etherton, B.

2017-12-01

One of the National Oceanic and Atmospheric Administration (NOAA) goals is to provide timely and reliable weather forecasts to support important decisions when and where people need it for safety, emergencies, planning for day-to-day activities. Satellite data is essential for areas lacking in-situ observations for use as initial conditions in Numerical Weather Prediction (NWP) Models, such as spans of the ocean or remote areas of land. Currently only about 7% of total received satellite data is selected for use and from that, an even smaller percentage ever are assimilated into NWP models. With machine learning, the computational and time costs needed for satellite data selection can be greatly reduced. We study various machine learning approaches to process orders of magnitude more satellite data in significantly less time allowing for a greater quantity and more intelligent selection of data to be used for assimilation purposes. Given the future launches of satellites in the upcoming years, machine learning is capable of being applied for better selection of Regions of Interest (ROI) in the magnitudes more of satellite data that will be received. This paper discusses the background of machine learning methods as applied to weather forecasting and the challenges of creating a "labeled dataset" for training and testing purposes. In the training stage of supervised machine learning, labeled data are important to identify a ROI as either true or false so that the model knows what signatures in satellite data to identify. Authors have selected cyclones, including tropical cyclones and mid-latitude lows, as ROI for their machine learning purposes and created a labeled dataset of true or false for ROI from Global Forecast System (GFS) reanalysis data. A dataset like this does not yet exist and given the need for a high quantity of samples, is was decided this was best done with automation. This process was done by developing a program similar to the National Center for Environmental Prediction (NCEP) tropical cyclone tracker by Marchok that was used to identify cyclones based off its physical characteristics. We will discuss the methods and challenges to creating this dataset and the dataset's use for our current supervised machine learning model as well as use for future work on events such as convection initiation.

Noise radiation characteristics of the Westinghouse WWG-0600 (600kW) wind turbine generator

NASA Technical Reports Server (NTRS)

Shepherd, Kevin P.; Hubbard, Harvey H.

1989-01-01

Acoustic data are presented from five different WWG-0600 machines for the wind speed range 6.7 to 13.4 m/s, for a power output range of 51 to 600 kW and for upwind, downwind and crosswind locations. Both broadband and narrowband data are presented and are compared with calculations and with similar data from other machines. Predicted broadband spectra are in good agreement with measurements at high power and underestimate them at low power. Discrete frequency rotational noise components are present in all measurements and are believed due to terrain induced wind gradients. Predictions are in general agreement with measurements upwind and downwind but underestimate them in the crosswind direction.

A machine independent expert system for diagnosing environmentally induced spacecraft anomalies

NASA Technical Reports Server (NTRS)

Rolincik, Mark J.

1991-01-01

A new rule-based, machine independent analytical tool for diagnosing spacecraft anomalies, the EnviroNET expert system, was developed. Expert systems provide an effective method for storing knowledge, allow computers to sift through large amounts of data pinpointing significant parts, and most importantly, use heuristics in addition to algorithms which allow approximate reasoning and inference, and the ability to attack problems not rigidly defines. The EviroNET expert system knowledge base currently contains over two hundred rules, and links to databases which include past environmental data, satellite data, and previous known anomalies. The environmental causes considered are bulk charging, single event upsets (SEU), surface charging, and total radiation dose.

Characterization of Subsurface Defects in Ceramic Rods by Laser Scattering and Fractography

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

Zhang, J. M.; Sun, J. G.; Andrews, M. J.

2006-03-06

Silicon nitride ceramics are leading materials being evaluated for valve train components in diesel engine applications. The surface and subsurface defects and damage induced by surface machining can significantly affect component strength and lifetime. In this study, a nondestructive evaluation (NDE) technique based upon laser scattering has been utilized to analyze eight transversely ground silicon nitride cylindrical rods before fracture tests. The fracture origins (machining cracks or material-inherent flaws) identified by fractography after fracture testing were correlated with laser scattering images. The results indicate that laser scattering is able to identify possible fracture origin in the silicon nitride subsurface withoutmore » the need for destructive fracture tests.« less

The EPIC-MOS Particle-Induced Background Spectrum

NASA Technical Reports Server (NTRS)

Kuntz, K. D.; Snowden, S. L.

2006-01-01

We have developed a method for constructing a spectrum of the particle-induced instrumental background of the XMM-Newton EPIC MOS detectors that can be used for observations of the diffuse background and extended sources that fill a significant fraction of the instrument field of view. The strength and spectrum of the particle-induced background, that is, the background due to the interaction of particles with the detector and the detector surroundings, is temporally variable as well as spatially variable over individual chips. Our method uses a combination of the filter-wheel-closed data and a database of unexposed-region data to construct a spectrum of the "quiescent" background. We show that, using this method of background subtraction, the differences between independent observations of the same region of "blank sky" are consistent with the statistical uncertainties except when there is clear evidence of solar wind charge exchange emission. We use the blank sky observations to show that contamination by SWCX emission is a strong function of the solar wind proton flux, and that observations through the flanks of the magnetosheath appear to be contaminated only at much higher solar wind fluxes. We have also developed a spectral model of the residual soft proton flares, which allows their effects to be removed to a substantial degree during spectral fitting.

Measurement-induced operation of two-ion quantum heat machines

NASA Astrophysics Data System (ADS)

Chand, Suman; Biswas, Asoka

2017-03-01

We show how one can implement a quantum heat machine by using two interacting trapped ions, in presence of a thermal bath. The electronic states of the ions act like a working substance, while the vibrational mode is modelled as the cold bath. The heat exchange with the cold bath is mimicked by the projective measurement of the electronic states. We show how such measurement in a suitable basis can lead to either a quantum heat engine or a refrigerator, which undergoes a quantum Otto cycle. The local magnetic field is adiabatically changed during the heat cycle. The performance of the heat machine depends upon the interaction strength between the ions, the magnetic fields, and the measurement cost. In our model, the coupling to the hot and the cold baths is never switched off in an alternative fashion during the heat cycle, unlike other existing proposals of quantum heat engines. This makes our proposal experimentally realizable using current tapped-ion technology.

Measurement-induced operation of two-ion quantum heat machines.

PubMed

Chand, Suman; Biswas, Asoka

2017-03-01

We show how one can implement a quantum heat machine by using two interacting trapped ions, in presence of a thermal bath. The electronic states of the ions act like a working substance, while the vibrational mode is modelled as the cold bath. The heat exchange with the cold bath is mimicked by the projective measurement of the electronic states. We show how such measurement in a suitable basis can lead to either a quantum heat engine or a refrigerator, which undergoes a quantum Otto cycle. The local magnetic field is adiabatically changed during the heat cycle. The performance of the heat machine depends upon the interaction strength between the ions, the magnetic fields, and the measurement cost. In our model, the coupling to the hot and the cold baths is never switched off in an alternative fashion during the heat cycle, unlike other existing proposals of quantum heat engines. This makes our proposal experimentally realizable using current tapped-ion technology.

A self-contained quantum harmonic engine

NASA Astrophysics Data System (ADS)

Reid, B.; Pigeon, S.; Antezza, M.; De Chiara, G.

2017-12-01

We propose a system made of three quantum harmonic oscillators as a compact quantum engine for producing mechanical work. The three oscillators play respectively the role of the hot bath, the working medium and the cold bath. The working medium performs an Otto cycle during which its frequency is changed and it is sequentially coupled to each of the two other oscillators. As the two environments are finite, the lifetime of the machine is finite and after a number of cycles it stops working and needs to be reset. Remarkably, we show that this machine can extract more than 90% of the available energy during 70 cycles. Differently from usually investigated infinite-reservoir configurations, this machine allows the protection of induced quantum correlations and we analyse the entanglement and quantum discord generated during the strokes. Interestingly, we show that high work generation is always accompanied by large quantum correlations. Our predictions can be useful for energy management at the nanoscale, and can be relevant for experiments with trapped ions and experiments with light in integrated optical circuits.

Exploring the potential of machine learning to break deadlock in convection parameterization

NASA Astrophysics Data System (ADS)

Pritchard, M. S.; Gentine, P.

2017-12-01

We explore the potential of modern machine learning tools (via TensorFlow) to replace parameterization of deep convection in climate models. Our strategy begins by generating a large ( 1 Tb) training dataset from time-step level (30-min) output harvested from a one-year integration of a zonally symmetric, uniform-SST aquaplanet integration of the SuperParameterized Community Atmosphere Model (SPCAM). We harvest the inputs and outputs connecting each of SPCAM's 8,192 embedded cloud-resolving model (CRM) arrays to its host climate model's arterial thermodynamic state variables to afford 143M independent training instances. We demonstrate that this dataset is sufficiently large to induce preliminary convergence for neural network prediction of desired outputs of SP, i.e. CRM-mean convective heating and moistening profiles. Sensitivity of the machine learning convergence to the nuances of the TensorFlow implementation are discussed, as well as results from pilot tests from the neural network operating inline within the SPCAM as a replacement to the (super)parameterization of convection.

Time dynamics of burst-train filamentation assisted femtosecond laser machining in glasses.

PubMed

Esser, Dagmar; Rezaei, Saeid; Li, Jianzhao; Herman, Peter R; Gottmann, Jens

2011-12-05

Bursts of femtosecond laser pulses with a repetition rate of f = 38.5MHz were created using a purpose-built optical resonator. Single Ti:Sapphire laser pulses, trapped inside a resonator and released into controllable burst profiles by computer generated trigger delays to a fast Pockels cell switch, drove filamentation-assisted laser machining of high aspect ratio holes deep into transparent glasses. The time dynamics of the hole formation and ablation plume physics on 2-ns to 400-ms time scales were examined in time-resolved side-view images recorded with an intensified-CCD camera during the laser machining process. Transient effects of photoluminescence and ablation plume emissions confirm the build-up of heat accumulation effects during the burst train, the formation of laser-generated filaments and plume-shielding effects inside the deeply etched vias. The small time interval between the pulses in the present burst train enabled a more gentle modification in the laser interaction volume that mitigated shock-induced microcracks compared with single pulses.

The potential of latent semantic analysis for machine grading of clinical case summaries.

PubMed

Kintsch, Walter

2002-02-01

This paper introduces latent semantic analysis (LSA), a machine learning method for representing the meaning of words, sentences, and texts. LSA induces a high-dimensional semantic space from reading a very large amount of texts. The meaning of words and texts can be represented as vectors in this space and hence can be compared automatically and objectively. A generative theory of the mental lexicon based on LSA is described. The word vectors LSA constructs are context free, and each word, irrespective of how many meanings or senses it has, is represented by a single vector. However, when a word is used in different contexts, context appropriate word senses emerge. Several applications of LSA to educational software are described, involving the ability of LSA to quickly compare the content of texts, such as an essay written by a student and a target essay. An LSA-based software tool is sketched for machine grading of clinical case summaries written by medical students.

Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification.

PubMed

Wang, Hsin-Wei; Lin, Ya-Chi; Pai, Tun-Wen; Chang, Hao-Teng

2011-01-01

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).

Integrating Machine Learning into a Crowdsourced Model for Earthquake-Induced Damage Assessment

NASA Technical Reports Server (NTRS)

Rebbapragada, Umaa; Oommen, Thomas

2011-01-01

On January 12th, 2010, a catastrophic 7.0M earthquake devastated the country of Haiti. In the aftermath of an earthquake, it is important to rapidly assess damaged areas in order to mobilize the appropriate resources. The Haiti damage assessment effort introduced a promising model that uses crowdsourcing to map damaged areas in freely available remotely-sensed data. This paper proposes the application of machine learning methods to improve this model. Specifically, we apply work on learning from multiple, imperfect experts to the assessment of volunteer reliability, and propose the use of image segmentation to automate the detection of damaged areas. We wrap both tasks in an active learning framework in order to shift volunteer effort from mapping a full catalog of images to the generation of high-quality training data. We hypothesize that the integration of machine learning into this model improves its reliability, maintains the speed of damage assessment, and allows the model to scale to higher data volumes.

Reverse Current Shock Induced by Plasma-Neutral Collision

NASA Astrophysics Data System (ADS)

Wongwaitayakornkul, Pakorn; Haw, Magnus; Li, Hui; Li, Shengtai; Bellan, Paul

2017-10-01

The Caltech solar experiment creates an arched plasma-filled flux rope expanding into low density background plasma. A layer of electrical current flowing in the opposite direction with respect to the flux rope current is induced in the background plasma just ahead of the flux rope. Two dimensional spatial and temporal measurements by a 3-dimensional magnetic vector probe demonstrate the existence of this induced current layer forming ahead of the flux rope. The induced current magnitude is 20% of the magnitude of the current in the flux rope. The reverse current in the low density background plasma is thought to be a diamagnetic response that shields out the magnetic field ahead of the propagation. The spatial and magnetic characteristics of the reverse current layer are consistent with similar shock structures seen in 3-dimensional ideal MHD numerical simulations performed on the Turquoise supercomputer cluster using the Los Alamos COMPutational Astrophysics Simulation Suite. This discovery of the induced diamagnetic current provides useful insights for space and solar plasma.

Experiments on the Expansion of a Dense Plasma into a Background Magnetoplasma

NASA Astrophysics Data System (ADS)

Gekelman, Walter; Vanzeeland, Mike; Vincena, Steve; Pribyl, Pat

2003-10-01

There are many situations, which occur in space (coronal mass ejections, or are man-made (upper atmospheric detonations) as well as the initial stages of a supernovae, in which a dense plasma expands into a background magnetized plasma, that can support Alfvèn waves. The upgraded LArge Plasma Device (LAPD) is a machine, at UCLA, in which Alfvèn wave propagation in homogeneous and inhomogeneous plasmas has been studied. We describe a series of experiments,which involve the expansion of a dense (initially, n_laser-plasma/n_0≫1) laser-produced plasma into an ambient highly magnetized background plasma capable of supporting Alfvèn waves will be presented. The 150 MW laser is pulsed at the same 1 Hz repetition rate as the plasma in a highly reproducible experiment. The interaction results in the production of intense shear Alfvèn waves, as well as large density perturbations. The waves propagate away from the target and are observed to become plasma column resonances. In the initial phase the background magnetic field is expelled from a plasma bubble. Currents in the main body of the plasma are generated to neutralize the positively charged bubble. The current system which results, becomes that of a spectrum of shear Alfvèn waves. Spatial patterns of the wave magnetic fields waves are measured at over 10^4 locations. As the dense plasma expands across the magnetic field it seeds the column with shear waves. Most of the Alfvèn wave energy is in shear waves, which become field line resonances after a machine transit time. The interplay between waves, currents, inductive electric fields and space charge is analyzed in great detail. Dramatic movies of the measured wave fields and their associated currents will be presented. Work supported by ONR, and DOE /NSF.

ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples

PubMed Central

2011-01-01

Background Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases. Results We propose ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe implements a novel machine learning strategy based on learning from positive and unlabeled examples, which allows to integrate various sources of information about the genes, to share information about known disease genes across diseases, and to perform genome-wide searches for new disease genes. Experiments on real data show that ProDiGe outperforms state-of-the-art methods for the prioritization of genes in human diseases. Conclusions ProDiGe implements a new machine learning paradigm for gene prioritization, which could help the identification of new disease genes. It is freely available at http://cbio.ensmp.fr/prodige. PMID:21977986

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

PubMed

Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

2016-01-01

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science

PubMed Central

Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

2016-01-01

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets. PMID:27532883

Optimal filtering and Bayesian detection for friction-based diagnostics in machines.

PubMed

Ray, L R; Townsend, J R; Ramasubramanian, A

2001-01-01

Non-model-based diagnostic methods typically rely on measured signals that must be empirically related to process behavior or incipient faults. The difficulty in interpreting a signal that is indirectly related to the fundamental process behavior is significant. This paper presents an integrated non-model and model-based approach to detecting when process behavior varies from a proposed model. The method, which is based on nonlinear filtering combined with maximum likelihood hypothesis testing, is applicable to dynamic systems whose constitutive model is well known, and whose process inputs are poorly known. Here, the method is applied to friction estimation and diagnosis during motion control in a rotating machine. A nonlinear observer estimates friction torque in a machine from shaft angular position measurements and the known input voltage to the motor. The resulting friction torque estimate can be analyzed directly for statistical abnormalities, or it can be directly compared to friction torque outputs of an applicable friction process model in order to diagnose faults or model variations. Nonlinear estimation of friction torque provides a variable on which to apply diagnostic methods that is directly related to model variations or faults. The method is evaluated experimentally by its ability to detect normal load variations in a closed-loop controlled motor driven inertia with bearing friction and an artificially-induced external line contact. Results show an ability to detect statistically significant changes in friction characteristics induced by normal load variations over a wide range of underlying friction behaviors.

A systematic analysis of the XMM-Newton background: I. Dataset and extraction procedures

NASA Astrophysics Data System (ADS)

Marelli, Martino; Salvetti, David; Gastaldello, Fabio; Ghizzardi, Simona; Molendi, Silvano; Luca, Andrea De; Moretti, Alberto; Rossetti, Mariachiara; Tiengo, Andrea

2017-12-01

XMM-Newton is the direct precursor of the future ESA ATHENA mission. A study of its particle-induced background provides therefore significant insight for the ATHENA mission design. We make use of ˜12 years of data, products from the third XMM-Newton catalog as well as FP7 EXTraS project to avoid celestial sources contamination and to disentangle the different components of the XMM-Newton particle-induced background. Within the ESA R&D AREMBES collaboration, we built new analysis pipelines to study the different components of this background: this covers time behavior as well as spectral and spatial characteristics.

The effects of implant topography on osseointegration under estrogen deficiency induced osteoporotic conditions: Histomorphometric, transcriptional and ultrastructural analysis.

PubMed

Du, Zhibin; Xiao, Yin; Hashimi, Saeed; Hamlet, Stephen M; Ivanovski, Saso

2016-09-15

Compromised bone quality and/or healing in osteoporosis are recognised risk factors for impaired dental implant osseointegration. This study examined the effects of (1) experimentally induced osteoporosis on titanium implant osseointegration and (2) the effect of modified implant surface topography on osseointegration under osteoporosis-like conditions. Machined and micro-roughened surface implants were placed into the maxillary first molar root socket of 64 ovariectomised and sham-operated Sprague-Dawley rats. Subsequent histological and SEM observations showed tissue maturation on the micro-rough surfaced implants in ovariectomised animals as early as 3days post-implantation. The degree of osseointegration was also significantly higher around the micro-rough implants in ovariectomised animals after 14days of healing although by day 28, similar levels of osseointegration were found for all test groups. The micro-rough implants significantly increased the early (day 3) gene expression of alkaline phosphatase, osteocalcin, receptor activator of nuclear factor kappa-B ligand and dentin matrix protein 1 in implant adherent cells. By day 7, the expression of inflammatory genes decreased while the expression of the osteogenic markers increased further although there were few statistically significant differences between the micro-rough and machined surfaces. Osteocyte morphology was also affected by estrogen deficiency with the size of the cells being reduced in trabecular bone. In conclusion, estrogen deficiency induced osteoporotic conditions negatively influenced the early osseointegration of machined implants while micro-rough implants compensated for these deleterious effects by enhancing osteogenic cell differentiation on the implant surface. Lower bone density, poor bone quality and osseous microstructural changes are all features characteristic of osteoporosis that may impair the osseointegration of dental implants. Using a clinically relevant trabecular bone model in the rat maxilla, we demonstrated histologically that the negative effects of surgically-induced osteoporosis on osseointegration could be ameliorated by the biomaterial's surface topography. Furthermore, gene expression analysis suggests this may be a result of enhanced osteogenic cell differentiation on the implant surface. Copyright © 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

A shuttle and space station manipulator system for assembly, docking, maintenance, cargo handling and spacecraft retrieval (preliminary design). Volume 2: Concept development and selection

NASA Technical Reports Server (NTRS)

1972-01-01

The overall program background, the various system concepts considered, and the rationale for the selected design are described. The concepts for each subsystem are also described and compared. Details are given for the requirements, boom configuration and dynamics, actuators, man/machine interface and control, visual system, control system, environmental control and life support, data processing, and materials.

An introduction to kernel-based learning algorithms.

PubMed

Müller, K R; Mika, S; Rätsch, G; Tsuda, K; Schölkopf, B

2001-01-01

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

Dose inspection and risk assessment on radiation safety for the use of non-medical X-ray machines in Taiwan

NASA Astrophysics Data System (ADS)

Hsu, Fang-Yuh; Hsu, Shih-Ming; Chao, Jiunn-Hsing

2017-11-01

The subject of this study is the on-site visits and inspections of facilities commissioned by the Atomic Energy Council (AEC) in Taiwan. This research was conducted to evaluate the possible dose and dose rate of cabinet-type X-ray equipment with nominal voltages of 30-150 kV and open-beam (portable or handheld) equipment, taking both normal operation and possibly abnormal operation conditions into account. Doses and dose rates were measured using a plastic scintillation survey meter and an electronic personal dosimeter. In total, 401 X-ray machines were inspected, including 139 units with nominal voltages of 30-50 kV X-ray equipment, 140 units with nominal voltages of 50-150 kV, and 122 open-beam (portable or handheld) X-ray equipment. The investigated doses for radiation workers and non-radiation workers operating cabinet-type X-ray equipment under normal safety conditions were all at the background dose level. Several investigated dose rates at the position of 10 cm away from the surface of open-beam (portable or handheld) X-ray equipment were very high, such X-ray machines are used by aeronautical police for the detection of suspected explosives, radiation workers are far away (at least 10 m away) from the X-ray machine during its operation. The doses per operation in X-ray equipment with a 30-50 kV nominal voltage were less than 1 mSv in all cases of abnormal use. Some doses were higher than 1 mSv per operation for X-ray equipment of 50-150 kV nominal voltage X-ray. The maximum dose rates at the beam exit have a very wide range, mostly less than 100 μSv/s and the largest value is about 3.92 mSv/s for open-beam (portable or handheld) X-ray devices. The risk induced by operating X-ray devices with nominal voltages of 30-50 kV is extremely low. The 11.5 mSv dose due to one operation at nominal voltage of 50-150 kV X-ray device is equivalent to the exposure of taking 575 chest X-rays. In the abnormal use of open-beam (portable or handheld) X-ray equipment, the effective dose of 3.92 mSv/s is equivalent to taking 196 chest radiographs within 1 s. This work assessed the annual doses (equivalent and effective doses) and risks of X-ray operator staff as reasonably as possible. The results of this research are helpful to the AEC (competent authority of ionization radiation) to improve the management and perform the safe control of X-ray equipment.

Pigmentation plasticity enhances crypsis in larval newts: associated metabolic cost and background choice behaviour

PubMed Central

Polo-Cavia, Nuria; Gomez-Mestre, Ivan

2017-01-01

In heterogeneous environments, the capacity for colour change can be a valuable adaptation enhancing crypsis against predators. Alternatively, organisms might achieve concealment by evolving preferences for backgrounds that match their visual traits, thus avoiding the costs of plasticity. Here we examined the degree of plasticity in pigmentation of newt larvae (Lissotriton boscai) in relation to predation risk. Furthermore, we tested for associated metabolic costs and pigmentation-dependent background choice behaviour. Newt larvae expressed substantial changes in pigmentation so that light, high-reflecting environment induced depigmentation whereas dark, low-reflecting environment induced pigmentation in just three days of exposure. Induced pigmentation was completely reversible upon switching microhabitats. Predator cues, however, did not enhance cryptic phenotypes, suggesting that environmental albedo induces changes in pigmentation improving concealment regardless of the perceived predation risk. Metabolic rate was higher in heavily pigmented individuals from dark environments, indicating a high energetic requirement of pigmentation that could impose a constraint to larval camouflage in dim habitats. Finally, we found partial evidence for larvae selecting backgrounds matching their induced phenotypes. However, in the presence of predator cues, larvae increased the time spent in light environments, which may reflect a escape response towards shallow waters rather than an attempt at increasing crypsis. PMID:28051112

Can muon-induced backgrounds explain the DAMA data?

NASA Astrophysics Data System (ADS)

Klinger, Joel; Kudryavtsev, Vitaly A.

2016-05-01

We present an accurate simulation of the muon-induced background in the DAMA/LIBRA experiment. Muon sampling underground has been performed using the MUSIC/MUSUN codes and subsequent interactions in the rock around the DAMA/LIBRA detector cavern and the experimental setup including shielding, have been simulated with GEANT4.9.6. In total we simulate the equivalent of 20 years of muon data. We have calculated the total muon-induced neutron flux in the DAMA/LIBRA detector cavern as Φμ n = 1.0 × 10-9 cm-2s-1, which is consistent with other simulations. After selecting events which satisfy the DAMA/LIBRA signal criteria, our simulation predicts 3.49 × 10-5 cpd/kg/keV which accounts for less than 0.3% of the DAMA/LIBRA modulation amplitude. We conclude from our work that muon-induced backgrounds are unable to contribute to the observed signal modulation.

Predicting a small molecule-kinase interaction map: A machine learning approach

PubMed Central

2011-01-01

Background We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features. Results A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed classifiers. Moreover, the paper introduces a new way of evaluating predictions in such a setting, where different amounts of information about the binding partners can be assumed to be available for training. Results on an external test set are also provided. Conclusions In most of the cases, the presented approach clearly outperforms the baseline methods used for comparison. Experimental results indicate that the applied machine learning methods are able to detect a signal in the data and predict binding affinity to some extent. For SVMs, the binding prediction can be improved significantly by using features that describe the active site of a kinase. For C5, besides diversity in the feature set, alignment scores of conserved regions turned out to be very useful. PMID:21708012

Microgravity simulation by diamagnetic levitation: effects of a strong gradient magnetic field on the transcriptional profile of Drosophila melanogaster

PubMed Central

2012-01-01

Background Many biological systems respond to the presence or absence of gravity. Since experiments performed in space are expensive and can only be undertaken infrequently, Earth-based simulation techniques are used to investigate the biological response to weightlessness. A high gradient magnetic field can be used to levitate a biological organism so that its net weight is zero. Results We have used a superconducting magnet to assess the effect of diamagnetic levitation on the fruit fly D. melanogaster in levitation experiments that proceeded for up to 22 consecutive days. We have compared the results with those of similar experiments performed in another paradigm for microgravity simulation, the Random Positioning Machine (RPM). We observed a delay in the development of the fruit flies from embryo to adult. Microarray analysis indicated changes in overall gene expression of imagoes that developed from larvae under diamagnetic levitation, and also under simulated hypergravity conditions. Significant changes were observed in the expression of immune-, stress-, and temperature-response genes. For example, several heat shock proteins were affected. We also found that a strong magnetic field, of 16.5 Tesla, had a significant effect on the expression of these genes, independent of the effects associated with magnetically-induced levitation and hypergravity. Conclusions Diamagnetic levitation can be used to simulate an altered effective gravity environment in which gene expression is tuned differentially in diverse Drosophila melanogaster populations including those of different age and gender. Exposure to the magnetic field per se induced similar, but weaker, changes in gene expression. PMID:22296880

Ultrafine-grained commercially pure titanium and microstructure response to hydroxyapatite coating methods

NASA Astrophysics Data System (ADS)

Calvert, Kayla L.

Commercially pure titanium (cp-Ti) is an ideal biomaterial as it does not evoke an inflammatory foreign body response in the body. However, the low strength of cp-Ti prevents the use in most orthopaedic load bearing applications. Therefore, many metal orthopaedic implants are commonly made of higher strength metal alloys that are less biocompatible. Nanostructured materials exhibit superior mechanical properties compared to their conventional grain sized counterparts. Severe plastic deformation (SPD) of metals has been shown to produce nanostructured materials. SPD by machining is a single-step deformation route that refines the grain microstructure, to develop an ultrafine grained (UFG) microstructure. UFG cp-Ti strips were developed with induced shear strains of up to 4.0 using a machining-based process. Both Vickers microhardness evaluation and microstructural analysis were used to characterize the as-received (annealed) and machined states. For induced shear strains between 1.9 and 4.0 in grade 2 cp-Ti the hardness was increased from 188 +/- 7 kg/mm2 in the as-received state to between 244 +/- 6 and 264 +/- 12 kg/mm 2 in the as-machined state, corresponding to an increase in hardness between 31 and 41%. The microstructural analysis revealed a grain size reduction from 34 +/- 11 mum in the as-received state to ˜ 100 nm for machined grade 2-Ti. A complete annealing study suggested that recovery/recrystallization occurs between 300 and 400°C, with a significant hardness drop between 400 and 600°C, while grain growth is continuous, starting at the lowest annealing temperature of 300°C. Hydroxyapatite (HA) is commonly applied to orthopaedic devices to promote bone growth. Machined Ti strips were coated with HA using conventional plasma spray as well as two alternative low-temperature application routes (sol-gel with calcination and anodization with hydrothermal treatment) to evaluate the thermal influence on the UFG-Ti substrate. Plasma spray produced a thick (20 to 70 mum) HA crystalline coating, sol-gel followed by calcination did not produce crystalline HA, while anodization with the proper hydrothermal treatment yielded a homogenous crystalline HA coating 5 to 15 mum thick based on the anodization condition. Mechanical and microstructural evaluation of the UFG-Ti substrates revealed that both the plasma spray and anodization followed by hydrothermal treatment (220 -- 225°C) did not affect the substrate grain size or hardness, while the thermal processing and calcination treatment at 313 -- 446°C for the sol-gel method caused recovery and grain growth, as well as a significant decrease in the hardness of the Ti-substrates.

Effect of stationary objects on illusory forward self-motion induced by a looming display.

PubMed

Ohmi, M; Howard, I P

1988-01-01

It has previously been shown that when a moving and a stationary display are superimposed, illusory self-rotation (circular vection) is induced only when the moving display appears as the background. Three experiments are reported on the extent to which illusory forward self-motion (forward vection) induced by a looming display is inhibited by a superimposed stationary display as a function of the size and location of the stationary display and of the depth between the stationary and looming displays. Results showed that forward vection was controlled by the display that was perceived as the background, and background stationary displays suppressed forward vection by about the same amount whatever their size and eccentricity. Also, the perception of foreground-background properties of competing displays determined which controlled forward vection, and this control was not tied to specific depth cues. The inhibitory effect of a stationary background on forward vection was, however, weaker than that found with circular vection. This difference makes sense because, for forward body motion, the image of a distant scene is virtually stationary whereas, when the body rotates, it is not.

Muscle activity during knee-extension strengthening exercise performed with elastic tubing and isotonic resistance.

PubMed

Jakobsen, Markus Due; Sundstrup, Emil; Andersen, Christoffer H; Bandholm, Thomas; Thorborg, Kristian; Zebis, Mette K; Andersen, Lars L

2012-12-01

While elastic resistance training, targeting the upper body is effective for strength training, the effect of elastic resistance training on lower body muscle activity remains questionable. The purpose of this study was to evaluate the EMG-angle relationship of the quadriceps muscle during 10-RM knee-extensions performed with elastic tubing and an isotonic strength training machine. 7 women and 9 men aged 28-67 years (mean age 44 and 41 years, respectively) participated. Electromyographic (EMG) activity was recorded in 10 muscles during the concentric and eccentric contraction phase of a knee extension exercise performed with elastic tubing and in training machine and normalized to maximal voluntary isometric contraction (MVC) EMG (nEMG). Knee joint angle was measured during the exercises using electronic inclinometers (range of motion 0-90°). When comparing the machine and elastic resistance exercises there were no significant differences in peak EMG of the rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM) during the concentric contraction phase. However, during the eccentric phase, peak EMG was significantly higher (p<0.01) in RF and VM when performing knee extensions using the training machine. In VL and VM the EMG-angle pattern was different between the two training modalities (significant angle by exercise interaction). When using elastic resistance, the EMG-angle pattern peaked towards full knee extension (0°), whereas angle at peak EMG occurred closer to knee flexion position (90°) during the machine exercise. Perceived loading (Borg CR10) was similar during knee extensions performed with elastic tubing (5.7±0.6) compared with knee extensions performed in training machine (5.9±0.5). Knee extensions performed with elastic tubing induces similar high (>70% nEMG) quadriceps muscle activity during the concentric contraction phase, but slightly lower during the eccentric contraction phase, as knee extensions performed using an isotonic training machine. During the concentric contraction phase the two different conditions displayed reciprocal EMG-angle patterns during the range of motion. 5.

Automated discovery systems and the inductivist controversy

NASA Astrophysics Data System (ADS)

Giza, Piotr

2017-09-01

The paper explores possible influences that some developments in the field of branches of AI, called automated discovery and machine learning systems, might have upon some aspects of the old debate between Francis Bacon's inductivism and Karl Popper's falsificationism. Donald Gillies facetiously calls this controversy 'the duel of two English knights', and claims, after some analysis of historical cases of discovery, that Baconian induction had been used in science very rarely, or not at all, although he argues that the situation has changed with the advent of machine learning systems. (Some clarification of terms machine learning and automated discovery is required here. The key idea of machine learning is that, given data with associated outcomes, software can be trained to make those associations in future cases which typically amounts to inducing some rules from individual cases classified by the experts. Automated discovery (also called machine discovery) deals with uncovering new knowledge that is valuable for human beings, and its key idea is that discovery is like other intellectual tasks and that the general idea of heuristic search in problem spaces applies also to discovery tasks. However, since machine learning systems discover (very low-level) regularities in data, throughout this paper I use the generic term automated discovery for both kinds of systems. I will elaborate on this later on). Gillies's line of argument can be generalised: thanks to automated discovery systems, philosophers of science have at their disposal a new tool for empirically testing their philosophical hypotheses. Accordingly, in the paper, I will address the question, which of the two philosophical conceptions of scientific method is better vindicated in view of the successes and failures of systems developed within three major research programmes in the field: machine learning systems in the Turing tradition, normative theory of scientific discovery formulated by Herbert Simon's group and the programme called HHNT, proposed by J. Holland, K. Holyoak, R. Nisbett and P. Thagard.

GENETIC BACKGROUND BUT NOT METALLOTHIONEIN PHENOTYPE DICTATES SENSITIVITY TO CADMIUM-INDUCED TESTICULAR INJURY IN MICE

EPA Science Inventory

Genetic Background but not Metallothionein Phenotype Dictates Sensitivity to
Cadmium-Induced Testicular Injury in Mice

Jie Liu1,2, Chris Corton3, David J. Dix4, Yaping Liu1, Michael P. Waalkes2
and Curtis D. Klaassen1

ABSTRACT

Parenteral administrati...

Measurements of the Mechanisms of Laminar-Turbulent Transition in the Mach-6 Quiet Tunnel

DTIC Science & Technology

2012-02-28

fairly complex axisymmetric models could be built on the 2001 CNC lathe in the department machine shop at a very affordable cost, (5) laminar flow seemed...produce laser-induced breakdown plasmas in a test cell, even at atmospheric pressure. Because of this, the contoured window and compensating optical

Bruising profile of fresh apples associated with tissue type and structure. Applied Engineering in Agriculture

USDA-ARS?s Scientific Manuscript database

It is important to understand how apples bruise in order to prevent or reduce bruising. Tissue from ‘Golden Delicious’ apples was analyzed to determine the bruising mechanism at different maturity levels. Bruising was induced by an artificial finger attached to an Instron machine applying an exter...

Cook-Levin Theorem Algorithmic-Reducibility/Completeness = Wilson Renormalization-(Semi)-Group Fixed-Points; ``Noise''-Induced Phase-Transitions (NITs) to Accelerate Algorithmics (``NIT-Picking'') REPLACING CRUTCHES!!!: Models: Turing-machine, finite-state-models, finite-automata

NASA Astrophysics Data System (ADS)

Young, Frederic; Siegel, Edward

Cook-Levin theorem theorem algorithmic computational-complexity(C-C) algorithmic-equivalence reducibility/completeness equivalence to renormalization-(semi)-group phase-transitions critical-phenomena statistical-physics universality-classes fixed-points, is exploited via Siegel FUZZYICS =CATEGORYICS = ANALOGYICS =PRAGMATYICS/CATEGORY-SEMANTICS ONTOLOGY COGNITION ANALYTICS-Aristotle ``square-of-opposition'' tabular list-format truth-table matrix analytics predicts and implements ''noise''-induced phase-transitions (NITs) to accelerate versus to decelerate Harel [Algorithmics (1987)]-Sipser[Intro.Thy. Computation(`97)] algorithmic C-C: ''NIT-picking''(!!!), to optimize optimization-problems optimally(OOPO). Versus iso-''noise'' power-spectrum quantitative-only amplitude/magnitude-only variation stochastic-resonance, ''NIT-picking'' is ''noise'' power-spectrum QUALitative-type variation via quantitative critical-exponents variation. Computer-''science''/SEANCE algorithmic C-C models: Turing-machine, finite-state-models, finite-automata,..., discrete-maths graph-theory equivalence to physics Feynman-diagrams are identified as early-days once-workable valid but limiting IMPEDING CRUTCHES(!!!), ONLY IMPEDE latter-days new-insights!!!

Separation of foreground and background from light field using gradient information.

PubMed

Lee, Jae Young; Park, Rae-Hong

2017-02-01

Studies of computer vision or machine vision applications using a light field camera have been increasing in recent years. However, the abilities that the light field camera has are not fully used in these applications. In this paper, we propose a method for direct separation of foreground and background that uses the gradient information and can be used in various applications such as pre-processing. From an optical phenomenon whereby the bundles of rays from the background are flipped, we derive that the disparity sign of the background in the captured three-dimensional scene has the opposite disparity sign of the foreground. Using the majority-weighted voting algorithm based on the gradient information with the Lambertian assumption and the gradient constraint, the foreground and background can be separated at each pixel. In regard to pre-processing, the proposed method can be used for various applications such as occlusion and saliency detection, disparity estimation, and so on. Experimental results with the EPFL light field dataset and Stanford Lytro light field dataset show that the proposed method achieves better performance in terms of the occlusion detection, and thus can be effectively used in pre-processing for saliency detection and disparity estimation.

Support vector machines and generalisation in HEP

NASA Astrophysics Data System (ADS)

Bevan, Adrian; Gamboa Goñi, Rodrigo; Hays, Jon; Stevenson, Tom

2017-10-01

We review the concept of Support Vector Machines (SVMs) and discuss examples of their use in a number of scenarios. Several SVM implementations have been used in HEP and we exemplify this algorithm using the Toolkit for Multivariate Analysis (TMVA) implementation. We discuss examples relevant to HEP including background suppression for H → τ + τ - at the LHC with several different kernel functions. Performance benchmarking leads to the issue of generalisation of hyper-parameter selection. The avoidance of fine tuning (over training or over fitting) in MVA hyper-parameter optimisation, i.e. the ability to ensure generalised performance of an MVA that is independent of the training, validation and test samples, is of utmost importance. We discuss this issue and compare and contrast performance of hold-out and k-fold cross-validation. We have extended the SVM functionality and introduced tools to facilitate cross validation in TMVA and present results based on these improvements.

Study of Two-Dimensional Compressible Non-Acoustic Modeling of Stirling Machine Type Components

NASA Technical Reports Server (NTRS)

Tew, Roy C., Jr.; Ibrahim, Mounir B.

2001-01-01

A two-dimensional (2-D) computer code was developed for modeling enclosed volumes of gas with oscillating boundaries, such as Stirling machine components. An existing 2-D incompressible flow computer code, CAST, was used as the starting point for the project. CAST was modified to use the compressible non-acoustic Navier-Stokes equations to model an enclosed volume including an oscillating piston. The devices modeled have low Mach numbers and are sufficiently small that the time required for acoustics to propagate across them is negligible. Therefore, acoustics were excluded to enable more time efficient computation. Background information about the project is presented. The compressible non-acoustic flow assumptions are discussed. The governing equations used in the model are presented in transport equation format. A brief description is given of the numerical methods used. Comparisons of code predictions with experimental data are then discussed.

Cosmic string detection with tree-based machine learning

NASA Astrophysics Data System (ADS)

Vafaei Sadr, A.; Farhang, M.; Movahed, S. M. S.; Bassett, B.; Kunz, M.

2018-07-01

We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies. The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of the processed CMB maps that boost cosmic string detectability. Our proposed classifiers, after training, give results similar to or better than claimed detectability levels from other methods for string tension, Gμ. They can make 3σ detection of strings with Gμ ≳ 2.1 × 10-10 for noise-free, 0.9'-resolution CMB observations. The minimum detectable tension increases to Gμ ≳ 3.0 × 10-8 for a more realistic, CMB S4-like (II) strategy, improving over previous results.

Cosmic String Detection with Tree-Based Machine Learning

NASA Astrophysics Data System (ADS)

Vafaei Sadr, A.; Farhang, M.; Movahed, S. M. S.; Bassett, B.; Kunz, M.

2018-05-01

We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies. The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of the processed CMB maps that boost cosmic string detectability. Our proposed classifiers, after training, give results similar to or better than claimed detectability levels from other methods for string tension, Gμ. They can make 3σ detection of strings with Gμ ≳ 2.1 × 10-10 for noise-free, 0.9΄-resolution CMB observations. The minimum detectable tension increases to Gμ ≳ 3.0 × 10-8 for a more realistic, CMB S4-like (II) strategy, improving over previous results.

KSC-99pp0277

NASA Image and Video Library

1999-03-06

During the 1999 FIRST Southeastern Regional robotic competition held at KSC, a robot carrying its cache of pillow-like disks maneuvers to move around another at left. Powered by 12-volt batteries and operated by remote control, the robotic gladiators spend two minutes each trying to grab, claw and hoist the pillows onto their machines. Teams play defense by taking away competitors' pillows and generally harassing opposing machines. Behind the field are a group of judges, including KSC former KSC Director of Shuttle Processing Robert Sieck (left, in cap), and Center Director Roy Bridges (in white shirt). A giant screen TV in the background displays the action on the playing field. FIRST is a nonprofit organization, For Inspiration and Recognition of Science and Technology. The competition comprised 27 teams, pairing high school students with engineer mentors and corporations. The FIRST robotics competition is designed to provide students with a hands-on, inside look at engineering and other professional careers

The research and application of visual saliency and adaptive support vector machine in target tracking field.

PubMed

Chen, Yuantao; Xu, Weihong; Kuang, Fangjun; Gao, Shangbing

2013-01-01

The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking's accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper's algorithm has been based on the mixture saliency of image features. These features include color, brightness, and sport feature. The execution process used visual saliency features and those common characteristics have been expressed as the target's saliency. Numerous experiments demonstrate the effectiveness and timeliness of the proposed target tracking algorithm in video sequences where the target objects undergo large changes in pose, scale, and illumination.

Machine vision application in animal trajectory tracking.

PubMed

Koniar, Dušan; Hargaš, Libor; Loncová, Zuzana; Duchoň, František; Beňo, Peter

2016-04-01

This article was motivated by the doctors' demand to make a technical support in pathologies of gastrointestinal tract research [10], which would be based on machine vision tools. Proposed solution should be less expensive alternative to already existing RF (radio frequency) methods. The objective of whole experiment was to evaluate the amount of animal motion dependent on degree of pathology (gastric ulcer). In the theoretical part of the article, several methods of animal trajectory tracking are presented: two differential methods based on background subtraction, the thresholding methods based on global and local threshold and the last method used for animal tracking was the color matching with a chosen template containing a searched spectrum of colors. The methods were tested offline on five video samples. Each sample contained situation with moving guinea pig locked in a cage under various lighting conditions. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

PubMed Central

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J.; Raboso, Mariano

2015-01-01

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. PMID:26091392

Grid-connected in-stream hydroelectric generation based on the doubly fed induction machine

NASA Astrophysics Data System (ADS)

Lenberg, Timothy J.

Within the United States, there is a growing demand for new environmentally friendly power generation. This has led to a surge in wind turbine development. Unfortunately, wind is not a stable prime mover, but water is. Why not apply the advances made for wind to in-stream hydroelectric generation? One important advancement is the creation of the Doubly Fed Induction Machine (DFIM). This thesis covers the application of a gearless DFIM topology for hydrokinetic generation. After providing background, this thesis presents many of the options available for the mechanical portion of the design. A mechanical turbine is then specified. Next, a method is presented for designing a DFIM including the actual design for this application. In Chapter 4, a simulation model of the system is presented, complete with a control system that maximizes power generation based on water speed. This section then goes on to present simulation results demonstrating proper operation.

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

PubMed

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

2015-06-17

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

Classification of radiological errors in chest radiographs, using support vector machine on the spatial frequency features of false- negative and false-positive regions

NASA Astrophysics Data System (ADS)

Pietrzyk, Mariusz W.; Donovan, Tim; Brennan, Patrick C.; Dix, Alan; Manning, David J.

2011-03-01

Aim: To optimize automated classification of radiological errors during lung nodule detection from chest radiographs (CxR) using a support vector machine (SVM) run on the spatial frequency features extracted from the local background of selected regions. Background: The majority of the unreported pulmonary nodules are visually detected but not recognized; shown by the prolonged dwell time values at false-negative regions. Similarly, overestimated nodule locations are capturing substantial amounts of foveal attention. Spatial frequency properties of selected local backgrounds are correlated with human observer responses either in terms of accuracy in indicating abnormality position or in the precision of visual sampling the medical images. Methods: Seven radiologists participated in the eye tracking experiments conducted under conditions of pulmonary nodule detection from a set of 20 postero-anterior CxR. The most dwelled locations have been identified and subjected to spatial frequency (SF) analysis. The image-based features of selected ROI were extracted with un-decimated Wavelet Packet Transform. An analysis of variance was run to select SF features and a SVM schema was implemented to classify False-Negative and False-Positive from all ROI. Results: A relative high overall accuracy was obtained for each individually developed Wavelet-SVM algorithm, with over 90% average correct ratio for errors recognition from all prolonged dwell locations. Conclusion: The preliminary results show that combined eye-tracking and image-based features can be used for automated detection of radiological error with SVM. The work is still in progress and not all analytical procedures have been completed, which might have an effect on the specificity of the algorithm.

Gamma-ray background induced by atmospheric neutrons

NASA Astrophysics Data System (ADS)

Ma, Y.-Q.

1984-03-01

A small piggyback detector system is used to study the reduction of gamma-ray background induced by atmospheric neutrons in the type of actively shielded gamma-ray spectroscopes. The system consists of two 1.5 x 1.5 arcsec NaI crystal units, one of which is surrounded by some neutron shield material. The results of a balloon flight in 1981 are presented. The data show that a shield of 3 cm-thick pure paraffin cannot reduce the gamma-ray background. On the contrary, it may even cause some enhancement.

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music.

PubMed

Giraldo, Sergio I; Ramirez, Rafael

2016-01-01

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules.

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music

PubMed Central

Giraldo, Sergio I.; Ramirez, Rafael

2016-01-01

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules. PMID:28066290

Disorder-induced transparency in a one-dimensional waveguide side coupled with optical cavities

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

Zhang, Yongyou, E-mail: yyzhang@bit.edu.cn; Dong, Guangda; Zou, Bingsuo

2014-05-07

Disorder influence on photon transmission behavior is theoretically studied in a one-dimensional waveguide side coupled with a series of optical cavities. For this sake, we propose a concept of disorder-induced transparency appearing on the low-transmission spectral background. Two kinds of disorders, namely, disorders of optical cavity eigenfrequencies and relative phases in the waveguide side coupled with optical cavities are considered to show the disorder-induced transparency. They both can induce the optical transmission peaks on the low-transmission backgrounds. The statistical mean value of the transmission also increases with increasing the disorders of the cavity eigenfrequencies and relative phases.

Cosmic bandits: Exploration versus exploitation in CMB B-mode experiments

NASA Astrophysics Data System (ADS)

Kovetz, Ely D.; Kamionkowski, Marc

2016-02-01

A preferred method to detect the curl-component, or B-mode, signature of inflationary gravitational waves (IGWs) in the cosmic microwave background (CMB) polarization, in the absence of foregrounds and lensing, is a prolonged integration over a single patch of sky of a few square degrees. In practice, however, foregrounds abound and the sensitivity to B modes can be improved considerably by finding the region of sky cleanest of foregrounds. The best strategy to detect B modes thus involves a tradeoff between exploration (to find lower-foreground patches) and exploitation (through prolonged integration). This problem is akin to the multi-armed bandit (MAB) problem in probability theory, wherein a gambler faces a series of slot machines with unknown winning odds and must develop a strategy to maximize his/her winnings with some finite number of pulls. While the optimal MAB strategy remains to be determined, a number of algorithms have been developed in an effort to maximize the winnings. Here, based on this resemblance, we tackle the search for IGW B modes with single frequency experiments in the presence of spatially varying foregrounds by developing adaptive survey strategies to optimize the sensitivity to IGW B modes. We demonstrate, using realistic foreground models and taking lensing-induced B modes into account, that adaptive experiments can substantially improve the upper bound on the tensor-to-scalar ratio (by factors of 2 and 3 in single frequency experiments, and possibly even more). Similar techniques can be applied to other surveys, including 21-cm measurements of signatures of the epoch of reionization, searches for a stochastic primordial gravitational wave background, deep-field imaging by the James Webb Space Telescope or various radio interferometers, and transient follow-up searches.

Environmental Exposure and Accelerated Testing of Rubber-to-Metal Vulcanized Bonded Assemblies

DTIC Science & Technology

1974-11-01

by weapon components in the field and to determine the effect of this exposure on the vulcanized bond The purpose is also to duplicate these long term...storage and environmental exposure, and to develop accelerated methods for use in predicting this resistance. BACKGROUND: The most effective method of... the rubber coatings on the M60 machine gun components, the shock isolator and recoil adapter on the CAU 28/A Minigun, rubber pads for all tracked

Career Profiles- Aero-Mechanical Design- Operations Engineering Branch

NASA Image and Video Library

2015-10-26

NASA Armstrong’s Aeromechanical Design Group provides mechanical design solutions ranging from research and development to ground support equipment. With an aerospace or mechanical engineering background, team members use the latest computer-aided design software to create one-of-kind parts, assemblies, and drawings, and aid in the design’s fabrication and integration. Reverse engineering and inspection of Armstrong’s fleet of aircraft is made possible by using state-of-the-art coordinate measuring machines and laser scanning equipment.

Evaluation of State-of-the-Art High Speed Deluge Systems Presently in Service at Various U.S. Army Ammunition Plants

DTIC Science & Technology

1993-09-01

designed to respond to. No data exists on spectral irradiances in the IR or UV spectral bands where the current detectors operate. A need exists to...appropriate fire/explosion detection spectral bands. Setting a pyrotechnic fire and testing the responses of commercial UV and IR detectors that are designed...PNZ B. DETECTOR BACKGROUND ............... 30 C. UV DETECTORS . . ............ . . . 32 D. IR DETECTORS . . . ......... . . ... 34 E. MACHINE VISION

Semiconductor measurement technology: Microelectronic ultrasonic bonding

NASA Technical Reports Server (NTRS)

Harman, G. G. (Editor)

1974-01-01

Information for making high quality ultrasonic wire bonds is presented as well as data to provide a basic understanding of the ultrasonic systems used. The work emphasizes problems and methods of solving them. The required measurement equipment is first introduced. This is followed by procedures and techniques used in setting up a bonding machine, and then various machine- or operator-induced reliability problems are discussed. The characterization of the ultrasonic system and its problems are followed by in-process bonding studies and work on the ultrasonic bonding (welding) mechanism. The report concludes with a discussion of various effects of bond geometry and wire metallurgical characteristics. Where appropriate, the latest, most accurate value of a particular measurement has been substituted for an earlier reported one.

Development of a machine vision system for automated structural assembly

NASA Technical Reports Server (NTRS)

Sydow, P. Daniel; Cooper, Eric G.

1992-01-01

Research is being conducted at the LaRC to develop a telerobotic assembly system designed to construct large space truss structures. This research program was initiated within the past several years, and a ground-based test-bed was developed to evaluate and expand the state of the art. Test-bed operations currently use predetermined ('taught') points for truss structural assembly. Total dependence on the use of taught points for joint receptacle capture and strut installation is neither robust nor reliable enough for space operations. Therefore, a machine vision sensor guidance system is being developed to locate and guide the robot to a passive target mounted on the truss joint receptacle. The vision system hardware includes a miniature video camera, passive targets mounted on the joint receptacles, target illumination hardware, and an image processing system. Discrimination of the target from background clutter is accomplished through standard digital processing techniques. Once the target is identified, a pose estimation algorithm is invoked to determine the location, in three-dimensional space, of the target relative to the robots end-effector. Preliminary test results of the vision system in the Automated Structural Assembly Laboratory with a range of lighting and background conditions indicate that it is fully capable of successfully identifying joint receptacle targets throughout the required operational range. Controlled optical bench test results indicate that the system can also provide the pose estimation accuracy to define the target position.

Contraceptive use among migrant women with a history of induced abortion in Finland.

PubMed

Väisänen, Heini; Koponen, Päivikki; Gissler, Mika; Kontula, Osmo

2018-06-25

Women's contraceptive choices may change after an induced abortion, due to contraceptive counselling or a behavioural change prompted by the experience. The effect may vary between women; sociocultural background, for example, may affect their subsequent reproductive choices. We examined whether women's current contraceptive use was differently associated with a history of induced abortion among immigrant groups in Finland (Russian, Kurdish and Somali) and the general Finnish population. We analysed data from two surveys, the Migrant Health and Wellbeing study and the Health 2011 study, linked to the Finnish register of induced abortions. Propensity score weighted logistic regression was used to analyse the data. The likelihood of using contraceptives after an abortion varied depending on women's sociocultural background. A history of induced abortion increased contraceptive use among all groups, except Russian women, in whom there was no effect. The effect was particularly strong for Kurdish women. Sociocultural background was an important determinant of post-abortion contraceptive use. Some immigrants may struggle to navigate the Finnish health care system due to language or literacy issues. Attention should be paid to improving access to family planning among these groups.

Present status of Accelerator-Based BNCT

PubMed Central

Kreiner, Andres Juan; Bergueiro, Javier; Cartelli, Daniel; Baldo, Matias; Castell, Walter; Asoia, Javier Gomez; Padulo, Javier; Suárez Sandín, Juan Carlos; Igarzabal, Marcelo; Erhardt, Julian; Mercuri, Daniel; Valda, Alejandro A.; Minsky, Daniel M.; Debray, Mario E.; Somacal, Hector R.; Capoulat, María Eugenia; Herrera, María S.; del Grosso, Mariela F.; Gagetti, Leonardo; Anzorena, Manuel Suarez; Canepa, Nicolas; Real, Nicolas; Gun, Marcelo; Tacca, Hernán

2016-01-01

Aim This work aims at giving an updated report of the worldwide status of Accelerator-Based BNCT (AB-BNCT). Background There is a generalized perception that the availability of accelerators installed in hospitals, as neutron sources, may be crucial for the advancement of BNCT. Accordingly, in recent years a significant effort has started to develop such machines. Materials and methods A variety of possible charged-particle induced nuclear reactions and the characteristics of the resulting neutron spectra are discussed along with the worldwide activity in suitable accelerator development. Results Endothermic 7Li(p,n)7Be and 9Be(p,n)9B and exothermic 9Be(d,n)10B are compared. In addition to having much better thermo-mechanical properties than Li, Be as a target leads to stable products. This is a significant advantage for a hospital-based facility. 9Be(p,n)9B needs at least 4–5 MeV bombarding energy to have a sufficient yield, while 9Be(d,n)10B can be utilized at about 1.4 MeV, implying the smallest possible accelerator. This reaction operating with a thin target can produce a sufficiently soft spectrum to be viable for AB-BNCT. The machines considered are electrostatic single ended or tandem accelerators or radiofrequency quadrupoles plus drift tube Linacs. Conclusions 7Li(p,n)7Be provides one of the best solutions for the production of epithermal neutron beams for deep-seated tumors. However, a Li-based target poses significant technological challenges. Hence, Be has been considered as an alternative target, both in combination with (p,n) and (d,n) reactions. 9Be(d,n)10B at 1.4 MeV, with a thin target has been shown to be a realistic option for the treatment of deep-seated lesions. PMID:26933390

Effect of Repetition Rate on Femtosecond Laser-Induced Homogenous Microstructures

PubMed Central

Biswas, Sanchari; Karthikeyan, Adya; Kietzig, Anne-Marie

2016-01-01

We report on the effect of repetition rate on the formation and surface texture of the laser induced homogenous microstructures. Different microstructures were micromachined on copper (Cu) and titanium (Ti) using femtosecond pulses at 1 and 10 kHz. We studied the effect of the repetition rate on structure formation by comparing the threshold accumulated pulse (FΣpulse) values and the effect on the surface texture through lacunarity analysis. Machining both metals at low FΣpulse resulted in microstructures with higher lacunarity at 10 kHz compared to 1 kHz. On increasing FΣpulse, the microstructures showed higher lacunarity at 1 kHz. The effect of the repetition rate on the threshold FΣpulse values were, however, considerably different on the two metals. With an increase in repetition rate, we observed a decrease in the threshold FΣpulse on Cu, while on Ti we observed an increase. These differences were successfully allied to the respective material characteristics and the resulting melt dynamics. While machining Ti at 10 kHz, the melt layer induced by one laser pulse persists until the next pulse arrives, acting as a dielectric for the subsequent pulse, thereby increasing FΣpulse. However, on Cu, the melt layer quickly resolidifies and no such dielectric like phase is observed. Our study contributes to the current knowledge on the effect of the repetition rate as an irradiation parameter. PMID:28774143

Technical Reliability Studies. EOS/ESD Technology Abstracts

DTIC Science & Technology

1981-01-01

MECHANISMS MELAMINE MESFETS MICROWAVE MIS 15025 AUTOMATIC MACHINE PRECAUTIONS FOR HOS/OiOS 15006 INSTRUCTIONS FOR INSTALLATION AND...ELIMINATION OF EOS INDUCED SECONDARY FAILURE MECHANISMS 15000 USE OF MELAMINE WORK-SURFACE FOR ESD POTENTIAL BLEED OFF 16141 MICROWAVE NANOSECOND... microwave devices, optoelectronics, and selected nonelectronic parts employed in military, space and commercial applications. In addition, a System

Summarization as the base for text assessment

NASA Astrophysics Data System (ADS)

Karanikolas, Nikitas N.

2015-02-01

We present a model that apply shallow text summarization as a cheap (in resources needed) process for Automatic (machine based) free text answer Assessment (AA). The evaluation of the proposed method induces the inference that the Conventional Assessment (CA, man made assessment of free text answers) does not have an obvious mechanical replacement. However, this is a research challenge.

Parameterization of typhoon-induced ocean cooling using temperature equation and machine learning algorithms: an example of typhoon Soulik (2013)

NASA Astrophysics Data System (ADS)

Wei, Jun; Jiang, Guo-Qing; Liu, Xin

2017-09-01

This study proposed three algorithms that can potentially be used to provide sea surface temperature (SST) conditions for typhoon prediction models. Different from traditional data assimilation approaches, which provide prescribed initial/boundary conditions, our proposed algorithms aim to resolve a flow-dependent SST feedback between growing typhoons and oceans in the future time. Two of these algorithms are based on linear temperature equations (TE-based), and the other is based on an innovative technique involving machine learning (ML-based). The algorithms are then implemented into a Weather Research and Forecasting model for the simulation of typhoon to assess their effectiveness, and the results show significant improvement in simulated storm intensities by including ocean cooling feedback. The TE-based algorithm I considers wind-induced ocean vertical mixing and upwelling processes only, and thus obtained a synoptic and relatively smooth sea surface temperature cooling. The TE-based algorithm II incorporates not only typhoon winds but also ocean information, and thus resolves more cooling features. The ML-based algorithm is based on a neural network, consisting of multiple layers of input variables and neurons, and produces the best estimate of the cooling structure, in terms of its amplitude and position. Sensitivity analysis indicated that the typhoon-induced ocean cooling is a nonlinear process involving interactions of multiple atmospheric and oceanic variables. Therefore, with an appropriate selection of input variables and neuron sizes, the ML-based algorithm appears to be more efficient in prognosing the typhoon-induced ocean cooling and in predicting typhoon intensity than those algorithms based on linear regression methods.

Proton-induced radioactivity in NaI (Tl) scintillation detectors

NASA Technical Reports Server (NTRS)

Fishman, G. J.

1977-01-01

Radioactivity induced by protons in sodium iodide scintillation crystals were calculated and directly measured. These data are useful in determining trapped radiation and cosmic-ray induced, background-counting rates in spaceborne detectors.

Surface Finish and Residual Stresses Induced by Orthogonal Dry Machining of AA7075-T651

PubMed Central

Jomaa, Walid; Songmene, Victor; Bocher, Philippe

2014-01-01

The surface finish was extensively studied in usual machining processes (turning, milling, and drilling). For these processes, the surface finish is strongly influenced by the cutting feed and the tool nose radius. However, a basic understanding of tool/surface finish interaction and residual stress generation has been lacking. This paper aims to investigate the surface finish and residual stresses under the orthogonal cutting since it can provide this information by avoiding the effect of the tool nose radius. The orthogonal machining of AA7075-T651 alloy through a series of cutting experiments was performed under dry conditions. Surface finish was studied using height and amplitude distribution roughness parameters. SEM and EDS were used to analyze surface damage and built-up edge (BUE) formation. An analysis of the surface topography showed that the surface roughness was sensitive to changes in cutting parameters. It was found that the formation of BUE and the interaction between the tool edge and the iron-rich intermetallic particles play a determinant role in controlling the surface finish during dry orthogonal machining of the AA7075-T651 alloy. Hoop stress was predominantly compressive on the surface and tended to be tensile with increased cutting speed. The reverse occurred for the surface axial stress. The smaller the cutting feed, the greater is the effect of cutting speed on both axial and hoop stresses. By controlling the cutting speed and feed, it is possible to generate a benchmark residual stress state and good surface finish using dry machining. PMID:28788534

Laser cutting: influence on morphological and physicochemical properties of polyhydroxybutyrate.

PubMed

Lootz, D; Behrend, D; Kramer, S; Freier, T; Haubold, A; Benkiesser, G; Schmitz, K P; Becher, B

2001-09-01

Polyhydroxybutyrate (PHB) is a biocompatible and resorbable implant material. For these reasons, it has been used for the fabrication of temporary stents, bone plates, nails and screws (Peng et al. Biomaterials 1996;17:685). In some cases, the brittle mechanical properties of PHB homopolymer limit its application. A typical plasticizer, triethylcitrate (TEC), was used to overcome such limitations by making the material more pliable. In the past few years, CO2-laser cutting of PHB was used in the manufacturing of small medical devices such as stents. Embrittlement of plasticized PHB tubes has been observed, after laser machining. Consequently, the physicochemical and morphological properties of laser-processed surfaces and cut edges of plasticized polymer samples were examined to determine the extent of changes in polymer properties as a result of laser machining. These studies included determination of the depth of the laser-induced heat affected zone by polariscopy of thin polymer sections. Molecular weight changes and changes in the TEC content as a function of distance from the laser-cut edge were determined. In a preliminary test, the cellular response to the processed material was investigated by cell culture study of L929 mouse fibroblasts on laser-machined surfaces. The heat-affected zone was readily classified into four different regions with a total depth of about 60 to 100 microm (Klamp, Master Thesis, University of Rostock, 1998). These results correspond well with the chemical analysis and molecular weight measurements. Furthermore, it was found that cells grew preferentially on the laser-machined area. These findings have significant implications for the manufacture of medical implants from PHB by laser machining.

Rejecting Non-MIP-Like Tracks using Boosted Decision Trees with the T2K Pi-Zero Subdetector

NASA Astrophysics Data System (ADS)

Hogan, Matthew; Schwehr, Jacklyn; Cherdack, Daniel; Wilson, Robert; T2K Collaboration

2016-03-01

Tokai-to-Kamioka (T2K) is a long-baseline neutrino experiment with a narrow band energy spectrum peaked at 600 MeV. The Pi-Zero detector (PØD) is a plastic scintillator-based detector located in the off-axis near detector complex 280 meters from the beam origin. It is designed to constrain neutral-current induced π0 production background at the far detector using the water target which is interleaved between scintillator layers. A PØD-based measurement of charged-current (CC) single charged pion (1π+) production on water is being developed which will have expanded phase space coverage as compared to the previous analysis. The signal channel for this analysis, which for T2K is dominated by Δ production, is defined as events that produce a single muon, single charged pion, and any number of nucleons in the final state. The analysis will employ machine learning algorithms to enhance CC1π+ selection by studying topological observables that characterize signal well. Important observables for this analysis are those that discriminate a minimum ionizing particle (MIP) like a muon or pion from a proton at the T2K energies. This work describes the development of a discriminator using Boosted Decision Trees to reject non-MIP-like PØD tracks.

Role of genetic background in induced instability

NASA Technical Reports Server (NTRS)

Kadhim, Munira A.; Nelson, G. A. (Principal Investigator)

2003-01-01

Genomic instability is effectively induced by ionizing radiation. Recently, evidence has accumulated supporting a relationship between genetic background and the radiation-induced genomic instability phenotype. This is possibly due to alterations in proteins responsible for maintenance of genomic integrity or altered oxidative metabolism. Studies in human cell lines, human primary cells, and mouse models have been performed predominantly using high linear energy transfer (LET) radiation, or high doses of low LET radiation. The interplay between genetics, radiation response, and genomic instability has not been fully determined at low doses of low LET radiation. However, recent studies using low doses of low LET radiation suggest that the relationship between genetic background and radiation-induced genomic instability may be more complicated than these same relationships at high LET or high doses of low LET radiation. The complexity of this relationship at low doses of low LET radiation suggests that more of the population may be at risk than previously recognized and may have implications for radiation risk assessment.

Machine Learning

NASA Astrophysics Data System (ADS)

Hoffmann, Achim; Mahidadia, Ashesh

The purpose of this chapter is to present fundamental ideas and techniques of machine learning suitable for the field of this book, i.e., for automated scientific discovery. The chapter focuses on those symbolic machine learning methods, which produce results that are suitable to be interpreted and understood by humans. This is particularly important in the context of automated scientific discovery as the scientific theories to be produced by machines are usually meant to be interpreted by humans. This chapter contains some of the most influential ideas and concepts in machine learning research to give the reader a basic insight into the field. After the introduction in Sect. 1, general ideas of how learning problems can be framed are given in Sect. 2. The section provides useful perspectives to better understand what learning algorithms actually do. Section 3 presents the Version space model which is an early learning algorithm as well as a conceptual framework, that provides important insight into the general mechanisms behind most learning algorithms. In section 4, a family of learning algorithms, the AQ family for learning classification rules is presented. The AQ family belongs to the early approaches in machine learning. The next, Sect. 5 presents the basic principles of decision tree learners. Decision tree learners belong to the most influential class of inductive learning algorithms today. Finally, a more recent group of learning systems are presented in Sect. 6, which learn relational concepts within the framework of logic programming. This is a particularly interesting group of learning systems since the framework allows also to incorporate background knowledge which may assist in generalisation. Section 7 discusses Association Rules - a technique that comes from the related field of Data mining. Section 8 presents the basic idea of the Naive Bayesian Classifier. While this is a very popular learning technique, the learning result is not well suited for human comprehension as it is essentially a large collection of probability values. In Sect. 9, we present a generic method for improving accuracy of a given learner by generatingmultiple classifiers using variations of the training data. While this works well in most cases, the resulting classifiers have significantly increased complexity and, hence, tend to destroy the human readability of the learning result that a single learner may produce. Section 10 contains a summary, mentions briefly other techniques not discussed in this chapter and presents outlook on the potential of machine learning in the future.

State but not District Nutrition Policies Are Associated with Less Junk Food in Vending Machines and School Stores in US Public Schools

PubMed Central

KUBIK, MARTHA Y.; WALL, MELANIE; SHEN, LIJUAN; NANNEY, MARILYN S.; NELSON, TOBEN F.; LASKA, MELISSA N.; STORY, MARY

2012-01-01

Background Policy that targets the school food environment has been advanced as one way to increase the availability of healthy food at schools and healthy food choice by students. Although both state- and district-level policy initiatives have focused on school nutrition standards, it remains to be seen whether these policies translate into healthy food practices at the school level, where student behavior will be impacted. Objective To examine whether state- and district-level nutrition policies addressing junk food in school vending machines and school stores were associated with less junk food in school vending machines and school stores. Junk food was defined as foods and beverages with low nutrient density that provide calories primarily through fats and added sugars. Design A cross-sectional study design was used to assess self-report data collected by computer-assisted telephone interviews or self-administered mail questionnaires from state-, district-, and school-level respondents participating in the School Health Policies and Programs Study 2006. The School Health Policies and Programs Study, administered every 6 years since 1994 by the Centers for Disease Control and Prevention, is considered the largest, most comprehensive assessment of school health policies and programs in the United States. Subjects/setting A nationally representative sample (n = 563) of public elementary, middle, and high schools was studied. Statistical analysis Logistic regression adjusted for school characteristics, sampling weights, and clustering was used to analyze data. Policies were assessed for strength (required, recommended, neither required nor recommended prohibiting junk food) and whether strength was similar for school vending machines and school stores. Results School vending machines and school stores were more prevalent in high schools (93%) than middle (84%) and elementary (30%) schools. For state policies, elementary schools that required prohibiting junk food in school vending machines and school stores offered less junk food than elementary schools that neither required nor recommended prohibiting junk food (13% vs 37%; P = 0.006). Middle schools that required prohibiting junk food in vending machines and school stores offered less junk food than middle schools that recommended prohibiting junk food (71% vs 87%; P = 0.07). Similar associations were not evident for district-level polices or high schools. Conclusions Policy may be an effective tool to decrease junk food in schools, particularly in elementary and middle schools. PMID:20630161

Interaction with Machine Improvisation

NASA Astrophysics Data System (ADS)

Assayag, Gerard; Bloch, George; Cont, Arshia; Dubnov, Shlomo

We describe two multi-agent architectures for an improvisation oriented musician-machine interaction systems that learn in real time from human performers. The improvisation kernel is based on sequence modeling and statistical learning. We present two frameworks of interaction with this kernel. In the first, the stylistic interaction is guided by a human operator in front of an interactive computer environment. In the second framework, the stylistic interaction is delegated to machine intelligence and therefore, knowledge propagation and decision are taken care of by the computer alone. The first framework involves a hybrid architecture using two popular composition/performance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The second framework shares the same representational schemes with the first but uses an Active Learning architecture based on collaborative, competitive and memory-based learning to handle stylistic interactions. Both systems are capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvisation practices, the statistical modelling tools and the concurrent agent architecture are presented. Then, an Active Learning scheme is described and considered in terms of using different improvisation regimes for improvisation planning. Finally, we provide more details about the different system implementations and describe several performances with the system.

Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation

NASA Astrophysics Data System (ADS)

Zhou, XueFei

2018-04-01

With the development of computer technology, the applications of machine learning are more and more extensive. And machine learning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks (CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machine learning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.

The effects of alcohol expectancy and intake on slot machine gambling behavior.

PubMed

Sagoe, Dominic; Mentzoni, Rune Aune; Leino, Tony; Molde, Helge; Haga, Sondre; Gjernes, Mikjel Fredericson; Hanss, Daniel; Pallesen, Ståle

2017-06-01

Background and aims Although alcohol intake and gambling often co-occur in related venues, there is conflicting evidence regarding the effects of alcohol expectancy and intake on gambling behavior. We therefore conducted an experimental investigation of the effects of alcohol expectancy and intake on slot machine gambling behavior. Methods Participants were 184 (females = 94) individuals [age range: 18-40 (mean = 21.9) years] randomized to four independent conditions differing in information/expectancy about beverage (told they received either alcohol or placebo) and beverage intake [actually ingesting low (target blood alcohol concentration [BAC] < 0.40 mg/L) vs. moderate (target BAC > 0.40 mg/L; ≈0.80 mg/L) amounts of alcohol]. All participants completed self-report questionnaires assessing demographic variables, subjective intoxication, alcohol effects (stimulant and sedative), and gambling factors (behavior and problems, evaluation, and beliefs). Participants also gambled on a simulated slot machine. Results A significant main effect of beverage intake on subjective intoxication and alcohol effects was detected as expected. No significant main or interaction effects were detected for number of gambling sessions, bet size and variation, remaining credits at termination, reaction time, and game evaluation. Conclusion Alcohol expectancy and intake do not affect gambling persistence, dissipation of funds, reaction time, or gambling enjoyment.

Collaborative human-machine analysis using a controlled natural language

NASA Astrophysics Data System (ADS)

Mott, David H.; Shemanski, Donald R.; Giammanco, Cheryl; Braines, Dave

2015-05-01

A key aspect of an analyst's task in providing relevant information from data is the reasoning about the implications of that data, in order to build a picture of the real world situation. This requires human cognition, based upon domain knowledge about individuals, events and environmental conditions. For a computer system to collaborate with an analyst, it must be capable of following a similar reasoning process to that of the analyst. We describe ITA Controlled English (CE), a subset of English to represent analyst's domain knowledge and reasoning, in a form that it is understandable by both analyst and machine. CE can be used to express domain rules, background data, assumptions and inferred conclusions, thus supporting human-machine interaction. A CE reasoning and modeling system can perform inferences from the data and provide the user with conclusions together with their rationale. We present a logical problem called the "Analysis Game", used for training analysts, which presents "analytic pitfalls" inherent in many problems. We explore an iterative approach to its representation in CE, where a person can develop an understanding of the problem solution by incremental construction of relevant concepts and rules. We discuss how such interactions might occur, and propose that such techniques could lead to better collaborative tools to assist the analyst and avoid the "pitfalls".

Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

PubMed Central

Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly

2013-01-01

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652

Biotea: RDFizing PubMed Central in support for the paper as an interface to the Web of Data

PubMed Central

2013-01-01

Background The World Wide Web has become a dissemination platform for scientific and non-scientific publications. However, most of the information remains locked up in discrete documents that are not always interconnected or machine-readable. The connectivity tissue provided by RDF technology has not yet been widely used to support the generation of self-describing, machine-readable documents. Results In this paper, we present our approach to the generation of self-describing machine-readable scholarly documents. We understand the scientific document as an entry point and interface to the Web of Data. We have semantically processed the full-text, open-access subset of PubMed Central. Our RDF model and resulting dataset make extensive use of existing ontologies and semantic enrichment services. We expose our model, services, prototype, and datasets at http://biotea.idiginfo.org/ Conclusions The semantic processing of biomedical literature presented in this paper embeds documents within the Web of Data and facilitates the execution of concept-based queries against the entire digital library. Our approach delivers a flexible and adaptable set of tools for metadata enrichment and semantic processing of biomedical documents. Our model delivers a semantically rich and highly interconnected dataset with self-describing content so that software can make effective use of it. PMID:23734622

3D FE simulation of semi-finishing machining of Ti6Al4V additively manufactured by direct metal laser sintering

NASA Astrophysics Data System (ADS)

Imbrogno, Stano; Rinaldi, Sergio; Raso, Antonio; Bordin, Alberto; Bruschi, Stefania; Umbrello, Domenico

2018-05-01

The Additive Manufacturing techniques are gaining more and more interest in various industrial fields due to the possibility of drastically reduce the material waste during the production processes, revolutionizing the standard scheme and strategies of the manufacturing processes. However, the metal parts shape produced, frequently do not satisfy the tolerances as well as the surface quality requirements. During the design phase, the finite element simulation results a fundamental tool to help the engineers in the correct decision of the most suitable process parameters, especially in manufacturing processes, in order to produce products of high quality. The aim of this work is to develop a 3D finite element model of semi-finishing turning operation of Ti6Al4V, produced via Direct Metal Laser Sintering (DMLS). A customized user sub-routine was built-up in order to model the mechanical behavior of the material under machining operations to predict the main fundamental variables as cutting forces and temperature. Moreover, the machining induced alterations are also studied by the finite element model developed.

Mining protein function from text using term-based support vector machines

PubMed Central

Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

2005-01-01

Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques

PubMed Central

Macyszyn, Luke; Akbari, Hamed; Pisapia, Jared M.; Da, Xiao; Attiah, Mark; Pigrish, Vadim; Bi, Yingtao; Pal, Sharmistha; Davuluri, Ramana V.; Roccograndi, Laura; Dahmane, Nadia; Martinez-Lage, Maria; Biros, George; Wolf, Ronald L.; Bilello, Michel; O'Rourke, Donald M.; Davatzikos, Christos

2016-01-01

Background MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB). Methods One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients. Results Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy. Conclusions By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood–brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients. PMID:26188015

Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence

PubMed Central

Ahn, Woo-Young; Vassileva, Jasmin

2016-01-01

Background Recent animal and human studies reveal distinct cognitive and neurobiological differences between opiate and stimulant addictions; however, our understanding of the common and specific effects of these two classes of drugs remains limited due to the high rates of polysubstance-dependence among drug users. Methods The goal of the current study was to identify multivariate substance-specific markers classifying heroin dependence (HD) and amphetamine dependence (AD), by using machine-learning approaches. Participants included 39 amphetamine mono-dependent, 44 heroin mono-dependent, 58 polysubstance dependent, and 81 non-substance dependent individuals. The majority of substance dependent participants were in protracted abstinence. We used demographic, personality (trait impulsivity, trait psychopathy, aggression, sensation seeking), psychiatric (attention deficit hyperactivity disorder, conduct disorder, antisocial personality disorder, psychopathy, anxiety, depression), and neurocognitive impulsivity measures (Delay Discounting, Go/No-Go, Stop Signal, Immediate Memory, Balloon Analogue Risk, Cambridge Gambling, and Iowa Gambling tasks) as predictors in a machine-learning algorithm. Results The machine-learning approach revealed substance-specific multivariate profiles that classified HD and AD in new samples with high degree of accuracy. Out of 54 predictors, psychopathy was the only classifier common to both types of addiction. Important dissociations emerged between factors classifying HD and AD, which often showed opposite patterns among individuals with HD and AD. Conclusions These results suggest that different mechanisms may underlie HD and AD, challenging the unitary account of drug addiction. This line of work may shed light on the development of standardized and cost-efficient clinical diagnostic tests and facilitate the development of individualized prevention and intervention programs for HD and AD. PMID:26905209

Automated Assessment of Cognitive Health Using Smart Home Technologies

PubMed Central

Dawadi, Prafulla N.; Cook, Diane J.; Schmitter-Edgecombe, Maureen; Parsey, Carolyn

2014-01-01

BACKGROUND The goal of this work is to develop intelligent systems to monitor the well being of individuals in their home environments. OBJECTIVE This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. METHODS This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. RESULTS Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve = 0.80, g-mean = 0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. CONCLUSIONS The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained. PMID:23949177

Are School Vending Machines Loaded with Calories and Fat: An Assessment of 106 Middle and High Schools

PubMed Central

PASCH, KERYN E.; LYTLE, LESLIE A.; SAMUELSON, ANNE C.; FARBAKHSH, KIAN; KUBIK, MARTHA Y.; PATNODE, CARRIE D.

2013-01-01

Background The purpose of this study was to describe the extent to which vending offerings in 106 schools in the St. Paul-Minneapolis, Minnesota metropolitan area met criteria for types of beverages, fat and calories based on selected criteria offered by the Institute of Medicine. Methods Schools where youth participants were attending for the 2006-2007 school year were identified and invited to participate in the study (n=143); 81% of schools (n=116) agreed to participate. Results Of the 116 schools, 106 had vending machines. Across schools with vending machines (n=106), 5085 food and 8442 beverage items were offered. Overall, only 18% of beverage items met criteria for calories and type of beverage; significantly more items in public schools met the criteria as compared to private schools (19% vs. 12%; p<0.01). This difference was also significant for high schools as compared to middle schools (18% vs. 22%; p<0.01). For food items, 41% met calorie criteria, 45% met fat criteria, and 22% met both fat and calorie criteria. Significantly more food items met both criteria in public than private schools (22% vs. 18%; p=0.01), while high schools (22%) and middle schools (21%) were similar. A very small proportion of foods (<5%) would have met the full criteria suggested by the Institute of Medicine for competitive foods.. Conclusion Overall, foods and beverages offered in vending machines continue to be high in fat and calories. Public schools are doing a slightly better job of providing healthy foods as compared to private schools. PMID:21392013

A cloud platform for remote diagnosis of breast cancer in mammography by fusion of machine and human intelligence

NASA Astrophysics Data System (ADS)

Jiang, Guodong; Fan, Ming; Li, Lihua

2016-03-01

Mammography is the gold standard for breast cancer screening, reducing mortality by about 30%. The application of a computer-aided detection (CAD) system to assist a single radiologist is important to further improve mammographic sensitivity for breast cancer detection. In this study, a design and realization of the prototype for remote diagnosis system in mammography based on cloud platform were proposed. To build this system, technologies were utilized including medical image information construction, cloud infrastructure and human-machine diagnosis model. Specifically, on one hand, web platform for remote diagnosis was established by J2EE web technology. Moreover, background design was realized through Hadoop open-source framework. On the other hand, storage system was built up with Hadoop distributed file system (HDFS) technology which enables users to easily develop and run on massive data application, and give full play to the advantages of cloud computing which is characterized by high efficiency, scalability and low cost. In addition, the CAD system was realized through MapReduce frame. The diagnosis module in this system implemented the algorithms of fusion of machine and human intelligence. Specifically, we combined results of diagnoses from doctors' experience and traditional CAD by using the man-machine intelligent fusion model based on Alpha-Integration and multi-agent algorithm. Finally, the applications on different levels of this system in the platform were also discussed. This diagnosis system will have great importance for the balanced health resource, lower medical expense and improvement of accuracy of diagnosis in basic medical institutes.

The association between state bans on soda only and adolescent substitution with other sugar-sweetened beverages: a cross-sectional study

PubMed Central

2015-01-01

Background Across the United States, many states have actively banned the sale of soda in high schools, and evidence suggests that students’ in-school access to soda has declined as a result. However, schools may be substituting soda with other sugar-sweetened beverages (SSBs), and national trends indicate that adolescents are consuming more sports drinks and energy drinks. This study examined whether students consumed more non-soda SSBs in states that banned the sale of soda in school. Methods Student data on consumption of various SSBs and in-school access to vending machines that sold SSBs were obtained from the National Youth Physical Activity and Nutrition Study (NYPANS), conducted in 2010. Student data were linked to state laws regarding the sale of soda in school in 2010. Students were cross-classified based on their access to vending machines and whether their state banned soda in school, creating 4 comparison groups. Zero-inflated negative binomial models were used to compare these 4 groups with respect to students’ self-reported consumption of diet soda, sports drinks, energy drinks, coffee/tea, or other SSBs. Students who had access to vending machines in a state that did not ban soda were the reference group. Models were adjusted for race/ethnicity, sex, grade, home food access, state median income, and U.S. Census region. Results Students consumed more servings of sports drinks, energy drinks, coffee/tea, and other SSBs if they resided in a state that banned soda in school but attended a school with vending machines that sold other SSBs. Similar results were observed where schools did not have vending machines but the state allowed soda to be sold in school. Intake was generally not elevated where both states and schools limited SSB availability – i.e., states banned soda and schools did not have SSB vending machines. Conclusion State laws that ban soda but allow other SSBs may lead students to substitute other non-soda SSBs. Additional longitudinal research is needed to confirm this. Elevated SSB intake was not observed when both states and schools took steps to remove SSBs from school. PMID:26221969

Muon-Induced Neutrons Do Not Explain the DAMA Data

NASA Astrophysics Data System (ADS)

Klinger, J.; Kudryavtsev, V. A.

2015-04-01

We present an accurate model of the muon-induced background in the DAMA/LIBRA experiment. Our work challenges proposed mechanisms which seek to explain the observed DAMA signal modulation with muon-induced backgrounds. Muon generation and transport are performed using the MUSIC /MUSUN code, and subsequent interactions in the vicinity of the DAMA detector cavern are simulated with Geant4. We estimate the total muon-induced neutron flux in the detector cavern to be Φnν=1.0 ×10-9 cm-2 s-1 . We predict 3.49 ×10-5 counts /day /kg /keV , which accounts for less than 0.3% of the DAMA signal modulation amplitude.

Evidence for the associated production of a W boson and a top quark at ATLAS

NASA Astrophysics Data System (ADS)

Koll, James

This thesis discusses a search for the Standard Model single top Wt-channel process. An analysis has been performed searching for the Wt-channel process using 4.7 fb-1 of integrated luminosity collected with the ATLAS detector at the Large Hadron Collider. A boosted decision tree is trained using machine learning techniques to increase the separation between signal and background. A profile likelihood fit is used to measure the cross-section of the Wt-channel process at sigma(pp → Wt + X) = 16.8 +/-2.9 (stat) +/- 4.9(syst) pb, consistent with the Standard Model prediction. This fit is also used to generate pseudoexperiments to calculate the significance, finding an observed (expected) 3.3 sigma (3.4 sigma) excess over background.

Mars Science Laboratory CHIMRA/IC/DRT Flight Software for Sample Acquisition and Processing

NASA Technical Reports Server (NTRS)

Kim, Won S.; Leger, Chris; Carsten, Joseph; Helmick, Daniel; Kuhn, Stephen; Redick, Richard; Trujillo, Diana

2013-01-01

The design methodologies of using sequence diagrams, multi-process functional flow diagrams, and hierarchical state machines were successfully applied in designing three MSL (Mars Science Laboratory) flight software modules responsible for handling actuator motions of the CHIMRA (Collection and Handling for In Situ Martian Rock Analysis), IC (Inlet Covers), and DRT (Dust Removal Tool) mechanisms. The methodologies were essential to specify complex interactions with other modules, support concurrent foreground and background motions, and handle various fault protections. Studying task scenarios with multi-process functional flow diagrams yielded great insight to overall design perspectives. Since the three modules require three different levels of background motion support, the methodologies presented in this paper provide an excellent comparison. All three modules are fully operational in flight.

Skierarchy: Extending the Power of Crowdsourcing Using a Hierarchy of Domain Experts, Crowd and Machine Learning

DTIC Science & Technology

2012-11-01

college background and good reading comprehension skills in English, and bring to them to the office space to work for us full-time on micro-tasks. This...reasonable reading comprehension skills in English. The expert spent only 1/3rd the time as each member of the crowd in the entire annotation process...3. DATES COVERED 00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Skierarchy: Extending the Power of Crowdsourcing Using a Hierarchy of Domain

16. INTERIOR VIEW OF HILLMAN FAN HOUSE ENGINE ROOM LOOKING ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

16. INTERIOR VIEW OF HILLMAN FAN HOUSE ENGINE ROOM LOOKING EAST This overview of the 1883 Pittston Engine and Machine Company steam engine includes the flywheel and pillowblock in the foreground, with the shaft and cylinder in the background. The engine is a horizontal, slide valve type of 30 inch bore and 60 inch stroke that turned the fan at 49 revolutions per minute. - Dorrance Colliery Fan Complex, South side of Susquehanna River at Route 115 & Riechard Street, Wilkes-Barre, Luzerne County, PA

LOOKING WEST FROM THE TOP OF THE WATER TOWER AT ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

LOOKING WEST FROM THE TOP OF THE WATER TOWER AT ROOFTOPS OF INDUSTRIAL SHEDS. BUILDINGS IN THE FOREGROUND INCLUDE THE SHIPPING BAYS OF THE STRUCTURAL MILL AND MAIN ROLL SHOP. BUILDINGS IN THE MIDDLE GROUND INCLUDE PRESS SHOP NO. 2, CHIPPING YARD, BLACKSMITH SHOP, AND MACHINE SHOP NO. 1. THE TALLEST STRUCTURE IS THE VERTICAL FURNACE BUILDING, AND OPEN HEARTH NO. 5 IS IN THE EXTREME BACKGROUND. - U.S. Steel Homestead Works, Structural Mill, Along Monongahela River, Homestead, Allegheny County, PA

Influence of an O2 background gas on the composition and kinetic energies of species in laser induced La0.4Ca0.6MnO3 plasmas

NASA Astrophysics Data System (ADS)

Chen, Jikun; Stender, Dieter; Bator, Matthias; Schneider, Christof W.; Lippert, Thomas; Wokaun, Alexander

2013-08-01

Oxygen is one of the most commonly used background gases for pulsed laser deposition of oxide thin films. In this work the properties of a 308 nm laser-induced La0.4Ca0.6MnO3 plasma were analyzed using a quadrupole mass spectrometer combined with an energy analyzer, to investigate the interaction between the various plasma species and the background gas. The composition and kinetic energies of the plasma species were compared in vacuum and an O2 background gas at different pressures. It has been observed that the O2 background gas decreases the kinetic energy of the positively charged atomic plasma species. In addition, the interaction with the O2 background gas causes the generation of positive diatomic oxide species of LaO+, CaO+ and MnO+. The amount of negatively charged diatomic or tri-atomic oxide species decreases in the O2 background compared to vacuum, while the amount of O2- increases strongly.

Body-Machine Interfaces after Spinal Cord Injury: Rehabilitation and Brain Plasticity.

PubMed

Seáñez-González, Ismael; Pierella, Camilla; Farshchiansadegh, Ali; Thorp, Elias B; Wang, Xue; Parrish, Todd; Mussa-Ivaldi, Ferdinando A

2016-12-19

The purpose of this study was to identify rehabilitative effects and changes in white matter microstructure in people with high-level spinal cord injury following bilateral upper-extremity motor skill training. Five subjects with high-level (C5-C6) spinal cord injury (SCI) performed five visuo-spatial motor training tasks over 12 sessions (2-3 sessions per week). Subjects controlled a two-dimensional cursor with bilateral simultaneous movements of the shoulders using a non-invasive inertial measurement unit-based body-machine interface. Subjects' upper-body ability was evaluated before the start, in the middle and a day after the completion of training. MR imaging data were acquired before the start and within two days of the completion of training. Subjects learned to use upper-body movements that survived the injury to control the body-machine interface and improved their performance with practice. Motor training increased Manual Muscle Test scores and the isometric force of subjects' shoulders and upper arms. Moreover, motor training increased fractional anisotropy (FA) values in the cingulum of the left hemisphere by 6.02% on average, indicating localized white matter microstructure changes induced by activity-dependent modulation of axon diameter, myelin thickness or axon number. This body-machine interface may serve as a platform to develop a new generation of assistive-rehabilitative devices that promote the use of, and that re-strengthen, the motor and sensory functions that survived the injury.

On-Command Force and Torque Impeding Devices (OC-FTID) Using ERF

NASA Technical Reports Server (NTRS)

Bar-Cohen, Yoseph; Badescu, Mircea; Sherrit, Stewart

2014-01-01

Various machines have been developed to address the need for countermeasures of bone and muscle deterioration when humans operate over extended time in space. Even though these machines are in use, each of them has many limitations that need to be addressed in an effort to prepare for human missions to distant bodies in the solar system. An exercise exoskeleton was conceived that performs on-demand resistivity by inducing force and torque impedance via ElectroRheological Fluid (ERF). The resistive elements consist of pistons that are moving inside ERF-filled cylinders or a donut-shaped cavity, and the fluid flows through the piston when the piston is moved. Tests of the operation of ERF against load showed the feasibility of this approach. ERF properties of high yield stress, low current density, and fast response (less than one millisecond) offer essential characteristics for the construction of the exoskeleton. ERFs can apply very high electrically controlled resistive forces or torque while their size (weight and geometric parameters) can be very small. Their long life and ability to function in a wide temperature range (from -40 to 200 C) allows for their use in extreme environments. ERFs are also nonabrasive, non-toxic, and nonpolluting (meet health and safety regulations). The technology is applicable as a compact exercise machine for astronauts' countermeasure of microgravity, an exercise machine for sport, or as a device for rehabilitation of patients with limb issues.

FLUXCOM - Overview and First Synthesis

NASA Astrophysics Data System (ADS)

Jung, M.; Ichii, K.; Tramontana, G.; Camps-Valls, G.; Schwalm, C. R.; Papale, D.; Reichstein, M.; Gans, F.; Weber, U.

2015-12-01

We present a community effort aiming at generating an ensemble of global gridded flux products by upscaling FLUXNET data using an array of different machine learning methods including regression/model tree ensembles, neural networks, and kernel machines. We produced products for gross primary production, terrestrial ecosystem respiration, net ecosystem exchange, latent heat, sensible heat, and net radiation for two experimental protocols: 1) at a high spatial and 8-daily temporal resolution (5 arc-minute) using only remote sensing based inputs for the MODIS era; 2) 30 year records of daily, 0.5 degree spatial resolution by incorporating meteorological driver data. Within each set-up, all machine learning methods were trained with the same input data for carbon and energy fluxes respectively. Sets of input driver variables were derived using an extensive formal variable selection exercise. The performance of the extrapolation capacities of the approaches is assessed with a fully internally consistent cross-validation. We perform cross-consistency checks of the gridded flux products with independent data streams from atmospheric inversions (NEE), sun-induced fluorescence (GPP), catchment water balances (LE, H), satellite products (Rn), and process-models. We analyze the uncertainties of the gridded flux products and for example provide a breakdown of the uncertainty of mean annual GPP originating from different machine learning methods, different climate input data sets, and different flux partitioning methods. The FLUXCOM archive will provide an unprecedented source of information for water, energy, and carbon cycle studies.

Tempest gas turbine extends EGT product line

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

Chellini, R.

With the introduction of the 7.8 MW (mechanical output) Tempest gas turbine, ECT has extended the company`s line of its small industrial turbines. The new Tempest machine, featuring a 7.5 MW electric output and a 33% thermal efficiency, ranks above the company`s single-shaft Typhoon gas turbine, rated 3.2 and 4.9 MW, and the 6.3 MW Tornado gas turbine. All three machines are well-suited for use in combined heat and power (CHP) plants, as demonstrated by the fact that close to 50% of the 150 Typhoon units sold are for CHP applications. This experience has induced EGT, of Lincoln, England, tomore » announce the introduction of the new gas turbine prior to completion of the testing program. The present single-shaft machine is expected to be used mainly for industrial trial cogeneration. This market segment, covering the needs of paper mills, hospitals, chemical plants, ceramic industry, etc., is a typical local market. Cogeneration plants are engineered according to local needs and have to be assisted by local organizations. For this reason, to efficiently cover the world market, EGT has selected a number of associates that will receive from Lincoln completely engineered machine packages and will engineer the cogeneration system according to custom requirements. These partners will also assist the customer and dispose locally of the spares required for maintenance operations.« less

A smart-hose for concrete displacing booms

NASA Astrophysics Data System (ADS)

Ripamonti, Francesco; Bucca, Giuseppe; Fava, Victor; Resta, Ferruccio

2016-04-01

During the last years, continuum robots have been used in many applications. They are smart structures with continuous curving, similar to a worm or an elephant trunk, characterized by a very high number of sub-actuated degrees of freedom (dof). They need a robust control system, aiming at both positioning the robot and suppressing induced vibrations. The idea is to adopt such a robot on a construction machine for the concrete distribution, substituting the reinforced rubber hose with the robotic smart solution. Particular attention has been paid to a control strategy able to reduce vibrations induced by the pumping procedure.

Ultraviolet radiation induces dose-dependent pigment dispersion in crustacean chromatophores.

PubMed

Gouveia, Glauce Ribeiro; Lopes, Thaís Martins; Neves, Carla Amorim; Nery, Luiz Eduardo Maia; Trindade, Gilma Santos

2004-10-01

Pigment dispersion in chromatophores as a response to UV radiation was investigated in two species of crustaceans, the crab Chasmagnathus granulata and the shrimp Palaemonetes argentinus. Eyestalkless crabs and shrimps maintained on either a black or a white background were irradiated with different UV bands. In eyestalkless crabs the significant minimal effective dose inducing pigment dispersion was 0.42 J/cm(2) for UVA and 2.15 J/cm(2) for UVB. Maximal response was achieved with 10.0 J/cm(2) UVA and 8.6 J/cm(2) UVB. UVA was more effective than UVB in inducing pigment dispersion. Soon after UV exposure, melanophores once again reached the initial stage of pigment aggregation after 45 min. Aggregated erythrophores of shrimps adapted to a white background showed significant pigment dispersion with 2.5 J/cm(2) UVA and 0.29 J/cm(2) UVC. Dispersed erythrophores of shrimps adapted to a black background did not show any significant response to UVA, UVB or UVC radiation. UVB did not induce any significant pigment dispersion in shrimps adapted to either a white or a black background. As opposed to the tanning response, which only protects against future UV exposure, the pigment dispersion response could be an important agent protecting against the harmful effects of UV radiation exposure.

Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis

PubMed Central

2013-01-01

Background Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks, and further, the experimental methods for identifying PPIs are both time-consuming and expensive. Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs. Results We present here a novel hierarchical PCA-EELM (principal component analysis-ensemble extreme learning machine) model to predict protein-protein interactions only using the information of protein sequences. In the proposed method, 11188 protein pairs retrieved from the DIP database were encoded into feature vectors by using four kinds of protein sequences information. Focusing on dimension reduction, an effective feature extraction method PCA was then employed to construct the most discriminative new feature set. Finally, multiple extreme learning machines were trained and then aggregated into a consensus classifier by majority voting. The ensembling of extreme learning machine removes the dependence of results on initial random weights and improves the prediction performance. Conclusions When performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 87.00% prediction accuracy with 86.15% sensitivity at the precision of 87.59%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Experimental results demonstrate that proposed PCA-EELM outperforms the SVM method by 5-fold cross-validation. Besides, PCA-EELM performs faster than PCA-SVM based method. Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent performance and less time. PMID:23815620

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 complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin. PMID:26848571

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review

PubMed Central

Mendes, Emilia; Berglund, Johan; Anderberg, Peter

2017-01-01

Background Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. Objective The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques. Method To achieve our goal we carried out a systematic literature review, in which three large databases—Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables. Results In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer’s disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable. Conclusions Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML techniques, given studies’ different contexts. PMID:28662070

Lasers for vascular lesions: standard guidelines of care.

PubMed

Srinivas, C R; Kumaresan, M

2011-01-01

Lasers are a good therapeutic tool for congenital and acquired vascular lesions. Technological advances in lasers have reduced the adverse effects and increased the efficacy. MACHINES: Among the various lasers used for treating vascular lesions, pulsed dye laser (PDL) has the best efficacy and safety data. The other machines that are widely available are Nd:YAG laser and intense pulse light (IPL). RATIONALE AND SCOPE OF GUIDELINE: Much variation exists in different machines and techniques, and therefore, establishing standard guidelines has limitations. The guidelines recommended here indicate minimum standards of care for lasers on vascular lesions based on current evidence. Laser may be administered by a dermatologist, who has received adequate background training in lasers during post-graduation or later at a center that provides education and training in lasers, or in focused workshops, which provide such trainings. He/she should have adequate knowledge of the lesions being treated, machines, parameters, cooling systems, and aftercare. The procedure may be performed in the physician's minor procedure room with adequate laser safety measures. PWS, hemangioma, facial telangiectasia, rosacea, spider angioma, pyogenic granuloma, venous lakes, leg veins. Absolute: Active local infection, photo-aggravated skin diseases, and medical conditions. Relative: Unstable vitiligo, psoriasis, keloid and keloidal tendencies, patient on isotretinoin, patient who is not cooperative or has unrealistic expectation. Patient selection should be done after detailed counseling with respect to the course of lesions, different treatment options, possible results, cost, need for multiple treatments, and possible postoperative complications. TREATMENT SESSIONS: The number of treatments per lesion varies from 2 to 12 or more at 6-8 week intervals. All lesions may not clear completely even after multiple sessions in many cases. Hence, a realistic expectation and proper counseling is very important. Laser parameters vary with area, type of lesion, skin color, depth of the lesion, and machine used. A test spot may be performed to determine individual specifications. Pain, edema, purpura, bleeding, scarring, postinflammatory hyperpigmentation/hypopigmentation, and atrophy changes.

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning.

PubMed

Zhao, Jonathan Z L; Mucaki, Eliseos J; Rogan, Peter K

2018-01-01

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2 ,  PRKDC , TPP2 , PTPRE , and GADD45A ) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2 ,  CD8A ,  TALDO1 ,  PCNA ,  EIF4G2 ,  LCN2 ,  CDKN1A ,  PRKCH ,  ENO1 ,  and PPM1D ) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

In Silico Identification Software (ISIS): A Machine Learning Approach to Tandem Mass Spectral Identification of Lipids

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

Kangas, Lars J.; Metz, Thomas O.; Isaac, Georgis

2012-05-15

Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissocia-tion tandem mass spectrometry. A preliminary test of the algorithm with 45 lipidsmore » from a subset of lipid classes shows both high sensitivity and specificity.« less

Analysis of Surface and Subsurface Damage Morphology in Rotary Ultrasonic Machining of BK7 Glass

NASA Astrophysics Data System (ADS)

Hong-xiang, Wang; Chu, Wang; Jun-liang, Liu; Shi, Gao; Wen-Jie, Zhai

2017-11-01

This paper investigates the formation process of surface/subsurface damage in the rotary ultrasonic machining of BK7 glass. The results show that during the milling using the end face of the tool, the cutting depth and the residual height between the abrasive grains constantly change with the high-frequency vibration, generating lots of cracks on both sides of the scratches. The high-frequency vibration accelerates the chips falling from the surface, so that the chips and thermal damage are reduced, causing the grinding surface quality better. A plastic deformation area is formed during the grinding, due to the non-uniform cutting force on the material surface, and the residual stress is produced in the deformation area, inducing the median/lateral cracks.

Machine vision extracted plant movement for early detection of plant water stress.

PubMed

Kacira, M; Ling, P P; Short, T H

2002-01-01

A methodology was established for early, non-contact, and quantitative detection of plant water stress with machine vision extracted plant features. Top-projected canopy area (TPCA) of the plants was extracted from plant images using image-processing techniques. Water stress induced plant movement was decoupled from plant diurnal movement and plant growth using coefficient of relative variation of TPCA (CRV[TPCA)] and was found to be an effective marker for water stress detection. Threshold value of CRV(TPCA) as an indicator of water stress was determined by a parametric approach. The effectiveness of the sensing technique was evaluated against the timing of stress detection by an operator. Results of this study suggested that plant water stress detection using projected canopy area based features of the plants was feasible.

Active vibration control of structures undergoing bending vibrations

NASA Technical Reports Server (NTRS)

Pla, Frederic G. (Inventor); Rajiyah, Harindra (Inventor)

1995-01-01

An active vibration control subassembly for a structure (such as a jet engine duct or a washing machine panel) undergoing bending vibrations caused by a source (such as the clothes agitator of the washing machine) independent of the subassembly. A piezoceramic actuator plate is vibratable by an applied electric AC signal. The plate is connected to the structure such that vibrations in the plate induced by the AC signal cause canceling bending vibrations in the structure and such that the plate is compressively pre-stressed along the structure when the structure is free of any bending vibrations. The compressive prestressing increases the amplitude of the canceling bending vibrations before the critical tensile stress level of the plate is reached. Preferably, a positive electric DC bias is also applied to the plate in its poling direction.

Method for the protection of extreme ultraviolet lithography optics

DOEpatents

Grunow, Philip A.; Clift, Wayne M.; Klebanoff, Leonard E.

2010-06-22

A coating for the protection of optical surfaces exposed to a high energy erosive plasma. A gas that can be decomposed by the high energy plasma, such as the xenon plasma used for extreme ultraviolet lithography (EUVL), is injected into the EUVL machine. The decomposition products coat the optical surfaces with a protective coating maintained at less than about 100 .ANG. thick by periodic injections of the gas. Gases that can be used include hydrocarbon gases, particularly methane, PH.sub.3 and H.sub.2S. The use of PH.sub.3 and H.sub.2S is particularly advantageous since films of the plasma-induced decomposition products S and P cannot grow to greater than 10 .ANG. thick in a vacuum atmosphere such as found in an EUVL machine.

Effects of Systematic Cue Exposure Through Virtual Reality on Cigarette Craving

PubMed Central

Pericot-Valverde, Irene; Secades-Villa, Roberto; Gutiérrez-Maldonado, José

2014-01-01

Introduction: Cigarette cravings have been associated with less successful attempts to quit smoking and a greater likelihood of relapse after smoking cessation. Background craving refers to a relatively steady and continuous experience of craving, while cue-induced craving refers to phases of intense craving triggered by cues associated with smoking. Cue exposure treatment (CET) involves repeated exposure to stimuli associated with substance use in order to reduce craving responses. However, mixed results have been found regarding the effect of CET on both types of craving. The aim of this study was to assess the effect of systematic virtual reality cue exposure treatment (VR-CET) on background and cue-induced cravings. Methods: Participants were 48 treatment-seeking smokers. The VR-CET consisted of prolonged exposure sessions to several interactive virtual environments. The VR-CET was applied once a week over 5 weeks. An individualized hierarchy of exposure was drawn up for each patient starting from the easiest virtual environment. Background and cue-induced cravings were recorded in each session. Results: Cue-induced craving decreased over each session as a result of prolonged exposure. VR-CET also reduced cue-induced and background cravings across the 5 sessions, showing a cumulative effect across the exposure sessions. Conclusions: Our results evidenced the utility of VR-CET in reducing both types of cigarette craving. A combination of CET through VR with psychological treatments may improve current treatments for smoking cessation. PMID:24962558

Fatigue Behavior of Porous Ti-6Al-4V Made by Laser-Engineered Net Shaping.

PubMed

Razavi, Seyed Mohammad Javad; Bordonaro, Giancarlo G; Ferro, Paolo; Torgersen, Jan; Berto, Filippo

2018-02-12

The fatigue behavior and fracture mechanisms of additively manufactured Ti-6Al-4V specimens are investigated in this study. Three sets of testing samples were fabricated for the assessment of fatigue life. The first batch of samples was built by using Laser-Engineered Net Shaping (LENS) technology, a Direct Energy Deposition (DED) method. Internal voids and defects were induced in a second batch of samples by changing LENS machine processing parameters. Fatigue performance of these samples is compared to the wrought Ti-6Al-4V samples. The effects of machine-induced porosity are assessed on mechanical properties and results are presented in the form of SN curves for the three sets of samples. Fracture mechanisms are examined by using Scanning Electron Microscopy (SEM) to characterize the morphological characteristics of the failure surface. Different fracture surface morphologies are observed for porous and non-porous specimens due to the combination of head write speed and laser power. Formation of defects such as pores, unmelted regions, and gas entrapments affect the failure mechanisms in porous specimens. Non-porous specimens exhibit fatigue properties comparable with that of the wrought specimens, but porous specimens are found to show a tremendous reduced fatigue strength.

Unraveling Network-induced Memory Contention: Deeper Insights with Machine Learning

DOE PAGES

Groves, Taylor Liles; Grant, Ryan; Gonzales, Aaron; ...

2017-11-21

Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems enabling asynchronous data transfers, so that applications may fully utilize CPU resources while simultaneously sharing data amongst remote nodes. We examine Network-induced Memory Contention (NiMC) on Infiniband networks. We expose the interactions between RDMA, main-memory and cache, when applications and out-of-band services compete for memory resources. We then explore NiMCs resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMCs impact grows with scale resulting in up to 3X performance degradation atmore » scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. In addition, this work examines the problem of predicting NiMC's impact on applications by leveraging machine learning and easily accessible performance counters. This approach provides additional insights about the root cause of NiMC and facilitates dynamic selection of potential solutions. Finally, we evaluated three potential techniques to reduce NiMCs impact, namely hardware offloading, core reservation and network throttling.« less

Cutting Modeling of Hybrid CFRP/Ti Composite with Induced Damage Analysis

PubMed Central

Xu, Jinyang; El Mansori, Mohamed

2016-01-01

In hybrid carbon fiber reinforced polymer (CFRP)/Ti machining, the bi-material interface is the weakest region vulnerable to severe damage formation when the tool cutting from one phase to another phase and vice versa. The interface delamination as well as the composite-phase damage is the most serious failure dominating the bi-material machining. In this paper, an original finite element (FE) model was developed to inspect the key mechanisms governing the induced damage formation when cutting this multi-phase material. The hybrid composite model was constructed by establishing three disparate physical constituents, i.e., the Ti phase, the interface, and the CFRP phase. Different constitutive laws and damage criteria were implemented to build up the entire cutting behavior of the bi-material system. The developed orthogonal cutting (OC) model aims to characterize the dynamic mechanisms of interface delamination formation and the affected interface zone (AIZ). Special focus was made on the quantitative analyses of the parametric effects on the interface delamination and composite-phase damage. The numerical results highlighted the pivotal role of AIZ in affecting the formation of interface delamination, and the significant impacts of feed rate and cutting speed on delamination extent and fiber/matrix failure. PMID:28787824

Theoretical Estimation of Thermal Effects in Drilling of Woven Carbon Fiber Composite

PubMed Central

Díaz-Álvarez, José; Olmedo, Alvaro; Santiuste, Carlos; Miguélez, María Henar

2014-01-01

Carbon Fiber Reinforced Polymer (CFRPs) composites are extensively used in structural applications due to their attractive properties. Although the components are usually made near net shape, machining processes are needed to achieve dimensional tolerance and assembly requirements. Drilling is a common operation required for further mechanical joining of the components. CFRPs are vulnerable to processing induced damage; mainly delamination, fiber pull-out, and thermal degradation, drilling induced defects being one of the main causes of component rejection during manufacturing processes. Despite the importance of analyzing thermal phenomena involved in the machining of composites, only few authors have focused their attention on this problem, most of them using an experimental approach. The temperature at the workpiece could affect surface quality of the component and its measurement during processing is difficult. The estimation of the amount of heat generated during drilling is important; however, numerical modeling of drilling processes involves a high computational cost. This paper presents a combined approach to thermal analysis of composite drilling, using both an analytical estimation of heat generated during drilling and numerical modeling for heat propagation. Promising results for indirect detection of risk of thermal damage, through the measurement of thrust force and cutting torque, are obtained. PMID:28788685

Unraveling Network-induced Memory Contention: Deeper Insights with Machine Learning

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

Groves, Taylor Liles; Grant, Ryan; Gonzales, Aaron

Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems enabling asynchronous data transfers, so that applications may fully utilize CPU resources while simultaneously sharing data amongst remote nodes. We examine Network-induced Memory Contention (NiMC) on Infiniband networks. We expose the interactions between RDMA, main-memory and cache, when applications and out-of-band services compete for memory resources. We then explore NiMCs resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMCs impact grows with scale resulting in up to 3X performance degradation atmore » scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. In addition, this work examines the problem of predicting NiMC's impact on applications by leveraging machine learning and easily accessible performance counters. This approach provides additional insights about the root cause of NiMC and facilitates dynamic selection of potential solutions. Finally, we evaluated three potential techniques to reduce NiMCs impact, namely hardware offloading, core reservation and network throttling.« less

Effect of Induced Periimplantitis on Dental Implants With and Without Ultrathin Hydroxyapatite Coating.

PubMed

Madi, Marwa; Zakaria, Osama; Ichinose, Shizuko; Kasugai, Shohei

2016-02-01

The aim of this study was to compare the effect of ligature-induced periimplantitis on dental implants with and without hydroxyapatite (HA) coat. Thirty-two dental implants (3.3 mm wide, 13 mm long) with 4 surface treatments (8 implant/group) (M: machined, SA: sandblasted acid etched, S: sputter HA coat and P: plasma-sprayed HA coat) were inserted into canine mandibles. After 12 weeks, oral hygiene procedures were stopped and silk ligatures were placed around the implant abutments to allow plaque accumulation for the following 16 weeks. Implants with the surrounding tissues were retrieved and prepared for histological examination. Bone-to-implant contact (BIC) and implant surfaces were examined using scanning electron microscopy and energy dispersive x-ray spectroscopy. Histological observation revealed marginal bone loss and large inflammatory cell infiltrates in the periimplant soft tissue. Sputter HA implants showed the largest BIC (98.1%) and machined implant showed the smallest values (70.4%). After 28 weeks, thin sputter HA coat was almost completely dissolved, whereas plasma-sprayed HA coat showed complete thickness preservation. Thin sputter HA-coated implants showed more bone implant contact and less marginal bone loss than thick HA-coated implants under periimplantitis condition.

Machine Learning and Inverse Problem in Geodynamics

NASA Astrophysics Data System (ADS)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R.

2017-12-01

During the past few decades numerical modeling and traditional HPC have been widely deployed in many diverse fields for problem solutions. However, in recent years the rapid emergence of machine learning (ML), a subfield of the artificial intelligence (AI), in many fields of sciences, engineering, and finance seems to mark a turning point in the replacement of traditional modeling procedures with artificial intelligence-based techniques. The study of the circulation in the interior of Earth relies on the study of high pressure mineral physics, geochemistry, and petrology where the number of the mantle parameters is large and the thermoelastic parameters are highly pressure- and temperature-dependent. More complexity arises from the fact that many of these parameters that are incorporated in the numerical models as input parameters are not yet well established. In such complex systems the application of machine learning algorithms can play a valuable role. Our focus in this study is the application of supervised machine learning (SML) algorithms in predicting mantle properties with the emphasis on SML techniques in solving the inverse problem. As a sample problem we focus on the spin transition in ferropericlase and perovskite that may cause slab and plume stagnation at mid-mantle depths. The degree of the stagnation depends on the degree of negative density anomaly at the spin transition zone. The training and testing samples for the machine learning models are produced by the numerical convection models with known magnitudes of density anomaly (as the class labels of the samples). The volume fractions of the stagnated slabs and plumes which can be considered as measures for the degree of stagnation are assigned as sample features. The machine learning models can determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at mid-mantle depths. Employing support vector machine (SVM) algorithms we show that SML techniques can successfully predict the magnitude of the mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex problems in mantle dynamics by employing deep learning algorithms for estimation of mantle properties such as viscosity, elastic parameters, and thermal and chemical anomalies.

Electromagnetic Signal Feedback Control for Proximity Detection Systems

NASA Astrophysics Data System (ADS)

Smith, Adam K.

Coal is the most abundant fossil fuel in the United States and remains an essential source of energy. While more than half of coal production comes from surface mining, nearly twice as many workers are employed by underground operations. One of the key pieces of equipment used in underground coal mining is the continuous mining machine. These large and powerful machines are operated in confined spaces by remote control. Since 1984, 40 mine workers in the U. S. have been killed when struck or pinned by a continuous mining machine. It is estimated that a majority of these accidents could have been prevented with the application of proximity detection systems. While proximity detection systems can significantly increase safety around a continuous mining machine, there are some system limitations. Commercially available proximity warning systems for continuous mining machines use magnetic field generators to detect workers and establish safe work areas around the machines. Several environmental factors, however, can influence and distort the magnetic fields. To minimize these effects, a control system has been developed using electromagnetic field strength and generator current to stabilize and control field drift induced by internal and external environmental factors. A laboratory test set-up was built using a ferrite-core magnetic field generator to produce a stable magnetic field. Previous work based on a field-invariant magnetic flux density model, which generically describes the electromagnetic field, is expanded upon. The analytically established transferable shell-based flux density distribution model is used to experimentally validate the control system. By controlling the current input to the ferrite-core generator, a more reliable and consistent magnetic field is produced. Implementation of this technology will improve accuracy and performance of existing commercial proximity detection systems. These research results will help reduce the risk of traumatic injuries and improve overall safety in the mining workplace.

Study of muon-induced neutron production using accelerator muon beam at CERN

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

Nakajima, Y.; Lin, C. J.; Ochoa-Ricoux, J. P.

2015-08-17

Cosmogenic muon-induced neutrons are one of the most problematic backgrounds for various underground experiments for rare event searches. In order to accurately understand such backgrounds, experimental data with high-statistics and well-controlled systematics is essential. We performed a test experiment to measure muon-induced neutron production yield and energy spectrum using a high-energy accelerator muon beam at CERN. We successfully observed neutrons from 160 GeV/c muon interaction on lead, and measured kinetic energy distributions for various production angles. Works towards evaluation of absolute neutron production yield is underway. This work also demonstrates that the setup is feasible for a future large-scale experimentmore » for more comprehensive study of muon-induced neutron production.« less

Semi-supervised anomaly detection - towards model-independent searches of new physics

NASA Astrophysics Data System (ADS)

Kuusela, Mikael; Vatanen, Tommi; Malmi, Eric; Raiko, Tapani; Aaltonen, Timo; Nagai, Yoshikazu

2012-06-01

Most classification algorithms used in high energy physics fall under the category of supervised machine learning. Such methods require a training set containing both signal and background events and are prone to classification errors should this training data be systematically inaccurate for example due to the assumed MC model. To complement such model-dependent searches, we propose an algorithm based on semi-supervised anomaly detection techniques, which does not require a MC training sample for the signal data. We first model the background using a multivariate Gaussian mixture model. We then search for deviations from this model by fitting to the observations a mixture of the background model and a number of additional Gaussians. This allows us to perform pattern recognition of any anomalous excess over the background. We show by a comparison to neural network classifiers that such an approach is a lot more robust against misspecification of the signal MC than supervised classification. In cases where there is an unexpected signal, a neural network might fail to correctly identify it, while anomaly detection does not suffer from such a limitation. On the other hand, when there are no systematic errors in the training data, both methods perform comparably.

The effects of subcutaneous injection of nicotine on osseointegration of machined and anodized implants in rabbits.

PubMed

Linden, Maria Salete Sandini; Bittencourt, Marcos Eugênio de; Carli, João Paulo De; Miyagaki, Daniela Cristina; Santos, Pâmela Letícia Dos; Paranhos, Luiz Renato; Groppo, Francisco Carlos; Ramacciato, Juliana Cama

2018-01-01

To evaluate the influence of subcutaneous injection nicotine in osseointegration process on different implant surfaces. Twenty-two male rabbits were distributed into two groups according to the subcutaneous injections: (1) nicotine 3 mg/day/kg and (2) 0.9 % NaCI 3 mL/day/kg, three times a day; subgroups were then designated-machined and anodized implants were placed in the right and left tibia bones, respectively. The animals were submitted euthanasia after periods of eight weeks to determine nicotine and cotinine levels, alkaline phosphatase and biomechanical analysis. The plasmatic levels of nicotine and cotinine were 0.5 ± 0.28 ng/mL and 9.5 ± 6.51 ng/mL, respectively. The alkaline phosphatase analyses in blood levels in control group were observed 40.8 ± 11.88 UI/L and 40.75 ± 12.46 UI/L, for the surfaces machined and anodized, respectively. In the test group was observed levels 37.9 ± 4.84 UI/L, for both implant surfaces. No significant differences were observed between control and test groups and between the implant surfaces regarding alkaline phosphatase blood levels. For biomechanics, no significant differences were observed in control group between the machined (25±8.46 Ncm) or anodized (31.2 ± 6.76 Ncm) implants. However, the treatment with nicotine induced higher torque than control in both machined (38.3 ± 13.52 Ncm) and anodized (35.5 ± 14.17 Ncm) implants, with p = 0.0024 and p = 0.0121, respectively. Subcutaneous injection of nicotine following implant insertion didn't have effect on osseointegration, independently from the implant surface.

Prospect of EUV mask repair technology using e-beam tool

NASA Astrophysics Data System (ADS)

Kanamitsu, Shingo; Hirano, Takashi; Suga, Osamu

2010-09-01

Currently, repair machines used for advanced photomasks utilize principle method like as FIB, AFM, and EB. There are specific characteristic respectively, thus they have an opportunity to be used in suitable situation. But when it comes to EUV generation, pattern size is so small highly expected as under 80nm that higher image resolution and repair accuracy is needed for its machines. Because FIB machine has intrinsic damage problem induced by Ga ion and AFM machine has critical tip size issue, those machines are basically difficult to be applied for EUV generation. Consequently, we focused on EB repair tool for research work. EB repair tool has undergone practical milestone about MoSi based masks. We have applied same process which is used for MoSi to EUV blank and confirmed its reaction. Then we found some severe problems which show uncontrollable feature due to its enormously strong reaction between etching gas and absorber material. Though we could etch opaque defect with conventional method and get the edge shaped straight by top-down SEM viewing, there were problems like as sidewall undercut or local erosion depending on defect shape. In order to cope with these problems, the tool vender has developed a new process and reported it through an international conference [1]. We have evaluated the new process mentioned above in detail. In this paper, we will bring the results of those evaluations. Several experiments for repair accuracy, process stability, and other items have been done under estimation of practical condition assuming diversified size and shape defects. A series of actual printability tests will be also included. On the basis of these experiments, we consider the possibility of EB-repair application for 20nm pattern.

An application to model traffic intensity of agricultural machinery at field scale

NASA Astrophysics Data System (ADS)

Augustin, Katja; Kuhwald, Michael; Duttmann, Rainer

2017-04-01

Several soil-pressure-models deal with the impact of agricultural machines on soils. In many cases, these models were used for single spots and consider a static machine configuration. Therefore, a statement about the spatial distribution of soil compaction risk for entire working processes is limited. The aim of the study is the development of an application for the spatial modelling of traffic lanes from agricultural vehicles including wheel load, ground pressure and wheel passages at the field scale. The application is based on Open Source software, application and data formats, using python programming language. Minimum input parameters are GPS-positions, vehicles and tires (producer and model) and the tire inflation pressure. Five working processes were distinguished: soil tillage, manuring, plant protection, sowing and harvest. Currently, two different models (Diserens 2009, Rücknagel et al. 2015) were implemented to calculate the soil pressure. The application was tested at a study site in Lower Saxony, Germany. Since 2015, field traffic were recorded by RTK-GPS and used machine set ups were noted. Using these input information the traffic lanes, wheel load and soil pressure were calculated for all working processes. For instance, the maize harvest in 2016 with a crop chopper and one transport vehicle crossed about 55 % of the total field area. At some places the machines rolled over up to 46 times. Approximately 35 % of the total area was affected by wheel loads over 7 tons and soil pressures between 163 and 193 kPa. With the information about the spatial distribution of wheel passages, wheel load and soil pressure it is possible to identify hot spots of intensive field traffic. Additionally, the use of the application enables the analysis of soil compaction risk induced by agricultural machines for long- and short-term periods.

Solution of magnetic field and eddy current problem induced by rotating magnetic poles (abstract)

NASA Astrophysics Data System (ADS)

Liu, Z. J.; Low, T. S.

1996-04-01

The magnetic field and eddy current problems induced by rotating permanent magnet poles occur in electromagnetic dampers, magnetic couplings, and many other devices. Whereas numerical techniques, for example, finite element methods can be exploited to study various features of these problems, such as heat generation and drag torque development, etc., the analytical solution is always of interest to the designers since it helps them to gain the insight into the interdependence of the parameters involved and provides an efficient tool for designing. Some of the previous work showed that the solution of the eddy current problem due to the linearly moving magnet poles can give satisfactory approximation for the eddy current problem due to rotating fields. However, in many practical cases, especially when the number of magnet poles is small, there is significant effect of flux focusing due to the geometry. The above approximation can therefore lead to marked errors in the theoretical predictions of the device performance. Bernot et al. recently described an analytical solution in a polar coordinate system where the radial field is excited by a time-varying source. A discussion of an analytical solution of the magnetic field and eddy current problems induced by moving magnet poles in radial field machines will be given in this article. The theoretical predictions obtained from this method is compared with the results obtained from finite element calculations. The validity of the method is also checked by the comparison of the theoretical predictions and the measurements from a test machine. It is shown that the introduced solution leads to a significant improvement in the air gap field prediction as compared with the results obtained from the analytical solution that models the eddy current problems induced by linearly moving magnet poles.

Predicting Droplet Formation on Centrifugal Microfluidic Platforms

NASA Astrophysics Data System (ADS)

Moebius, Jacob Alfred

Centrifugal microfluidics is a widely known research tool for biological sample and water quality analysis. Currently, the standard equipment used for such diagnostic applications include slow, bulky machines controlled by multiple operators. These machines can be condensed into a smaller, faster benchtop sample-to-answer system. Sample processing is an important step taken to extract, isolate, and convert biological factors, such as nucleic acids or proteins, from a raw sample to an analyzable solution. Volume definition is one such step. The focus of this thesis is the development of a model predicting monodispersed droplet formation and the application of droplets as a technique for volume definition. First, a background of droplet microfluidic platforms is presented, along with current biological analysis technologies and the advantages of integrating such technologies onto microfluidic platforms. Second, background and theories of centrifugal microfluidics is given, followed by theories relevant to droplet emulsions. Third, fabrication techniques for centrifugal microfluidic designs are discussed. Finally, the development of a model for predicting droplet formation on the centrifugal microfluidic platform are presented for the rest of the thesis. Predicting droplet formation analytically based on the volumetric flow rates of the continuous and dispersed phases, the ratios of these two flow rates, and the interfacial tension between the continuous and dispersed phases presented many challenges, which will be discussed in this work. Experimental validation was completed using continuous phase solutions of different interfacial tensions. To conclude, prospective applications are discussed with expected challenges.

Auditory contributions to flavour perception and feeding behaviour.

PubMed

Spence, Charles

2012-11-05

This article reviews the research that has looked at the role of audition in both flavour perception and feeding behaviour in humans. The article starts by looking at early research that focused on the effect of background noise on the sensory-discriminative aspects of taste/flavour perception and on people's hedonic responses to food and beverage items. Next, I move on to look at the role of the sound made by the food (or beverage) itself. Additionally, recent studies that have started to assess the impact of food and beverage packaging sounds, not to mention food preparation sounds, on people's sensory-discriminative and hedonic responses to a variety of food and beverage products are discussed. Finally, the literature on the effect of background music and/or soundscapes on food and beverage perception/consumption are reviewed briefly. Taken together, this body of research, spanning both highly-controlled laboratory experiments and more ecologically-valid field studies, clearly demonstrates that what the consumer hears, be it the sound of the food, the sound of the packaging, the sound of the machine used to prepare that food or beverage (e.g., as in the case of the sound of a coffee machine), and even the sound of the environment in which the consumer happens to be eating and drinking can all exert a profound, if often unacknowledged, role in our feeding behaviours not to mention on our flavour perception. Copyright © 2012 Elsevier Inc. All rights reserved.

Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

NASA Astrophysics Data System (ADS)

Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

2014-09-01

Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

Automated analysis of high-content microscopy data with deep learning.

PubMed

Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

2017-04-18

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review.

PubMed

Pérez, Luis; Rodríguez, Íñigo; Rodríguez, Nuria; Usamentiaga, Rubén; García, Daniel F

2016-03-05

In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works.

CEPC-SPPC accelerator status towards CDR

NASA Astrophysics Data System (ADS)

Gao, J.

2017-12-01

In this paper we will give an introduction to the Circular Electron Positron Collider (CEPC). The scientific background, physics goal, the collider design requirements and the conceptual design principle of the CEPC are described. On the CEPC accelerator, the optimization of parameter designs for the CEPC with different energies, machine lengths, single ring and crab-waist collision partial double ring, advanced partial double ring and fully partial double ring options, etc. have been discussed systematically, and compared. The CEPC accelerator baseline and alternative designs have been proposed based on the luminosity potential in relation with the design goals. The CEPC sub-systems, such as the collider main ring, booster, electron positron injector, etc. have also been introduced. The detector and the MAchine-Detector Interface (MDI) design have been briefly mentioned. Finally, the optimization design of the Super Proton-Proton Collider (SppC), its energy and luminosity potentials, in the same tunnel of the CEPC are also discussed. The CEPC-SppC Progress Report (2015-2016) has been published.

Evaluation of application for approval of alternative methodology for compliance with the NESHAP for shipbuilding and ship repair and recommended requirements for compliance (application submitted by Metro Machine Corporation, Norfolk, Virginia). Final report

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

Serageldin, M.A.

1999-07-01

The US Environmental Protection Agency is providing background information that supports the use of Metro Machine Corporation`s (MMC) compliant all position enclosure (CAPE) plus air management system and regenerative thermal oxidizer (RTO) (CAPE + RTO System) as an alternative means of limiting the emissions of volatile organic hazardous air pollutants per volume of applied solids (nonvolatiles). This document also explains how the authors arrived at the operating, recordkeeping, and reporting conditions that MMC must meet for approval. The add-on control system they used consists of a pollution capture unit operation (CAPE) plus air management system and a destruction unit operationmore » (RTO). When operated according to the specified procedures, it will control emissions to a level no greater than that from using coatings which comply with the limits in Table 2 of 40 CFR Part 63, Subpart II.« less

Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review

PubMed Central

Pérez, Luis; Rodríguez, Íñigo; Rodríguez, Nuria; Usamentiaga, Rubén; García, Daniel F.

2016-01-01

In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works. PMID:26959030

Final matches of the FIRST regional robotic competition at KSC

NASA Technical Reports Server (NTRS)

1999-01-01

During the 1999 FIRST Southeastern Regional robotic competition held at KSC, a robot carrying its cache of pillow-like disks maneuvers to move around another at left. Powered by 12-volt batteries and operated by remote control, the robotic gladiators spend two minutes each trying to grab, claw and hoist the pillows onto their machines. Teams play defense by taking away competitors' pillows and generally harassing opposing machines. Behind the field are a group of judges, including KSC former KSC Director of Shuttle Processing Robert Sieck (left, in cap), and Center Director Roy Bridges (in white shirt). A giant screen TV in the background displays the action on the playing field. FIRST is a nonprofit organization, For Inspiration and Recognition of Science and Technology. The competition comprised 27 teams, pairing high school students with engineer mentors and corporations. The FIRST robotics competition is designed to provide students with a hands-on, inside look at engineering and other professional careers.

SU-D-204-06: Integration of Machine Learning and Bioinformatics Methods to Analyze Genome-Wide Association Study Data for Rectal Bleeding and Erectile Dysfunction Following Radiotherapy in Prostate Cancer

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

Oh, J; Deasy, J; Kerns, S

Purpose: We investigated whether integration of machine learning and bioinformatics techniques on genome-wide association study (GWAS) data can improve the performance of predictive models in predicting the risk of developing radiation-induced late rectal bleeding and erectile dysfunction in prostate cancer patients. Methods: We analyzed a GWAS dataset generated from 385 prostate cancer patients treated with radiotherapy. Using genotype information from these patients, we designed a machine learning-based predictive model of late radiation-induced toxicities: rectal bleeding and erectile dysfunction. The model building process was performed using 2/3 of samples (training) and the predictive model was tested with 1/3 of samples (validation).more » To identify important single nucleotide polymorphisms (SNPs), we computed the SNP importance score, resulting from our random forest regression model. We performed gene ontology (GO) enrichment analysis for nearby genes of the important SNPs. Results: After univariate analysis on the training dataset, we filtered out many SNPs with p>0.001, resulting in 749 and 367 SNPs that were used in the model building process for rectal bleeding and erectile dysfunction, respectively. On the validation dataset, our random forest regression model achieved the area under the curve (AUC)=0.70 and 0.62 for rectal bleeding and erectile dysfunction, respectively. We performed GO enrichment analysis for the top 25%, 50%, 75%, and 100% SNPs out of the select SNPs in the univariate analysis. When we used the top 50% SNPs, more plausible biological processes were obtained for both toxicities. An additional test with the top 50% SNPs improved predictive power with AUC=0.71 and 0.65 for rectal bleeding and erectile dysfunction. A better performance was achieved with AUC=0.67 when age and androgen deprivation therapy were added to the model for erectile dysfunction. Conclusion: Our approach that combines machine learning and bioinformatics techniques enabled designing better models and identifying more plausible biological processes associated with the outcomes.« less

The neuromuscular fatigue induced by repeated scrums generates instability that can be limited by appropriate recovery.

PubMed

Morel, B; Hautier, C A

2017-02-01

The aim of this study was to evaluate the influence of the fatigue on the machine scrum pushing sagittal forces during repeated scrums and to determine the origin of the knee extensor fatigue. Twelve elite U23 rugby union front row players performed six 6-s scrums every 30 s against a dynamic scrum machine with passive or active recovery. The peak, average, and the standard deviation of the force were measured. A neuromuscular testing procedure of the knee extensors was carried out before and immediately after the repeated scrum protocol including maximal voluntary force, evoked force, and voluntary activation. The average and peak forces did not decrease after six scrums with passive recovery. The standard deviation of the force increased by 70.2 ± 42.7% (P < 0.001). Maximal voluntary/evoked force and voluntary activation decreased (respectively 25.1 ± 7.0%, 14.6 ± 5.5%, and 24 ± 9.9%; P < 0.001). The standard deviation of the force did not increase with active recovery and was associated with lower decrease of maximal voluntary/evoked force and voluntary activation (respectively 12.8 ± 7.9%, 4.9 ± 6.5%, and 7.6 ± 4.1%; all P < 0.01). As a conclusion repeated scrummaging induced an increased machine scrum pushing instability associated with central and peripheral fatigue of the knee extensors. Active recovery seems to limit all these manifestations of fatigue. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A machine learning methodology for the selection and classification of spontaneous spinal cord dorsum potentials allows disclosure of structured (non-random) changes in neuronal connectivity induced by nociceptive stimulation

PubMed Central

Martin, Mario; Contreras-Hernández, Enrique; Béjar, Javier; Esposito, Gennaro; Chávez, Diógenes; Glusman, Silvio; Cortés, Ulises; Rudomin, Pablo

2015-01-01

Previous studies aimed to disclose the functional organization of the neuronal networks involved in the generation of the spontaneous cord dorsum potentials (CDPs) generated in the lumbosacral spinal segments used predetermined templates to select specific classes of spontaneous CDPs. Since this procedure was time consuming and required continuous supervision, it was limited to the analysis of two specific types of CDPs (negative CDPs and negative positive CDPs), thus excluding potentials that may reflect activation of other neuronal networks of presumed functional relevance. We now present a novel procedure based in machine learning that allows the efficient and unbiased selection of a variety of spontaneous CDPs with different shapes and amplitudes. The reliability and performance of the present method is evaluated by analyzing the effects on the probabilities of generation of different classes of spontaneous CDPs induced by the intradermic injection of small amounts of capsaicin in the anesthetized cat, a procedure known to induce a state of central sensitization leading to allodynia and hyperalgesia. The results obtained with the selection method presently described allowed detection of spontaneous CDPs with specific shapes and amplitudes that are assumed to represent the activation of functionally coupled sets of dorsal horn neurones that acquire different, structured configurations in response to nociceptive stimuli. These changes are considered as responses tending to adequate transmission of sensory information to specific functional requirements as part of homeostatic adjustments. PMID:26379540

Combining rules, background knowledge and change patterns to maintain semantic annotations.

PubMed

Cardoso, Silvio Domingos; Chantal, Reynaud-Delaître; Da Silveira, Marcos; Pruski, Cédric

2017-01-01

Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system.

Combining rules, background knowledge and change patterns to maintain semantic annotations

PubMed Central

Cardoso, Silvio Domingos; Chantal, Reynaud-Delaître; Da Silveira, Marcos; Pruski, Cédric

2017-01-01

Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system. PMID:29854115

Effect on cosmic microwave background polarization of coupling of quintessence to pseudoscalar formed from the electromagnetic field and its dual.

PubMed

Liu, Guo-Chin; Lee, Seokcheon; Ng, Kin-Wang

2006-10-20

We present the full set of power spectra of cosmic microwave background (CMB) temperature and polarization anisotropies due to the coupling between quintessence and pseudoscalar of electromagnetism. This coupling induces a rotation of the polarization plane of the CMB, thus resulting in a nonvanishing B mode and parity-violating TB and EB modes. Using the BOOMERANG data from the flight of 2003, we derive the most stringent constraint on the coupling strength. We find that in some cases the rotation-induced B mode can confuse the hunting for the gravitational lensing-induced B mode.

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

PubMed Central

2015-01-01

Background Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets. Results and discussion Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large ‘omics’ datasets are increasingly being used in the area of rheumatology. Conclusions Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery. PMID:25923811

Life cycle costing as a decision making tool for technology acquisition in radio-diagnosis

PubMed Central

Chakravarty, Abhijit; Debnath, Jyotindu

2014-01-01

Background Life cycle costing analysis is an emerging conceptual tool to validate capital investment in healthcare. Methods A preliminary study was done to analyze the long-term cost impact of acquiring a new 3 T MRI system when compared to technological upgradation of the existing 1.5 T MRI system with a view to evolve a decision matrix for correct investment planning and technology management. Operating costing method was utilized to estimate cost per unit MRI scan, costing inputs were considered for the existing 1.5 T and the proposed 3 T machine. Cost for each expected year in the life span of both 1.5 T and 3 T MRI scan options were then discounted to its Net Present Value. Net Present Value thus calculated for both the alternative options of 1.5 T and 3 T MRI machine was charted along with various intangible but critical Figures of Merit (FOM) to create a decision matrix for capital investment planning. Result Considering all fixed and variable costs contributing towards assumed operation, unit cost per MRI procedure was found to be Rs. 4244.58 for the 1.5 T upgrade and Rs. 6059.37 for the new 3 T MRI machine. Life Cycle Cost Analysis of the proposed 1.5 T upgrade and new 3 T machine showed a Net Present Value of Rs. 42,148,587.80 and Rs. 27,587,842.38 respectively. Conclusion The utility of life cycle costing as a strategic decision making tool towards evaluating alternative options for capital investment planning in health care environment is reiterated. PMID:25609862

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

Feature selection using a one dimensional naïve Bayes' classifier increases the accuracy of support vector machine classification of CDR3 repertoires.

PubMed

Cinelli, Mattia; Sun, Yuxin; Best, Katharine; Heather, James M; Reich-Zeliger, Shlomit; Shifrut, Eric; Friedman, Nir; Shawe-Taylor, John; Chain, Benny

2017-04-01

Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund's adjuvant (CFA) or CFA alone. The CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching >90% in some cases. The study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund's Adjuvant. The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893 . The Decombinator package is available at github.com/innate2adaptive/Decombinator . The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html . b.chain@ucl.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

The reported incidence of man-machine interface issues in Army aviators using the Aviator's Night Vision System (ANVIS) in a combat theatre

NASA Astrophysics Data System (ADS)

Hiatt, Keith L.; Rash, Clarence E.

2011-06-01

Background: Army Aviators rely on the ANVIS for night operations. Human factors literature notes that the ANVIS man-machine interface results in reports of visual and spinal complaints. This is the first study that has looked at these issues in the much harsher combat environment. Last year, the authors reported on the statistically significant (p<0.01) increased complaints of visual discomfort, degraded visual cues, and incidence of static and dynamic visual illusions in the combat environment [Proc. SPIE, Vol. 7688, 76880G (2010)]. In this paper we present the findings regarding increased spinal complaints and other man-machine interface issues found in the combat environment. Methods: A survey was administered to Aircrew deployed in support of Operation Enduring Freedom (OEF). Results: 82 Aircrew (representing an aggregate of >89,000 flight hours of which >22,000 were with ANVIS) participated. Analysis demonstrated high complaints of almost all levels of back and neck pain. Additionally, the use of body armor and other Aviation Life Support Equipment (ALSE) caused significant ergonomic complaints when used with ANVIS. Conclusions: ANVIS use in a combat environment resulted in higher and different types of reports of spinal symptoms and other man-machine interface issues over what was previously reported. Data from this study may be more operationally relevant than that of the peacetime literature as it is derived from actual combat and not from training flights, and it may have important implications about making combat predictions based on performance in training scenarios. Notably, Aircrew remarked that they could not execute the mission without ANVIS and ALSE and accepted the degraded ergonomic environment.

EXACT2: the semantics of biomedical protocols

PubMed Central

2014-01-01

Background The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility. Methods We have developed the ontology EXACT2 (EXperimental ACTions) that is designed to capture the full semantics of biomedical protocols required for their reproducibility. To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously 'unseen' (not used for the construction of EXACT2) protocols. Results The paper reports on a fundamentally new version EXACT2 that supports the semantically-defined representation of biomedical protocols. The ability of EXACT2 to capture the semantics of biomedical procedures was verified through a text mining use case. In this EXACT2 is used as a reference model for text mining tools to identify terms pertinent to experimental actions, and their properties, in biomedical protocols expressed in natural language. An EXACT2-based framework for the translation of biomedical protocols to a machine amenable format is proposed. Conclusions The EXACT2 ontology is sufficient to record, in a machine processable form, the essential information about biomedical protocols. EXACT2 defines explicit semantics of experimental actions, and can be used by various computer applications. It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format. PMID:25472549

The habitual meal and snacking patterns of university students in two countries and their use of vending machines.

PubMed

Spanos, D; Hankey, C R

2010-02-01

Dietary patterns and food choices in western and northern European countries can differ from those in countries that surround the Mediterranean basin. However, irregular meal patterns and the consumption of high-energy snacks tend to become common in most countries and their association with the prevalence of obesity has been examined in many studies. The first aim of the present study was to describe the habitual meal and snack intakes, including the use of vending machines, for two groups of first-year university students in two countries of different cultural backgrounds. The second aim was to explore the relationships between body mass index (BMI) and snacking for these two groups. One hundred and sixty first-year undergraduate university students from two defined universities in Greece (n = 80) and Scotland (n = 80) volunteered to complete a food frequency questionnaire (FFQ). The FFQ comprised 16 questions assessing their meal and snacking habits. Self-assessed height and weight data were collected. The majority of the 160 students reported a BMI in the healthy range (<25 kg m(-2)). Overall, 26% of the students reported never consuming breakfast. More Scottish students (74%) used vending machines (P < 0.05). More Scottish students consumed chocolate bars and crisps than Greek students (41% and 34% versus 37.5% and 20%, respectively). Only the choice of chocolate bars from vending machines and the consumption of crisps and low fat yogurts were related to BMI (P < 0.05) for both groups. University students living in different countries report similar dietary patterns but differ in their snacking habits. No relationships were found between BMI and snacking. There is a need to carry out research to further our understanding of this relationship.

Using Active Learning for Speeding up Calibration in Simulation Models

PubMed Central

Cevik, Mucahit; Ali Ergun, Mehmet; Stout, Natasha K.; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2015-01-01

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. PMID:26471190

Culture impacts the magnitude of the emotion-induced memory trade-off effect.

PubMed

Gutchess, Angela; Garner, Lauryn; Ligouri, Laura; Konuk, Ayse Isilay; Boduroglu, Aysecan

2017-10-04

The present study assessed the extent to which culture impacts the emotion-induced memory trade-off effect. This trade-off effect occurs because emotional items are better remembered than neutral ones, but this advantage comes at the expense of memory for backgrounds such that neutral backgrounds are remembered worse when they occurred with an emotional item than with a neutral one. Cultures differ in their prioritisation of focal object versus contextual background information, with Westerners focusing more on objects and Easterners focusing more on backgrounds. Americans, a Western culture, and Turks, an Eastern-influenced culture, incidentally encoded positive, negative, and neutral items placed against neutral backgrounds, and then completed a surprise memory test with the items and backgrounds tested separately. Results revealed a reduced trade-off for Turks compared to Americans. Although both groups exhibited an emotional enhancement in item memory, Turks did not show a decrement in memory for backgrounds that had been paired with emotional items. These findings complement prior ones showing reductions in trade-off effects as a result of task instructions. Here, we suggest that a contextual-focus at the level of culture can mitigate trade-off effects in emotional memory.

Attitude Control of a Satellite Simulator Using Reaction Wheels and a PID Controller

DTIC Science & Technology

2010-03-01

allow continuous and smooth control while inducing the smallest possible disturbance torques. The objective of this research is to design , build...making me feel welcome in their machine shop and for educating me on the difficulties of fabricating my engineering designs . On a personal note, much...3.16. SimSat II Reaction Wheel Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.17. EPOS 70/10 Velocity Controller

Significant Suppression of CT Radiation-Induced DNA Damage in Normal Human Cells by the PrC-210 Radioprotector.

PubMed

Jermusek, Frank; Benedict, Chelsea; Dreischmeier, Emma; Brand, Michael; Uder, Michael; Jeffery, Justin J; Ranallo, Frank N; Fahl, William E

2018-05-21

While computed tomography (CT) is now commonly used and considered to be clinically valuable, significant DNA double-strand breaks (γ-H2AX foci) in white blood cells from adult and pediatric CT patients have been frequently reported. In this study to determine whether γ-H2AX foci and X-ray-induced naked DNA damage are suppressed by administration of the PrC-210 radioprotector, human blood samples were irradiated in a CT scanner at 50-150 mGy with or without PrC-210, and γ-H2AX foci were scored. X-ray-induced naked DNA damage was also studied, and the DNA protective efficacy of PrC-210 was compared against 12 other common "antioxidants." PrC-210 reduced CT radiation-induced γ-H2AX foci in white blood cells to near background ( P < 0.0001) at radiation doses of 50-150 mGy. PrC-210 was most effective among the 13 "antioxidants" in reducing naked DNA X-ray damage, and its addition at 30 s before an • OH pulse reduced to background the • OH insult that otherwise induced >95% DNA damage. A systemic PrC-210 dose known to confer 100% survival in irradiated mice had no discernible effect on micro-CT image signal-to-noise ratio and CT image integrity. PrC-210 suppressed DNA damage to background or near background in each of these assay systems, thus supporting its development as a radioprotector for humans in multiple radiation exposure settings.

Effects of systematic cue exposure through virtual reality on cigarette craving.

PubMed

Pericot-Valverde, Irene; Secades-Villa, Roberto; Gutiérrez-Maldonado, José; García-Rodríguez, Olaya

2014-11-01

Cigarette cravings have been associated with less successful attempts to quit smoking and a greater likelihood of relapse after smoking cessation. Background craving refers to a relatively steady and continuous experience of craving, while cue-induced craving refers to phases of intense craving triggered by cues associated with smoking. Cue exposure treatment (CET) involves repeated exposure to stimuli associated with substance use in order to reduce craving responses. However, mixed results have been found regarding the effect of CET on both types of craving. The aim of this study was to assess the effect of systematic virtual reality cue exposure treatment (VR-CET) on background and cue-induced cravings. Participants were 48 treatment-seeking smokers. The VR-CET consisted of prolonged exposure sessions to several interactive virtual environments. The VR-CET was applied once a week over 5 weeks. An individualized hierarchy of exposure was drawn up for each patient starting from the easiest virtual environment. Background and cue-induced cravings were recorded in each session. Cue-induced craving decreased over each session as a result of prolonged exposure. VR-CET also reduced cue-induced and background cravings across the 5 sessions, showing a cumulative effect across the exposure sessions. Our results evidenced the utility of VR-CET in reducing both types of cigarette craving. A combination of CET through VR with psychological treatments may improve current treatments for smoking cessation. © The Author 2014. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study

PubMed Central

Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

2016-01-01

Background and objective: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. Methods: The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). Results: AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. Conclusion: The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions. PMID:27802228

CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing

PubMed Central

2011-01-01

Background Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. Results We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. Conclusion The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing. PMID:21878105

Machine learning and next-generation asteroid surveys

NASA Astrophysics Data System (ADS)

Nugent, Carrie R.; Dailey, John; Cutri, Roc M.; Masci, Frank J.; Mainzer, Amy K.

2017-10-01

Next-generation surveys such as NEOCam (Mainzer et al., 2016) will sift through tens of millions of point source detections daily to detect and discover asteroids. This requires new, more efficient techniques to distinguish between solar system objects, background stars and galaxies, and artifacts such as cosmic rays, scattered light and diffraction spikes.Supervised machine learning is a set of algorithms that allows computers to classify data on a training set, and then apply that classification to make predictions on new datasets. It has been employed by a broad range of fields, including computer vision, medical diagnoses, economics, and natural language processing. It has also been applied to astronomical datasets, including transient identification in the Palomar Transient Factory pipeline (Masci et al., 2016), and in the Pan-STARRS1 difference imaging (D. E. Wright et al., 2015).As part of the NEOCam extended phase A work we apply machine learning techniques to the problem of asteroid detection. Asteroid detection is an ideal application of supervised learning, as there is a wealth of metrics associated with each extracted source, and suitable training sets are easily created. Using the vetted NEOWISE dataset (E. L. Wright et al., 2010, Mainzer et al., 2011) as a proof-of-concept of this technique, we applied the python package sklearn. We report on reliability, feature set selection, and the suitability of various algorithms.

Efficient and robust pupil size and blink estimation from near-field video sequences for human-machine interaction.

PubMed

Chen, Siyuan; Epps, Julien

2014-12-01

Monitoring pupil and blink dynamics has applications in cognitive load measurement during human-machine interaction. However, accurate, efficient, and robust pupil size and blink estimation pose significant challenges to the efficacy of real-time applications due to the variability of eye images, hence to date, require manual intervention for fine tuning of parameters. In this paper, a novel self-tuning threshold method, which is applicable to any infrared-illuminated eye images without a tuning parameter, is proposed for segmenting the pupil from the background images recorded by a low cost webcam placed near the eye. A convex hull and a dual-ellipse fitting method are also proposed to select pupil boundary points and to detect the eyelid occlusion state. Experimental results on a realistic video dataset show that the measurement accuracy using the proposed methods is higher than that of widely used manually tuned parameter methods or fixed parameter methods. Importantly, it demonstrates convenience and robustness for an accurate and fast estimate of eye activity in the presence of variations due to different users, task types, load, and environments. Cognitive load measurement in human-machine interaction can benefit from this computationally efficient implementation without requiring a threshold calibration beforehand. Thus, one can envisage a mini IR camera embedded in a lightweight glasses frame, like Google Glass, for convenient applications of real-time adaptive aiding and task management in the future.

Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data

PubMed Central

2013-01-01

Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs) and Support Vector Machines (SVMs) were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression. PMID:23369200

Standard guidelines of care: laser and IPL hair reduction.

PubMed

Buddhadev, Rajesh M

2008-01-01

Laser-assisted hair removal, Laser hair removal, Laser and light-assisted hair removal, Laser and light-assisted, long-term hair reduction, IPL photodepilation, LHE photodepilation; all these are acceptable synonyms. Laser (Ruby, Nd Yag, Alexandrite, Diode), intense pulse light, light and heat energy system are the different light-/Laser-based systems used for hair removal; each have its advantages and disadvantages. The word "LONG-TERM HAIR REDUCTION" should be used rather than permanent hair removal. Patient counseling is essential about the need for multiple sessions. PHYSICIANS' QUALIFICATIONS: Laser hair removal may be practiced by any dermatologist, who has received adequate background training during postgraduation or later at a centre that provides education and training in Lasers or in focused workshops providing such training. The dermatologist should have adequate knowledge of the machines, the parameters and aftercare. The physician may allow the actual procedure to be performed under his/her direct supervision by a trained nurse assistant/junior doctor. However, the final responsibility for the procedure would lie with the physician. The procedure may be performed in the physician's minor procedure room. Investigations to rule out any underlying cause for hair growth are important; concurrent drug therapy may be needed. Laser parameters vary with area, type of hair, and the machine used. Full knowledge about the machine and cooling system is important. Future maintenance treatments may be needed.

Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments

PubMed Central

Sachs, Christian Carsten; Grünberger, Alexander; Helfrich, Stefan; Probst, Christopher; Wiechert, Wolfgang; Kohlheyer, Dietrich; Nöh, Katharina

2016-01-01

Background Microfluidic lab-on-chip technology combined with live-cell imaging has enabled the observation of single cells in their spatio-temporal context. The mother machine (MM) cultivation system is particularly attractive for the long-term investigation of rod-shaped bacteria since it facilitates continuous cultivation and observation of individual cells over many generations in a highly parallelized manner. To date, the lack of fully automated image analysis software limits the practical applicability of the MM as a phenotypic screening tool. Results We present an image analysis pipeline for the automated processing of MM time lapse image stacks. The pipeline supports all analysis steps, i.e., image registration, orientation correction, channel/cell detection, cell tracking, and result visualization. Tailored algorithms account for the specialized MM layout to enable a robust automated analysis. Image data generated in a two-day growth study (≈ 90 GB) is analyzed in ≈ 30 min with negligible differences in growth rate between automated and manual evaluation quality. The proposed methods are implemented in the software molyso (MOther machine AnaLYsis SOftware) that provides a new profiling tool to analyze unbiasedly hitherto inaccessible large-scale MM image stacks. Conclusion Presented is the software molyso, a ready-to-use open source software (BSD-licensed) for the unsupervised analysis of MM time-lapse image stacks. molyso source code and user manual are available at https://github.com/modsim/molyso. PMID:27661996

An Analysis of a Digital Variant of the Trail Making Test Using Machine Learning Techniques

PubMed Central

Dahmen, Jessamyn; Cook, Diane; Fellows, Robert; Schmitter-Edgecombe, Maureen

2017-01-01

BACKGROUND The goal of this work is to develop a digital version of a standard cognitive assessment, the Trail Making Test (TMT), and assess its utility. OBJECTIVE This paper introduces a novel digital version of the TMT and introduces a machine learning based approach to assess its capabilities. METHODS Using digital Trail Making Test (dTMT) data collected from (N=54) older adult participants as feature sets, we use machine learning techniques to analyze the utility of the dTMT and evaluate the insights provided by the digital features. RESULTS Predicted TMT scores correlate well with clinical digital test scores (r=0.98) and paper time to completion scores (r=0.65). Predicted TICS exhibited a small correlation with clinically-derived TICS scores (r=0.12 Part A, r=0.10 Part B). Predicted FAB scores exhibited a small correlation with clinically-derived FAB scores (r=0.13 Part A, r=0.29 for Part B). Digitally-derived features were also used to predict diagnosis (AUC of 0.65). CONCLUSION Our findings indicate that the dTMT is capable of measuring the same aspects of cognition as the paper-based TMT. Furthermore, the dTMT’s additional data may be able to help monitor other cognitive processes not captured by the paper-based TMT alone. PMID:27886019

A comparison of machine learning techniques for survival prediction in breast cancer

PubMed Central

2011-01-01

Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data. PMID:21569330

Manufacturing conditioned roughness and wear of biomedical oxide ceramics for all-ceramic knee implants

PubMed Central

2013-01-01

Background Ceramic materials are used in a growing proportion of hip joint prostheses due to their wear resistance and biocompatibility properties. However, ceramics have not been applied successfully in total knee joint endoprostheses to date. One reason for this is that with strict surface quality requirements, there are significant challenges with regard to machining. High-toughness bioceramics can only be machined by grinding and polishing processes. The aim of this study was to develop an automated process chain for the manufacturing of an all-ceramic knee implant. Methods A five-axis machining process was developed for all-ceramic implant components. These components were used in an investigation of the influence of surface conformity on wear behavior under simplified knee joint motion. Results The implant components showed considerably reduced wear compared to conventional material combinations. Contact area resulting from a variety of component surface shapes, with a variety of levels of surface conformity, greatly influenced wear rate. Conclusions It is possible to realize an all-ceramic knee endoprosthesis device, with a precise and affordable manufacturing process. The shape accuracy of the component surfaces, as specified by the design and achieved during the manufacturing process, has a substantial influence on the wear behavior of the prosthesis. This result, if corroborated by results with a greater sample size, is likely to influence the design parameters of such devices. PMID:23988155

Technology for robotic surface inspection in space

NASA Technical Reports Server (NTRS)

Volpe, Richard; Balaram, J.

1994-01-01

This paper presents on-going research in robotic inspection of space platforms. Three main areas of investigation are discussed: machine vision inspection techniques, an integrated sensor end-effector, and an orbital environment laboratory simulation. Machine vision inspection utilizes automatic comparison of new and reference images to detect on-orbit induced damage such as micrometeorite impacts. The cameras and lighting used for this inspection are housed in a multisensor end-effector, which also contains a suite of sensors for detection of temperature, gas leaks, proximity, and forces. To fully test all of these sensors, a realistic space platform mock-up has been created, complete with visual, temperature, and gas anomalies. Further, changing orbital lighting conditions are effectively mimicked by a robotic solar simulator. In the paper, each of these technology components will be discussed, and experimental results are provided.

Machining of glass and quartz using nanosecond and picosecond laser pulses

NASA Astrophysics Data System (ADS)

Ashkenasi, David; Kaszemeikat, Tristan; Mueller, Norbert; Lemke, Andreas; Eichler, Hans Joachim

2012-03-01

New laser processing strategies in micro processing of glass, quartz and other optically transparent materials are being developed with increasing effort. Utilizing diode-pumped solid-state laser generating nanosecond pulsed green (532 nm) laser light in conjunction with either scanners or special trepanning systems can provide for reliable glass machining at excellent efficiency. Micro ablation can be induced either from the front or rear side of the glass sample. Ablation rates of over 100 μm per pulse can be achieved in rear side processing. In comparison, picosecond laser processing of glass and quartz (at a wavelength of 1064 or 532 nm) yield smaller feed rates at however much better surface and bore wall quality. This is of great importance for small sized features, e.g. through-hole diameters smaller 50 μm in thin glass. Critical for applications with minimum micro cracks and maximum performance is an appropriate distribution of laser pulses over the work piece along with optimum laser parameters. Laser machining tasks are long aspect micro drilling, slanted through holes, internal contour cuts, micro pockets and more complex geometries in e.g. soda-lime glass, B33, B270, D236T, AF45 and BK7 glass, quartz, and Zerodur.

Parsimonious kernel extreme learning machine in primal via Cholesky factorization.

PubMed

Zhao, Yong-Ping

2016-08-01

Recently, extreme learning machine (ELM) has become a popular topic in machine learning community. By replacing the so-called ELM feature mappings with the nonlinear mappings induced by kernel functions, two kernel ELMs, i.e., P-KELM and D-KELM, are obtained from primal and dual perspectives, respectively. Unfortunately, both P-KELM and D-KELM possess the dense solutions in direct proportion to the number of training data. To this end, a constructive algorithm for P-KELM (CCP-KELM) is first proposed by virtue of Cholesky factorization, in which the training data incurring the largest reductions on the objective function are recruited as significant vectors. To reduce its training cost further, PCCP-KELM is then obtained with the application of a probabilistic speedup scheme into CCP-KELM. Corresponding to CCP-KELM, a destructive P-KELM (CDP-KELM) is presented using a partial Cholesky factorization strategy, where the training data incurring the smallest reductions on the objective function after their removals are pruned from the current set of significant vectors. Finally, to verify the efficacy and feasibility of the proposed algorithms in this paper, experiments on both small and large benchmark data sets are investigated. Copyright © 2016 Elsevier Ltd. All rights reserved.

Noise induced hearing loss of forest workers in Turkey.

PubMed

Tunay, M; Melemez, K

2008-09-01

In this study, a total number of 114 workers who were in 3 different groups in terms of age and work underwent audiometric analysis. In order to determine whether there was a statistically significant difference between the hearing loss levels of the workers who were included in the study, variance analysis was applied with the help of the data obtained as a result of the evaluation. Correlation and regression analysis were applied in order to determine the relations between hearing loss and their age and their time of work. As a result of the variance analysis, statistically significant differences were found at 500, 2000 and 4000 Hz frequencies. The most specific difference was observed among chainsaw machine operators at 4000 Hz frequency, which was determined by the variance analysis. As a result of the correlation analysis, significant relations were found between time of work and hearing loss in 0.01 confidence level and between age and hearing loss in 0.05 confidence level. Forest workers using chainsaw machines should be informed, they should wear or use protective materials and less noising chainsaw machines should be used if possible and workers should undergo audiometric tests when they start work and once a year.

Rainfall-induced Landslide Susceptibility assessment at the Longnan county

NASA Astrophysics Data System (ADS)

Hong, Haoyuan; Zhang, Ying

2017-04-01

Landslides are a serious disaster in Longnan county, China. Therefore landslide susceptibility assessment is useful tool for government or decision making. The main objective of this study is to investigate and compare the frequency ratio, support vector machines, and logistic regression. The Longnan county (Jiangxi province, China) was selected as the case study. First, the landslide inventory map with 354 landslide locations was constructed. Then landslide locations were then randomly divided into a ratio of 70/30 for the training and validating the models. Second, fourteen landslide conditioning factors were prepared such as slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), plan curvature, lithology, distance to faults, distance to rivers, distance to roads, land use, normalized difference vegetation index (NDVI), and rainfall. Using the frequency ratio, support vector machines, and logistic regression, a total of three landslide susceptibility models were constructed. Finally, the overall performance of the resulting models was assessed and compared using the Receiver operating characteristic (ROC) curve technique. The result showed that the support vector machines model is the best model in the study area. The success rate is 88.39 %; and prediction rate is 84.06 %.

The RNA-induced silencing complex: a versatile gene-silencing machine.

PubMed

Pratt, Ashley J; MacRae, Ian J

2009-07-03

RNA interference is a powerful mechanism of gene silencing that underlies many aspects of eukaryotic biology. On the molecular level, RNA interference is mediated by a family of ribonucleoprotein complexes called RNA-induced silencing complexes (RISCs), which can be programmed to target virtually any nucleic acid sequence for silencing. The ability of RISC to locate target RNAs has been co-opted by evolution many times to generate a broad spectrum of gene-silencing pathways. Here, we review the fundamental biochemical and biophysical properties of RISC that facilitate gene targeting and describe the various mechanisms of gene silencing known to exploit RISC activity.

Design and fabrication of sub-wavelength annular apertures on fiber tip for femtosecond laser machining

NASA Astrophysics Data System (ADS)

Tung, Yen-Chun; Chung, Ming-Han; Sung, I.-Hui; Lee, Chih-Kung

2014-03-01

Adopting optical technique to pursue micromachining must make a compromise between the focal spot sizes the depth of focus. The focal spot size determines the minimum features can be fabricated. On the other hand, the depth of focus influences the ease of alignment in positioning the fabrication light beam. A typical approach to bypass the diffraction limit is to adopt the near-field approach, which has spot size in the range of the optical fiber tip. However, the depth of focus of the emitted light beam will be limited to tens of nanometers in most cases, which posts a difficult challenge to control the distance between the optical fiber tip and the sample to be machined optically. More specifically, problems remained in this machining approach, which include issues such as residue induced by laser ablation tends to deposit near the optical fiber tip and leads to loss of coupling efficiency. We proposed a method based on illuminating femtosecond laser through a sub-wavelength annular aperture on metallic film so as to produce Bessel light beam of sub-wavelength while maintaining large depth of focus first. To further advance the ease of use in one such system, producing sub-wavelength annular aperture on a single mode optical fiber head with sub-wavelength focusing ability is detailed. It is shown that this method can be applied in material machining with an emphasis to produce high aspect ratio structure. Simulations and experimental results are presented in this paper.

Ion Beam Surface Treatment: A New Technique for Thermally Modifying Surfaces Using Intense, Pulsed Ion Beams

DTIC Science & Technology

1995-07-01

background gasses in the treatment chamber. Polishing of Ti - 6Al - 4V In other experiments we treated Ti - 6Al - 4V on the Anaconda accelerator (400 kV peak...4. IBEST treatment of a Ti - 6Al - 4V machined surface (top) treated with 4, 400 ns, 7 J/cm2 mixed proton and carbon beam pulses demonstrates...Figure 8. This technique shows promise for surface porosity reduction but also shows some microcracking on a 0.1 micron scale. 50 Before After Figure

Proceedings of the Brain Mapping Machine Design Workshop Held in College Station, TX on 10-16 August, 1985. Volume 3. Background Papers Submitted by Participants.

DTIC Science & Technology

1985-08-01

interactively. First, with the "tissue highlight" function, the user must define the range of intensity values (in Hounsfield units ) corresponding to the...Cosponsored by the United States Army Medical Research and Development Command, Scripps Clinic and Research Foundation, Texas A&M University, University of...Research & Development Command DAMDI7-85-G-5042 Sc. ADDRESS (City, State, and ZIP Code) 10. SOURCE OF FUNDING NUMBERS e PROGRAM PROJECT TASK IWORK UNIT