Advanced Concept

NASA Image and Video Library

2008-02-15

Testing of the Ascent Thrust Vector Control System in support of the Ares 1-X program at the Marshall Space Flight Center in Huntsville, Alabama. This image is extracted from a high definition video file and is the highest resolution available

On the sparseness of 1-norm support vector machines.

PubMed

Zhang, Li; Zhou, Weida

2010-04-01

There is some empirical evidence available showing that 1-norm Support Vector Machines (1-norm SVMs) have good sparseness; however, both how good sparseness 1-norm SVMs can reach and whether they have a sparser representation than that of standard SVMs are not clear. In this paper we take into account the sparseness of 1-norm SVMs. Two upper bounds on the number of nonzero coefficients in the decision function of 1-norm SVMs are presented. First, the number of nonzero coefficients in 1-norm SVMs is at most equal to the number of only the exact support vectors lying on the +1 and -1 discriminating surfaces, while that in standard SVMs is equal to the number of support vectors, which implies that 1-norm SVMs have better sparseness than that of standard SVMs. Second, the number of nonzero coefficients is at most equal to the rank of the sample matrix. A brief review of the geometry of linear programming and the primal steepest edge pricing simplex method are given, which allows us to provide the proof of the two upper bounds and evaluate their tightness by experiments. Experimental results on toy data sets and the UCI data sets illustrate our analysis. Copyright 2009 Elsevier Ltd. All rights reserved.

Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.

PubMed

Cannon, Edward O; Amini, Ata; Bender, Andreas; Sternberg, Michael J E; Muggleton, Stephen H; Glen, Robert C; Mitchell, John B O

2007-05-01

We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.

Solar vector magnetograph for Max 1991 programs

NASA Technical Reports Server (NTRS)

Rust, D. M.; Obyrne, J. W.; Harris, T. J.

1988-01-01

An instrument for measuring solar magnetic fields is under construction. Key requirements for any solar vector magnetograph are high spatial resolution, high optical throughput, fine spectral selectivity, and ultralow instrumental polarization. An available 25 cm Cassegrain telescope will provide 0.5 arcsec spatial resolution. Spectral selection will be accomplished with a 150 mA filter based on electrically tunable solid Fabry-Perot etalon. Filter and polarization analyzer design concepts for the magnetograph are described in detail. The instrument will be tested at JHU/APL, and then moved to the National Solar Observatory in late 1988. It will be available to support the Max 1991 program.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

PubMed

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Support vector machine firefly algorithm based optimization of lens system.

PubMed

Shamshirband, Shahaboddin; Petković, Dalibor; Pavlović, Nenad T; Ch, Sudheer; Altameem, Torki A; Gani, Abdullah

2015-01-01

Lens system design is an important factor in image quality. The main aspect of the lens system design methodology is the optimization procedure. Since optimization is a complex, nonlinear task, soft computing optimization algorithms can be used. There are many tools that can be employed to measure optical performance, but the spot diagram is the most useful. The spot diagram gives an indication of the image of a point object. In this paper, the spot size radius is considered an optimization criterion. Intelligent soft computing scheme support vector machines (SVMs) coupled with the firefly algorithm (FFA) are implemented. The performance of the proposed estimators is confirmed with the simulation results. The result of the proposed SVM-FFA model has been compared with support vector regression (SVR), artificial neural networks, and generic programming methods. The results show that the SVM-FFA model performs more accurately than the other methodologies. Therefore, SVM-FFA can be used as an efficient soft computing technique in the optimization of lens system designs.

Universal Documentation System Handbook. Volume 2: Program Requirements and Operations Requirements Documents

DTIC Science & Technology

1979-11-01

plane. The local horizontal plane is de- lined as a plane normal to the geocentric position vector. Boxes 11J and UJ are the angles measured east...support the program/mission. BOX 1-9 Follow instructions for Pa«« 1010. BOX 10 LOCATION: Enter the areas or locations that are to be staffed with

The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

NASA Astrophysics Data System (ADS)

Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

2015-08-01

The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

Research on computer systems benchmarking

NASA Technical Reports Server (NTRS)

Smith, Alan Jay (Principal Investigator)

1996-01-01

This grant addresses the topic of research on computer systems benchmarking and is more generally concerned with performance issues in computer systems. This report reviews work in those areas during the period of NASA support under this grant. The bulk of the work performed concerned benchmarking and analysis of CPUs, compilers, caches, and benchmark programs. The first part of this work concerned the issue of benchmark performance prediction. A new approach to benchmarking and machine characterization was reported, using a machine characterizer that measures the performance of a given system in terms of a Fortran abstract machine. Another report focused on analyzing compiler performance. The performance impact of optimization in the context of our methodology for CPU performance characterization was based on the abstract machine model. Benchmark programs are analyzed in another paper. A machine-independent model of program execution was developed to characterize both machine performance and program execution. By merging these machine and program characterizations, execution time can be estimated for arbitrary machine/program combinations. The work was continued into the domain of parallel and vector machines, including the issue of caches in vector processors and multiprocessors. All of the afore-mentioned accomplishments are more specifically summarized in this report, as well as those smaller in magnitude supported by this grant.

LAMDA at TREC CDS track 2015: Clinical Decision Support Track

DTIC Science & Technology

2015-11-20

outperforms all the other vector space models supported by Elasticsearch. MetaMap is the online tool that maps biomedical text to the Metathesaurus, and...cases. The medical knowledge consists of 700,000 biomedical documents supported by the PubMed Central [3] which is online digital database freely...Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT , and Future Planning (MSIP

Compositional Verification with Abstraction, Learning, and SAT Solving

DTIC Science & Technology

2015-05-01

arithmetic, and bit-vectors (currently, via bit-blasting). The front-end is based on an existing tool called UFO [8] which converts C programs to the Horn...supports propositional logic, linear arithmetic, and bit-vectors (via bit-blasting). The front-end is based on the tool UFO [8]. It encodes safety of...tool UFO [8]. The encoding in Horn-SMT only uses the theory of Linear Rational Arithmetic. All experiments were carried out on an Intel R© CoreTM2 Quad

Dengue and climate change in Australia: predictions for the future should incorporate knowledge from the past.

PubMed

Russell, Richard C; Currie, Bart J; Lindsay, Michael D; Mackenzie, John S; Ritchie, Scott A; Whelan, Peter I

2009-03-02

Dengue transmission in Australia is currently restricted to Queensland, where the vector mosquito Aedes aegypti is established. Locally acquired infections have been reported only from urban areas in the north-east of the state, where the vector is most abundant. Considerable attention has been drawn to the potential impact of climate change on dengue distribution within Australia, with projections for substantial rises in incidence and distribution associated with increasing temperatures. However, historical data show that much of Australia has previously sustained both the vector mosquito and dengue viruses. Although current vector distribution is restricted to Queensland, the area inhabited by A. aegypti is larger than the disease-transmission areas, and is not restricted by temperature (or vector-control programs); thus, it is unlikely that rising temperatures alone will bring increased vector or virus distribution. Factors likely to be important to dengue and vector distribution in the future include increased dengue activity in Asian and Pacific nations that would raise rates of virus importation by travellers, importation of vectors via international ports to regions without A. aegypti, higher rates of domestic collection and storage of water that would provide habitat in urban areas, and growing human populations in northern Australia. Past and recent successful control initiatives in Australia lend support to the idea that well resourced and functioning surveillance programs, and effective public health intervention capabilities, are essential to counter threats from dengue and other mosquito-borne diseases. Models projecting future activity of dengue (or other vector-borne disease) with climate change should carefully consider the local historical and contemporary data on the ecology and distribution of the vector and local virus transmission.

LANDMARK-BASED SPEECH RECOGNITION: REPORT OF THE 2004 JOHNS HOPKINS SUMMER WORKSHOP.

PubMed

Hasegawa-Johnson, Mark; Baker, James; Borys, Sarah; Chen, Ken; Coogan, Emily; Greenberg, Steven; Juneja, Amit; Kirchhoff, Katrin; Livescu, Karen; Mohan, Srividya; Muller, Jennifer; Sonmez, Kemal; Wang, Tianyu

2005-01-01

Three research prototype speech recognition systems are described, all of which use recently developed methods from artificial intelligence (specifically support vector machines, dynamic Bayesian networks, and maximum entropy classification) in order to implement, in the form of an automatic speech recognizer, current theories of human speech perception and phonology (specifically landmark-based speech perception, nonlinear phonology, and articulatory phonology). All three systems begin with a high-dimensional multiframe acoustic-to-distinctive feature transformation, implemented using support vector machines trained to detect and classify acoustic phonetic landmarks. Distinctive feature probabilities estimated by the support vector machines are then integrated using one of three pronunciation models: a dynamic programming algorithm that assumes canonical pronunciation of each word, a dynamic Bayesian network implementation of articulatory phonology, or a discriminative pronunciation model trained using the methods of maximum entropy classification. Log probability scores computed by these models are then combined, using log-linear combination, with other word scores available in the lattice output of a first-pass recognizer, and the resulting combination score is used to compute a second-pass speech recognition output.

Capacity-Building Efforts by the AFHSC-GEIS Program

DTIC Science & Technology

2011-01-01

and equipment support was provided to over 500 field sites in 74 countries worldwide from October 2008 to September 2009. These activities allowed...and equipment support was provided to over 500 field sites in 74 countries worldwide from October 2008 to September 2009. These activities allowed...other vector-borne ill- nesses, acute diarrheal diseases, antimalarial and antimi- crobial resistance, sexually transmitted diseases, and bacterial

Forecasting Caspian Sea level changes using satellite altimetry data (June 1992-December 2013) based on evolutionary support vector regression algorithms and gene expression programming

NASA Astrophysics Data System (ADS)

Imani, Moslem; You, Rey-Jer; Kuo, Chung-Yen

2014-10-01

Sea level forecasting at various time intervals is of great importance in water supply management. Evolutionary artificial intelligence (AI) approaches have been accepted as an appropriate tool for modeling complex nonlinear phenomena in water bodies. In the study, we investigated the ability of two AI techniques: support vector machine (SVM), which is mathematically well-founded and provides new insights into function approximation, and gene expression programming (GEP), which is used to forecast Caspian Sea level anomalies using satellite altimetry observations from June 1992 to December 2013. SVM demonstrates the best performance in predicting Caspian Sea level anomalies, given the minimum root mean square error (RMSE = 0.035) and maximum coefficient of determination (R2 = 0.96) during the prediction periods. A comparison between the proposed AI approaches and the cascade correlation neural network (CCNN) model also shows the superiority of the GEP and SVM models over the CCNN.

A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine

NASA Astrophysics Data System (ADS)

Kong, Xianyu; Che, Xiaowei; Su, Rongguo; Zhang, Chuansong; Yao, Qingzhen; Shi, Xiaoyong

2017-05-01

There is an urgent need to develop efficient evaluation tools that use easily measured variables to make rapid and timely eutrophication assessments, which are important for marine health management, and to implement eutrophication monitoring programs. In this study, an approach for rapidly assessing the eutrophication status of coastal waters with three easily measured parameters (turbidity, chlorophyll a and dissolved oxygen) was developed by the grid search (GS) optimized support vector machine (SVM), with trophic index TRIX classification results as the reference. With the optimized penalty parameter C =64 and the kernel parameter γ =1, the classification accuracy rates reached 89.3% for the training data, 88.3% for the cross-validation, and 88.5% for the validation dataset. Because the developed approach only used three easy-to-measure variables, its application could facilitate the rapid assessment of the eutrophication status of coastal waters, resulting in potential cost savings in marine monitoring programs and assisting in the provision of timely advice for marine management.

Building a computer program to support children, parents, and distraction during healthcare procedures.

PubMed

Hanrahan, Kirsten; McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W Nick; Zimmerman, M Bridget; Ersig, Anne L

2012-10-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, titled Children, Parents and Distraction, is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure.

Improvements on ν-Twin Support Vector Machine.

PubMed

Khemchandani, Reshma; Saigal, Pooja; Chandra, Suresh

2016-07-01

In this paper, we propose two novel binary classifiers termed as "Improvements on ν-Twin Support Vector Machine: Iν-TWSVM and Iν-TWSVM (Fast)" that are motivated by ν-Twin Support Vector Machine (ν-TWSVM). Similar to ν-TWSVM, Iν-TWSVM determines two nonparallel hyperplanes such that they are closer to their respective classes and are at least ρ distance away from the other class. The significant advantage of Iν-TWSVM over ν-TWSVM is that Iν-TWSVM solves one smaller-sized Quadratic Programming Problem (QPP) and one Unconstrained Minimization Problem (UMP); as compared to solving two related QPPs in ν-TWSVM. Further, Iν-TWSVM (Fast) avoids solving a smaller sized QPP and transforms it as a unimodal function, which can be solved using line search methods and similar to Iν-TWSVM, the other problem is solved as a UMP. Due to their novel formulation, the proposed classifiers are faster than ν-TWSVM and have comparable generalization ability. Iν-TWSVM also implements structural risk minimization (SRM) principle by introducing a regularization term, along with minimizing the empirical risk. The other properties of Iν-TWSVM, related to support vectors (SVs), are similar to that of ν-TWSVM. To test the efficacy of the proposed method, experiments have been conducted on a wide range of UCI and a skewed variation of NDC datasets. We have also given the application of Iν-TWSVM as a binary classifier for pixel classification of color images. Copyright © 2016 Elsevier Ltd. All rights reserved.

Research on bearing fault diagnosis of large machinery based on mathematical morphology

NASA Astrophysics Data System (ADS)

Wang, Yu

2018-04-01

To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

Review of V/STOL lift/cruise fan technology

NASA Technical Reports Server (NTRS)

Rolls, L. S.; Quigley, H. C.; Perkins, R. G., Jr.

1976-01-01

This paper presents an overview of supporting technology programs conducted to reduce the risk in the joint NASA/Navy Lift/Cruise Fan Research and Technology Aircraft Program. The aeronautical community has endeavored to combine the low-speed and lifting capabilities of the helicopter with the high-speed capabilities of the jet aircraft; recent developments have indicated a lift/cruise fan propulsion system may provide these desired characteristics. NASA and the Navy have formulated a program that will provide a research and technology aircraft to furnish viability of the lift/cruise fan aircraft through flight experiences and obtain data on designs for future naval and civil V/STOL aircraft. The supporting technology programs discussed include: (1) design studies for operational aircraft, a research and technology aircraft, and associated propulsion systems; (2) wind-tunnel tests of several configurations; (3) propulsion-system thrust vectoring tests; and (4) simulation. These supporting technology programs have indicated that a satisfactory research and technology aircraft program can be accomplished within the current level of technology.

Extending R packages to support 64-bit compiled code: An illustration with spam64 and GIMMS NDVI3g data

NASA Astrophysics Data System (ADS)

Gerber, Florian; Mösinger, Kaspar; Furrer, Reinhard

2017-07-01

Software packages for spatial data often implement a hybrid approach of interpreted and compiled programming languages. The compiled parts are usually written in C, C++, or Fortran, and are efficient in terms of computational speed and memory usage. Conversely, the interpreted part serves as a convenient user-interface and calls the compiled code for computationally demanding operations. The price paid for the user friendliness of the interpreted component is-besides performance-the limited access to low level and optimized code. An example of such a restriction is the 64-bit vector support of the widely used statistical language R. On the R side, users do not need to change existing code and may not even notice the extension. On the other hand, interfacing 64-bit compiled code efficiently is challenging. Since many R packages for spatial data could benefit from 64-bit vectors, we investigate strategies to efficiently pass 64-bit vectors to compiled languages. More precisely, we show how to simply extend existing R packages using the foreign function interface to seamlessly support 64-bit vectors. This extension is shown with the sparse matrix algebra R package spam. The new capabilities are illustrated with an example of GIMMS NDVI3g data featuring a parametric modeling approach for a non-stationary covariance matrix.

Using an object-based grid system to evaluate a newly developed EP approach to formulate SVMs as applied to the classification of organophosphate nerve agents

NASA Astrophysics Data System (ADS)

Land, Walker H., Jr.; Lewis, Michael; Sadik, Omowunmi; Wong, Lut; Wanekaya, Adam; Gonzalez, Richard J.; Balan, Arun

2004-04-01

This paper extends the classification approaches described in reference [1] in the following way: (1.) developing and evaluating a new method for evolving organophosphate nerve agent Support Vector Machine (SVM) classifiers using Evolutionary Programming, (2.) conducting research experiments using a larger database of organophosphate nerve agents, and (3.) upgrading the architecture to an object-based grid system for evaluating the classification of EP derived SVMs. Due to the increased threats of chemical and biological weapons of mass destruction (WMD) by international terrorist organizations, a significant effort is underway to develop tools that can be used to detect and effectively combat biochemical warfare. This paper reports the integration of multi-array sensors with Support Vector Machines (SVMs) for the detection of organophosphates nerve agents using a grid computing system called Legion. Grid computing is the use of large collections of heterogeneous, distributed resources (including machines, databases, devices, and users) to support large-scale computations and wide-area data access. Finally, preliminary results using EP derived support vector machines designed to operate on distributed systems have provided accurate classification results. In addition, distributed training time architectures are 50 times faster when compared to standard iterative training time methods.

Project MAGNET High-level Vector Survey Data Reduction

NASA Technical Reports Server (NTRS)

Coleman, Rachel J.

1992-01-01

Since 1951, the U.S. Navy, under its Project MAGNET program, has been continuously collecting vector aeromagnetic survey data to support the U.S. Defense Mapping Agency's world magnetic and charting program. During this forty-year period, a variety of survey platforms and instrumentation configurations have been used. The current Project MAGNET survey platform is a Navy Orion RP-3D aircraft which has been specially modified and specially equipped with a redundant suite of navigational positioning, attitude, and magnetic sensors. A review of the survey data collection procedures and calibration and editing techniques applied to the data generated by this suite of instrumentation will be presented. Among the topics covered will be the determination of its parameters from the low-level calibration maneuvers flown over geomagnetic observatories.

A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes.

PubMed

Ebtehaj, Isa; Bonakdari, Hossein

2016-01-01

Sediment transport without deposition is an essential consideration in the optimum design of sewer pipes. In this study, a novel method based on a combination of support vector regression (SVR) and the firefly algorithm (FFA) is proposed to predict the minimum velocity required to avoid sediment settling in pipe channels, which is expressed as the densimetric Froude number (Fr). The efficiency of support vector machine (SVM) models depends on the suitable selection of SVM parameters. In this particular study, FFA is used by determining these SVM parameters. The actual effective parameters on Fr calculation are generally identified by employing dimensional analysis. The different dimensionless variables along with the models are introduced. The best performance is attributed to the model that employs the sediment volumetric concentration (C(V)), ratio of relative median diameter of particles to hydraulic radius (d/R), dimensionless particle number (D(gr)) and overall sediment friction factor (λ(s)) parameters to estimate Fr. The performance of the SVR-FFA model is compared with genetic programming, artificial neural network and existing regression-based equations. The results indicate the superior performance of SVR-FFA (mean absolute percentage error = 2.123%; root mean square error =0.116) compared with other methods.

Prediction of Henry Constants and Adsorption Mechanism of Volatile Organic Compounds on Multi-Walled Carbon Nanotubes by Using Support Vector Regression

NASA Astrophysics Data System (ADS)

Wen-De, Cheng; Cong-Zhong, Cai

2016-04-01

Not Available Supported by the Innovative Talent Funds for Project 985 under Grant No WLYJSBJRCTD201102, the Fundamental Research Funds for the Central Universities under Grant No CQDXWL-2013-014, the Natural Science Foundation of Chongqing under Grant No CSTC2006BB5240, and the Program for New Century Excellent Talents in Universities of China under Grant No NCET-07-0903.

Building a Computer Program to Support Children, Parents, and Distraction during Healthcare Procedures

PubMed Central

McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W. Nick; Zimmerman, M. Bridget; Ersig, Anne L.

2012-01-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children’s responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, the Children, Parents and Distraction (CPaD), is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure. PMID:22805121

Industrial Prep, Volume Four, Junior Year--Contents: Mathematics and Guidance.

ERIC Educational Resources Information Center

Hackensack Public Schools, NJ.

As part of a 3-year comprehensive interdisciplinary program in industrial preparation for vocational students, this 11th Grade teaching guide consists of units on technical mathematics and guidance. Designed as supportive material for related physics and English curriculums, the first four sections of Volume 4 on algebra, vectors, simple machines,…

An object-oriented approach to nested data parallelism

NASA Technical Reports Server (NTRS)

Sheffler, Thomas J.; Chatterjee, Siddhartha

1994-01-01

This paper describes an implementation technique for integrating nested data parallelism into an object-oriented language. Data-parallel programming employs sets of data called 'collections' and expresses parallelism as operations performed over the elements of a collection. When the elements of a collection are also collections, then there is the possibility for 'nested data parallelism.' Few current programming languages support nested data parallelism however. In an object-oriented framework, a collection is a single object. Its type defines the parallel operations that may be applied to it. Our goal is to design and build an object-oriented data-parallel programming environment supporting nested data parallelism. Our initial approach is built upon three fundamental additions to C++. We add new parallel base types by implementing them as classes, and add a new parallel collection type called a 'vector' that is implemented as a template. Only one new language feature is introduced: the 'foreach' construct, which is the basis for exploiting elementwise parallelism over collections. The strength of the method lies in the compilation strategy, which translates nested data-parallel C++ into ordinary C++. Extracting the potential parallelism in nested 'foreach' constructs is called 'flattening' nested parallelism. We show how to flatten 'foreach' constructs using a simple program transformation. Our prototype system produces vector code which has been successfully run on workstations, a CM-2, and a CM-5.

Project Physics Programmed Instruction, Vectors 1.

ERIC Educational Resources Information Center

Harvard Univ., Cambridge, MA. Harvard Project Physics.

This programmed instruction booklet is an interim version of instructional materials being developed by Harvard Project Physics. It is the first in a series of three booklets on vectors and covers the definitions of vectors and scalars, drawing vector quantities to scale, and negative vectors. For others in this series, see SE 015 550 and SE 015…

Project Physics Programmed Instruction, Vectors 2.

ERIC Educational Resources Information Center

Harvard Univ., Cambridge, MA. Harvard Project Physics.

This is the second of a series of three programmed instruction booklets on vectors developed by Harvard Project Physics. It covers adding two or more vectors together, and finding a third vector that could be added to two given vectors to make a sum of zero. For other booklets in this series, see SE 015 549 and SE 015 551. (DT)

A vectorization of the Jameson-Caughey NYU transonic swept-wing computer program FLO-22-V1 for the STAR-100 computer

NASA Technical Reports Server (NTRS)

Smith, R. E.; Pitts, J. I.; Lambiotte, J. J., Jr.

1978-01-01

The computer program FLO-22 for analyzing inviscid transonic flow past 3-D swept-wing configurations was modified to use vector operations and run on the STAR-100 computer. The vectorized version described herein was called FLO-22-V1. Vector operations were incorporated into Successive Line Over-Relaxation in the transformed horizontal direction. Vector relational operations and control vectors were used to implement upwind differencing at supersonic points. A high speed of computation and extended grid domain were characteristics of FLO-22-V1. The new program was not the optimal vectorization of Successive Line Over-Relaxation applied to transonic flow; however, it proved that vector operations can readily be implemented to increase the computation rate of the algorithm.

Spatially explicit multi-criteria decision analysis for managing vector-borne diseases

PubMed Central

2011-01-01

The complex epidemiology of vector-borne diseases creates significant challenges in the design and delivery of prevention and control strategies, especially in light of rapid social and environmental changes. Spatial models for predicting disease risk based on environmental factors such as climate and landscape have been developed for a number of important vector-borne diseases. The resulting risk maps have proven value for highlighting areas for targeting public health programs. However, these methods generally only offer technical information on the spatial distribution of disease risk itself, which may be incomplete for making decisions in a complex situation. In prioritizing surveillance and intervention strategies, decision-makers often also need to consider spatially explicit information on other important dimensions, such as the regional specificity of public acceptance, population vulnerability, resource availability, intervention effectiveness, and land use. There is a need for a unified strategy for supporting public health decision making that integrates available data for assessing spatially explicit disease risk, with other criteria, to implement effective prevention and control strategies. Multi-criteria decision analysis (MCDA) is a decision support tool that allows for the consideration of diverse quantitative and qualitative criteria using both data-driven and qualitative indicators for evaluating alternative strategies with transparency and stakeholder participation. Here we propose a MCDA-based approach to the development of geospatial models and spatially explicit decision support tools for the management of vector-borne diseases. We describe the conceptual framework that MCDA offers as well as technical considerations, approaches to implementation and expected outcomes. We conclude that MCDA is a powerful tool that offers tremendous potential for use in public health decision-making in general and vector-borne disease management in particular. PMID:22206355

Project Physics Programmed Instruction, Vectors 3.

ERIC Educational Resources Information Center

Harvard Univ., Cambridge, MA. Harvard Project Physics.

This is the third of a series of three programmed instruction booklets on vectors developed by Harvard Project Physics. Separating vectors into components and obtaining a vector from its components are the topics covered. For other booklets in this series, see SE 015 549 and SE 015 550. (DT)

The nondeterministic divide

NASA Technical Reports Server (NTRS)

Charlesworth, Arthur

1990-01-01

The nondeterministic divide partitions a vector into two non-empty slices by allowing the point of division to be chosen nondeterministically. Support for high-level divide-and-conquer programming provided by the nondeterministic divide is investigated. A diva algorithm is a recursive divide-and-conquer sequential algorithm on one or more vectors of the same range, whose division point for a new pair of recursive calls is chosen nondeterministically before any computation is performed and whose recursive calls are made immediately after the choice of division point; also, access to vector components is only permitted during activations in which the vector parameters have unit length. The notion of diva algorithm is formulated precisely as a diva call, a restricted call on a sequential procedure. Diva calls are proven to be intimately related to associativity. Numerous applications of diva calls are given and strategies are described for translating a diva call into code for a variety of parallel computers. Thus diva algorithms separate logical correctness concerns from implementation concerns.

The Vector, Signal, and Image Processing Library (VSIPL): an Open Standard for Astronomical Data Processing

NASA Astrophysics Data System (ADS)

Kepner, J. V.; Janka, R. S.; Lebak, J.; Richards, M. A.

1999-12-01

The Vector/Signal/Image Processing Library (VSIPL) is a DARPA initiated effort made up of industry, government and academic representatives who have defined an industry standard API for vector, signal, and image processing primitives for real-time signal processing on high performance systems. VSIPL supports a wide range of data types (int, float, complex, ...) and layouts (vectors, matrices and tensors) and is ideal for astronomical data processing. The VSIPL API is intended to serve as an open, vendor-neutral, industry standard interface. The object-based VSIPL API abstracts the memory architecture of the underlying machine by using the concept of memory blocks and views. Early experiments with VSIPL code conversions have been carried out by the High Performance Computing Program team at the UCSD. Commercially, several major vendors of signal processors are actively developing implementations. VSIPL has also been explicitly required as part of a recent Rome Labs teraflop procurement. This poster presents the VSIPL API, its functionality and the status of various implementations.

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

PubMed

Wang, Yuanjia; Chen, Tianle; Zeng, Donglin

2016-01-01

Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.

A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.

PubMed

Chen, Zhenyu; Li, Jianping; Wei, Liwei

2007-10-01

Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.

The maximum vector-angular margin classifier and its fast training on large datasets using a core vector machine.

PubMed

Hu, Wenjun; Chung, Fu-Lai; Wang, Shitong

2012-03-01

Although pattern classification has been extensively studied in the past decades, how to effectively solve the corresponding training on large datasets is a problem that still requires particular attention. Many kernelized classification methods, such as SVM and SVDD, can be formulated as the corresponding quadratic programming (QP) problems, but computing the associated kernel matrices requires O(n2)(or even up to O(n3)) computational complexity, where n is the size of the training patterns, which heavily limits the applicability of these methods for large datasets. In this paper, a new classification method called the maximum vector-angular margin classifier (MAMC) is first proposed based on the vector-angular margin to find an optimal vector c in the pattern feature space, and all the testing patterns can be classified in terms of the maximum vector-angular margin ρ, between the vector c and all the training data points. Accordingly, it is proved that the kernelized MAMC can be equivalently formulated as the kernelized Minimum Enclosing Ball (MEB), which leads to a distinctive merit of MAMC, i.e., it has the flexibility of controlling the sum of support vectors like v-SVC and may be extended to a maximum vector-angular margin core vector machine (MAMCVM) by connecting the core vector machine (CVM) method with MAMC such that the corresponding fast training on large datasets can be effectively achieved. Experimental results on artificial and real datasets are provided to validate the power of the proposed methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Working with and Visualizing Big Data Efficiently with Python for the DARPA XDATA Program

DTIC Science & Technology

2017-08-01

same function to be used with scalar inputs, input arrays of the same shape, or even input arrays of dimensionality in some cases. Most of the math ... math operations on values ● Split-apply-combine: similar to group-by operations in databases ● Join: combine two datasets using common columns 4.3.3...Numba - Continue to increase SIMD performance with support for fast math flags and improved support for AVX, Intel’s large vector

Development of a unified guidance system for geocentric transfer. [for solar electric propulsion spacecraft

NASA Technical Reports Server (NTRS)

Cake, J. E.; Regetz, J. D., Jr.

1975-01-01

A method is presented for open loop guidance of a solar electric propulsion spacecraft to geosynchronous orbit. The method consists of determining the thrust vector profiles on the ground with an optimization computer program, and performing updates based on the difference between the actual trajectory and that predicted with a precision simulation computer program. The motivation for performing the guidance analysis during the mission planning phase is discussed, and a spacecraft design option that employs attitude orientation constraints is presented. The improvements required in both the optimization program and simulation program are set forth, together with the efforts to integrate the programs into the ground support software for the guidance system.

Development of a unified guidance system for geocentric transfer. [solar electric propulsion spacecraft

NASA Technical Reports Server (NTRS)

Cake, J. E.; Regetz, J. D., Jr.

1975-01-01

A method is presented for open loop guidance of a solar electric propulsion spacecraft to geosynchronsus orbit. The method consists of determining the thrust vector profiles on the ground with an optimization computer program, and performing updates based on the difference between the actual trajectory and that predicted with a precision simulation computer program. The motivation for performing the guidance analysis during the mission planning phase is discussed, and a spacecraft design option that employs attitude orientation constraints is presented. The improvements required in both the optimization program and simulation program are set forth, together with the efforts to integrate the programs into the ground support software for the guidance system.

Robust support vector regression networks for function approximation with outliers.

PubMed

Chuang, Chen-Chia; Su, Shun-Feng; Jeng, Jin-Tsong; Hsiao, Chih-Ching

2002-01-01

Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. When the parameters used in SVR are improperly selected, overfitting phenomena may still occur. However, the selection of various parameters is not straightforward. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. In this paper, a novel regression approach, termed as the robust support vector regression (RSVR) network, is proposed to enhance the robust capability of SVR. In the approach, traditional robust learning approaches are employed to improve the learning performance for any selected parameters. From the simulation results, our RSVR can always improve the performance of the learned systems for all cases. Besides, it can be found that even the training lasted for a long period, the testing errors would not go up. In other words, the overfitting phenomenon is indeed suppressed.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Currency crisis indication by using ensembles of support vector machine classifiers

NASA Astrophysics Data System (ADS)

Ramli, Nor Azuana; Ismail, Mohd Tahir; Wooi, Hooy Chee

2014-07-01

There are many methods that had been experimented in the analysis of currency crisis. However, not all methods could provide accurate indications. This paper introduces an ensemble of classifiers by using Support Vector Machine that's never been applied in analyses involving currency crisis before with the aim of increasing the indication accuracy. The proposed ensemble classifiers' performances are measured using percentage of accuracy, root mean squared error (RMSE), area under the Receiver Operating Characteristics (ROC) curve and Type II error. The performances of an ensemble of Support Vector Machine classifiers are compared with the single Support Vector Machine classifier and both of classifiers are tested on the data set from 27 countries with 12 macroeconomic indicators for each country. From our analyses, the results show that the ensemble of Support Vector Machine classifiers outperforms single Support Vector Machine classifier on the problem involving indicating a currency crisis in terms of a range of standard measures for comparing the performance of classifiers.

Multithreading in vector processors

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

Evangelinos, Constantinos; Kim, Changhoan; Nair, Ravi

In one embodiment, a system includes a processor having a vector processing mode and a multithreading mode. The processor is configured to operate on one thread per cycle in the multithreading mode. The processor includes a program counter register having a plurality of program counters, and the program counter register is vectorized. Each program counter in the program counter register represents a distinct corresponding thread of a plurality of threads. The processor is configured to execute the plurality of threads by activating the plurality of program counters in a round robin cycle.

Semisupervised Support Vector Machines With Tangent Space Intrinsic Manifold Regularization.

PubMed

Sun, Shiliang; Xie, Xijiong

2016-09-01

Semisupervised learning has been an active research topic in machine learning and data mining. One main reason is that labeling examples is expensive and time-consuming, while there are large numbers of unlabeled examples available in many practical problems. So far, Laplacian regularization has been widely used in semisupervised learning. In this paper, we propose a new regularization method called tangent space intrinsic manifold regularization. It is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in the formulation of the regularization are local tangent space representations, which are estimated by local principal component analysis, and the connections that relate adjacent tangent spaces. Simultaneously, we explore its application to semisupervised classification and propose two new learning algorithms called tangent space intrinsic manifold regularized support vector machines (TiSVMs) and tangent space intrinsic manifold regularized twin SVMs (TiTSVMs). They effectively integrate the tangent space intrinsic manifold regularization consideration. The optimization of TiSVMs can be solved by a standard quadratic programming, while the optimization of TiTSVMs can be solved by a pair of standard quadratic programmings. The experimental results of semisupervised classification problems show the effectiveness of the proposed semisupervised learning algorithms.

An analysis of cross-coupling of a multicomponent jet engine test stand using finite element modeling techniques

NASA Technical Reports Server (NTRS)

Schweikhard, W. G.; Singnoi, W. N.

1985-01-01

A two axis thrust measuring system was analyzed by using a finite a element computer program to determine the sensitivities of the thrust vectoring nozzle system to misalignment of the load cells and applied loads, and the stiffness of the structural members. Three models were evaluated: (1) the basic measuring element and its internal calibration load cells; (2) the basic measuring element and its external load calibration equipment; and (3) the basic measuring element, external calibration load frame and the altitude facility support structure. Alignment of calibration loads was the greatest source of error for multiaxis thrust measuring systems. Uniform increases or decreases in stiffness of the members, which might be caused by the selection of the materials, have little effect on the accuracy of the measurements. It is found that the POLO-FINITE program is a viable tool for designing and analyzing multiaxis thrust measurement systems. The response of the test stand to step inputs that might be encountered with thrust vectoring tests was determined. The dynamic analysis show a potential problem for measuring the dynamic response characteristics of thrust vectoring systems because of the inherently light damping of the test stand.

Experimental investigation of vector static magnetic field detection using an NV center with a single first-shell 13C nuclear spin in diamond

NASA Astrophysics Data System (ADS)

Jiang, Feng-Jian; Ye, Jian-Feng; Jiao, Zheng; Jiang, Jun; Ma, Kun; Yan, Xin-Hu; Lv, Hai-Jiang

2018-05-01

We perform a proof-of-principle experiment that uses a single negatively charged nitrogen–vacancy (NV) color center with a nearest neighbor 13C nuclear spin in diamond to detect the strength and direction (including both polar and azimuth angles) of a static vector magnetic field by optical detection magnetic resonance (ODMR) technique. With the known hyperfine coupling tensor between an NV center and a nearest neighbor 13C nuclear spin, we show that the information of static vector magnetic field could be extracted by observing the pulsed continuous wave (CW) spectrum. Project supported by the National Natural Science Foundation of China (Grant Nos. 11305074, 11135002, and 11275083), the Key Program of the Education Department Outstanding Youth Foundation of Anhui Province, China (Grant No. gxyqZD2017080), and the Education Department Natural Science Foundation of Anhui Province, China (Grant No. KJHS2015B09).

Converging Human and Malaria Vector Diagnostics with Data Management towards an Integrated Holistic One Health Approach.

PubMed

Mitsakakis, Konstantinos; Hin, Sebastian; Müller, Pie; Wipf, Nadja; Thomsen, Edward; Coleman, Michael; Zengerle, Roland; Vontas, John; Mavridis, Konstantinos

2018-02-03

Monitoring malaria prevalence in humans, as well as vector populations, for the presence of Plasmodium , is an integral component of effective malaria control, and eventually, elimination. In the field of human diagnostics, a major challenge is the ability to define, precisely, the causative agent of fever, thereby differentiating among several candidate (also non-malaria) febrile diseases. This requires genetic-based pathogen identification and multiplexed analysis, which, in combination, are hardly provided by the current gold standard diagnostic tools. In the field of vectors, an essential component of control programs is the detection of Plasmodium species within its mosquito vectors, particularly in the salivary glands, where the infective sporozoites reside. In addition, the identification of species composition and insecticide resistance alleles within vector populations is a primary task in routine monitoring activities, aiming to support control efforts. In this context, the use of converging diagnostics is highly desirable for providing comprehensive information, including differential fever diagnosis in humans, and mosquito species composition, infection status, and resistance to insecticides of vectors. Nevertheless, the two fields of human diagnostics and vector control are rarely combined, both at the diagnostic and at the data management end, resulting in fragmented data and mis- or non-communication between various stakeholders. To this direction, molecular technologies, their integration in automated platforms, and the co-assessment of data from multiple diagnostic sources through information and communication technologies are possible pathways towards a unified human vector approach.

Converging Human and Malaria Vector Diagnostics with Data Management towards an Integrated Holistic One Health Approach

PubMed Central

Mitsakakis, Konstantinos; Hin, Sebastian; Wipf, Nadja; Coleman, Michael; Zengerle, Roland; Vontas, John; Mavridis, Konstantinos

2018-01-01

Monitoring malaria prevalence in humans, as well as vector populations, for the presence of Plasmodium, is an integral component of effective malaria control, and eventually, elimination. In the field of human diagnostics, a major challenge is the ability to define, precisely, the causative agent of fever, thereby differentiating among several candidate (also non-malaria) febrile diseases. This requires genetic-based pathogen identification and multiplexed analysis, which, in combination, are hardly provided by the current gold standard diagnostic tools. In the field of vectors, an essential component of control programs is the detection of Plasmodium species within its mosquito vectors, particularly in the salivary glands, where the infective sporozoites reside. In addition, the identification of species composition and insecticide resistance alleles within vector populations is a primary task in routine monitoring activities, aiming to support control efforts. In this context, the use of converging diagnostics is highly desirable for providing comprehensive information, including differential fever diagnosis in humans, and mosquito species composition, infection status, and resistance to insecticides of vectors. Nevertheless, the two fields of human diagnostics and vector control are rarely combined, both at the diagnostic and at the data management end, resulting in fragmented data and mis- or non-communication between various stakeholders. To this direction, molecular technologies, their integration in automated platforms, and the co-assessment of data from multiple diagnostic sources through information and communication technologies are possible pathways towards a unified human vector approach. PMID:29401670

A vectorized algorithm for 3D dynamics of a tethered satellite

NASA Technical Reports Server (NTRS)

Wilson, Howard B.

1989-01-01

Equations of motion characterizing the three dimensional motion of a tethered satellite during the retrieval phase are studied. The mathematical model involves an arbitrary number of point masses connected by weightless cords. Motion occurs in a gravity gradient field. The formulation presented accounts for general functions describing support point motion, rate of tether retrieval, and arbitrary forces applied to the point masses. The matrix oriented program language MATLAB is used to produce an efficient vectorized formulation for computing natural frequencies and mode shapes for small oscillations about the static equilibrium configuration; and for integrating the nonlinear differential equations governing large amplitude motions. An example of time response pertaining to the skip rope effect is investigated.

Signal detection using support vector machines in the presence of ultrasonic speckle

NASA Astrophysics Data System (ADS)

Kotropoulos, Constantine L.; Pitas, Ioannis

2002-04-01

Support Vector Machines are a general algorithm based on guaranteed risk bounds of statistical learning theory. They have found numerous applications, such as in classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. In this paper we propose the use of support vector machines to segment lesions in ultrasound images and we assess thoroughly their lesion detection ability. We demonstrate that trained support vector machines with a Radial Basis Function kernel segment satisfactorily (unseen) ultrasound B-mode images as well as clinical ultrasonic images.

SH2 Ligand Prediction-Guidance for In-Silico Screening.

PubMed

Li, Shawn S C; Li, Lei

2017-01-01

Systematic identification of binding partners for SH2 domains is important for understanding the biological function of the corresponding SH2 domain-containing proteins. Here, we describe two different web-accessible computer programs, SMALI and DomPep, for predicting binding ligands for SH2 domains. The former was developed using a Scoring Matrix method and the latter based on the Support Vector Machine model.

Joint Data Management for MOVINT Data-to-Decision Making

DTIC Science & Technology

2011-07-01

flux tensor , aligned motion history images, and related approaches have been shown to be versatile approaches [12, 16, 17, 18]. Scaling these...methods include voting , neural networks, fuzzy logic, neuro-dynamic programming, support vector machines, Bayesian and Dempster-Shafer methods. One way...Information Fusion, 2010. [16] F. Bunyak, K. Palaniappan, S. K. Nath, G. Seetharaman, “Flux tensor constrained geodesic active contours with sensor fusion

A general approach for developing system-specific functions to score protein-ligand docked complexes using support vector inductive logic programming.

PubMed

Amini, Ata; Shrimpton, Paul J; Muggleton, Stephen H; Sternberg, Michael J E

2007-12-01

Despite the increased recent use of protein-ligand and protein-protein docking in the drug discovery process due to the increases in computational power, the difficulty of accurately ranking the binding affinities of a series of ligands or a series of proteins docked to a protein receptor remains largely unsolved. This problem is of major concern in lead optimization procedures and has lead to the development of scoring functions tailored to rank the binding affinities of a series of ligands to a specific system. However, such methods can take a long time to develop and their transferability to other systems remains open to question. Here we demonstrate that given a suitable amount of background information a new approach using support vector inductive logic programming (SVILP) can be used to produce system-specific scoring functions. Inductive logic programming (ILP) learns logic-based rules for a given dataset that can be used to describe properties of each member of the set in a qualitative manner. By combining ILP with support vector machine regression, a quantitative set of rules can be obtained. SVILP has previously been used in a biological context to examine datasets containing a series of singular molecular structures and properties. Here we describe the use of SVILP to produce binding affinity predictions of a series of ligands to a particular protein. We also for the first time examine the applicability of SVILP techniques to datasets consisting of protein-ligand complexes. Our results show that SVILP performs comparably with other state-of-the-art methods on five protein-ligand systems as judged by similar cross-validated squares of their correlation coefficients. A McNemar test comparing SVILP to CoMFA and CoMSIA across the five systems indicates our method to be significantly better on one occasion. The ability to graphically display and understand the SVILP-produced rules is demonstrated and this feature of ILP can be used to derive hypothesis for future ligand design in lead optimization procedures. The approach can readily be extended to evaluate the binding affinities of a series of protein-protein complexes. (c) 2007 Wiley-Liss, Inc.

Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy.

PubMed

Wang, Zhi-Long; Zhou, Zhi-Guo; Chen, Ying; Li, Xiao-Ting; Sun, Ying-Shi

The aim of this study was to diagnose lymph node metastasis of esophageal cancer by support vector machines model based on computed tomography. A total of 131 esophageal cancer patients with preoperative chemotherapy and radical surgery were included. Various indicators (tumor thickness, tumor length, tumor CT value, total number of lymph nodes, and long axis and short axis sizes of largest lymph node) on CT images before and after neoadjuvant chemotherapy were recorded. A support vector machines model based on these CT indicators was built to predict lymph node metastasis. Support vector machines model diagnosed lymph node metastasis better than preoperative short axis size of largest lymph node on CT. The area under the receiver operating characteristic curves were 0.887 and 0.705, respectively. The support vector machine model of CT images can help diagnose lymph node metastasis in esophageal cancer with preoperative chemotherapy.

Experiences in using the CYBER 203 for three-dimensional transonic flow calculations

NASA Technical Reports Server (NTRS)

Melson, N. D.; Keller, J. D.

1982-01-01

In this paper, the authors report on some of their experiences modifying two three-dimensional transonic flow programs (FLO22 and FLO27) for use on the NASA Langley Research Center CYBER 203. Both of the programs discussed were originally written for use on serial machines. Several methods were attempted to optimize the execution of the two programs on the vector machine, including: (1) leaving the program in a scalar form (i.e., serial computation) with compiler software used to optimize and vectorize the program, (2) vectorizing parts of the existing algorithm in the program, and (3) incorporating a new vectorizable algorithm (ZEBRA I or ZEBRA II) in the program.

Adaptive Hybrid Picture Coding. Volume 2.

DTIC Science & Technology

1985-02-01

ooo5 V.a Measurement Vector ..eho..............57 V.b Size Variable o .entroi* Vector .......... .- 59 V * c Shape Vector .Ř 0-60o oe 6 I V~d...the Program for the Adaptive Line of Sight Method .i.. 18.. o ... .... .... 1 B Details of the Feature Vector FormationProgram .. o ...oo..-....- .122 C ...shape recognition is analogous to recognition of curves in space. Therefore, well known concepts and theorems from differential geometry can be 34 . o

VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases

PubMed Central

Giraldo-Calderón, Gloria I.; Emrich, Scott J.; MacCallum, Robert M.; Maslen, Gareth; Dialynas, Emmanuel; Topalis, Pantelis; Ho, Nicholas; Gesing, Sandra; Madey, Gregory; Collins, Frank H.; Lawson, Daniel

2015-01-01

VectorBase is a National Institute of Allergy and Infectious Diseases supported Bioinformatics Resource Center (BRC) for invertebrate vectors of human pathogens. Now in its 11th year, VectorBase currently hosts the genomes of 35 organisms including a number of non-vectors for comparative analysis. Hosted data range from genome assemblies with annotated gene features, transcript and protein expression data to population genetics including variation and insecticide-resistance phenotypes. Here we describe improvements to our resource and the set of tools available for interrogating and accessing BRC data including the integration of Web Apollo to facilitate community annotation and providing Galaxy to support user-based workflows. VectorBase also actively supports our community through hands-on workshops and online tutorials. All information and data are freely available from our website at https://www.vectorbase.org/. PMID:25510499

Use of CYBER 203 and CYBER 205 computers for three-dimensional transonic flow calculations

NASA Technical Reports Server (NTRS)

Melson, N. D.; Keller, J. D.

1983-01-01

Experiences are discussed for modifying two three-dimensional transonic flow computer programs (FLO 22 and FLO 27) for use on the CDC CYBER 203 computer system. Both programs were originally written for use on serial machines. Several methods were attempted to optimize the execution of the two programs on the vector machine: leaving the program in a scalar form (i.e., serial computation) with compiler software used to optimize and vectorize the program, vectorizing parts of the existing algorithm in the program, and incorporating a vectorizable algorithm (ZEBRA I or ZEBRA II) in the program. Comparison runs of the programs were made on CDC CYBER 175. CYBER 203, and two pipe CDC CYBER 205 computer systems.

Advanced development of atmospheric models. [SEASAT Program support

NASA Technical Reports Server (NTRS)

Kesel, P. G.; Langland, R. A.; Stephens, P. L.; Welleck, R. E.; Wolff, P. M.

1979-01-01

A set of atmospheric analysis and prediction models was developed in support of the SEASAT Program existing objective analysis models which utilize a 125x125 polar stereographic grid of the Northern Hemisphere, which were modified in order to incorporate and assess the impact of (real or simulated) satellite data in the analysis of a two-day meteorological scenario in January 1979. Program/procedural changes included: (1) a provision to utilize winds in the sea level pressure and multi-level height analyses (1000-100 MBS); (2) The capability to perform a pre-analysis at two control levels (1000 MBS and 250 MBS); (3) a greater degree of wind- and mass-field coupling, especially at these controls levels; (4) an improved facility to bogus the analyses based on results of the preanalysis; and (5) a provision to utilize (SIRS) satellite thickness values and cloud motion vectors in the multi-level height analysis.

Generating and executing programs for a floating point single instruction multiple data instruction set architecture

DOEpatents

Gschwind, Michael K

2013-04-16

Mechanisms for generating and executing programs for a floating point (FP) only single instruction multiple data (SIMD) instruction set architecture (ISA) are provided. A computer program product comprising a computer recordable medium having a computer readable program recorded thereon is provided. The computer readable program, when executed on a computing device, causes the computing device to receive one or more instructions and execute the one or more instructions using logic in an execution unit of the computing device. The logic implements a floating point (FP) only single instruction multiple data (SIMD) instruction set architecture (ISA), based on data stored in a vector register file of the computing device. The vector register file is configured to store both scalar and floating point values as vectors having a plurality of vector elements.

Ontology for Vector Surveillance and Management

PubMed Central

LOZANO-FUENTES, SAUL; BANDYOPADHYAY, ARITRA; COWELL, LINDSAY G.; GOLDFAIN, ALBERT; EISEN, LARS

2013-01-01

Ontologies, which are made up by standardized and defined controlled vocabulary terms and their interrelationships, are comprehensive and readily searchable repositories for knowledge in a given domain. The Open Biomedical Ontologies (OBO) Foundry was initiated in 2001 with the aims of becoming an “umbrella” for life-science ontologies and promoting the use of ontology development best practices. A software application (OBO-Edit; *.obo file format) was developed to facilitate ontology development and editing. The OBO Foundry now comprises over 100 ontologies and candidate ontologies, including the NCBI organismal classification ontology (NCBITaxon), the Mosquito Insecticide Resistance Ontology (MIRO), the Infectious Disease Ontology (IDO), the IDOMAL malaria ontology, and ontologies for mosquito gross anatomy and tick gross anatomy. We previously developed a disease data management system for dengue and malaria control programs, which incorporated a set of information trees built upon ontological principles, including a “term tree” to promote the use of standardized terms. In the course of doing so, we realized that there were substantial gaps in existing ontologies with regards to concepts, processes, and, especially, physical entities (e.g., vector species, pathogen species, and vector surveillance and management equipment) in the domain of surveillance and management of vectors and vector-borne pathogens. We therefore produced an ontology for vector surveillance and management, focusing on arthropod vectors and vector-borne pathogens with relevance to humans or domestic animals, and with special emphasis on content to support operational activities through inclusion in databases, data management systems, or decision support systems. The Vector Surveillance and Management Ontology (VSMO) includes >2,200 unique terms, of which the vast majority (>80%) were newly generated during the development of this ontology. One core feature of the VSMO is the linkage, through the has_vector relation, of arthropod species to the pathogenic microorganisms for which they serve as biological vectors. We also recognized and addressed a potential roadblock for use of the VSMO by the vector-borne disease community: the difficulty in extracting information from OBO-Edit ontology files (*.obo files) and exporting the information to other file formats. A novel ontology explorer tool was developed to facilitate extraction and export of information from the VSMO *.obo file into lists of terms and their associated unique IDs in *.txt or *.csv file formats. These lists can then be imported into a database or data management system for use as select lists with predefined terms. This is an important step to ensure that the knowledge contained in our ontology can be put into practical use. PMID:23427646

Ontology for vector surveillance and management.

PubMed

Lozano-Fuentes, Saul; Bandyopadhyay, Aritra; Cowell, Lindsay G; Goldfain, Albert; Eisen, Lars

2013-01-01

Ontologies, which are made up by standardized and defined controlled vocabulary terms and their interrelationships, are comprehensive and readily searchable repositories for knowledge in a given domain. The Open Biomedical Ontologies (OBO) Foundry was initiated in 2001 with the aims of becoming an "umbrella" for life-science ontologies and promoting the use of ontology development best practices. A software application (OBO-Edit; *.obo file format) was developed to facilitate ontology development and editing. The OBO Foundry now comprises over 100 ontologies and candidate ontologies, including the NCBI organismal classification ontology (NCBITaxon), the Mosquito Insecticide Resistance Ontology (MIRO), the Infectious Disease Ontology (IDO), the IDOMAL malaria ontology, and ontologies for mosquito gross anatomy and tick gross anatomy. We previously developed a disease data management system for dengue and malaria control programs, which incorporated a set of information trees built upon ontological principles, including a "term tree" to promote the use of standardized terms. In the course of doing so, we realized that there were substantial gaps in existing ontologies with regards to concepts, processes, and, especially, physical entities (e.g., vector species, pathogen species, and vector surveillance and management equipment) in the domain of surveillance and management of vectors and vector-borne pathogens. We therefore produced an ontology for vector surveillance and management, focusing on arthropod vectors and vector-borne pathogens with relevance to humans or domestic animals, and with special emphasis on content to support operational activities through inclusion in databases, data management systems, or decision support systems. The Vector Surveillance and Management Ontology (VSMO) includes >2,200 unique terms, of which the vast majority (>80%) were newly generated during the development of this ontology. One core feature of the VSMO is the linkage, through the has vector relation, of arthropod species to the pathogenic microorganisms for which they serve as biological vectors. We also recognized and addressed a potential roadblock for use of the VSMO by the vector-borne disease community: the difficulty in extracting information from OBO-Edit ontology files (*.obo files) and exporting the information to other file formats. A novel ontology explorer tool was developed to facilitate extraction and export of information from the VSMO*.obo file into lists of terms and their associated unique IDs in *.txt or *.csv file formats. These lists can then be imported into a database or data management system for use as select lists with predefined terms. This is an important step to ensure that the knowledge contained in our ontology can be put into practical use.

Multidirectional Scanning Model, MUSCLE, to Vectorize Raster Images with Straight Lines

PubMed Central

Karas, Ismail Rakip; Bayram, Bulent; Batuk, Fatmagul; Akay, Abdullah Emin; Baz, Ibrahim

2008-01-01

This paper presents a new model, MUSCLE (Multidirectional Scanning for Line Extraction), for automatic vectorization of raster images with straight lines. The algorithm of the model implements the line thinning and the simple neighborhood methods to perform vectorization. The model allows users to define specified criteria which are crucial for acquiring the vectorization process. In this model, various raster images can be vectorized such as township plans, maps, architectural drawings, and machine plans. The algorithm of the model was developed by implementing an appropriate computer programming and tested on a basic application. Results, verified by using two well known vectorization programs (WinTopo and Scan2CAD), indicated that the model can successfully vectorize the specified raster data quickly and accurately. PMID:27879843

Multidirectional Scanning Model, MUSCLE, to Vectorize Raster Images with Straight Lines.

PubMed

Karas, Ismail Rakip; Bayram, Bulent; Batuk, Fatmagul; Akay, Abdullah Emin; Baz, Ibrahim

2008-04-15

This paper presents a new model, MUSCLE (Multidirectional Scanning for Line Extraction), for automatic vectorization of raster images with straight lines. The algorithm of the model implements the line thinning and the simple neighborhood methods to perform vectorization. The model allows users to define specified criteria which are crucial for acquiring the vectorization process. In this model, various raster images can be vectorized such as township plans, maps, architectural drawings, and machine plans. The algorithm of the model was developed by implementing an appropriate computer programming and tested on a basic application. Results, verified by using two well known vectorization programs (WinTopo and Scan2CAD), indicated that the model can successfully vectorize the specified raster data quickly and accurately.

Vector Data Model: A New Model of HDF-EOS to Support GIS Applications in EOS

NASA Astrophysics Data System (ADS)

Chi, E.; Edmonds, R d

2001-05-01

NASA's Earth Science Data Information System (ESDIS) project has an active program of research and development of systems for the storage and management of Earth science data for Earth Observation System (EOS) mission, a key program of NASA Earth Science Enterprise. EOS has adopted an extension of the Hierarchical Data Format (HDF) as the format of choice for standard product distribution. Three new EOS specific datatypes - point, swath and grid - have been defined within the HDF framework. The enhanced data format is named HDF-EOS. Geographic Information Systems (GIS) are used by Earth scientists in EOS data product generation, visualization, and analysis. There are two major data types in GIS applications, raster and vector. The current HDF-EOS handles only raster type in the swath data model. The vector data model is identified and developed as a new HDFEOS format to meet the requirements of scientists working with EOS data products in vector format. The vector model is designed using a topological data structure, which defines the spatial relationships among points, lines, and polygons. The three major topological concepts that the vector model adopts are: a) lines connect to each other at nodes (connectivity), b) lines that connect to surround an area define a polygon (area definition), and c) lines have direction and left and right sides (contiguity). The vector model is implemented in HDF by mapping the conceptual model to HDF internal data models and structures, viz. Vdata, Vgroup, and their associated attribute structures. The point, line, and polygon geometry and attribute data are stored in similar tables. Further, the vector model utilizes the structure and product metadata, which characterize the HDF-EOS. Both types of metadata are stored as attributes in HDF-EOS files, and are encoded in text format by using Object Description Language (ODL) and stored as global attributes in HDF-EOS files. EOS has developed a series of routines for storing, retrieving, and manipulating vector data in category of access, definition, basic I/O, inquiry, and subsetting. The routines are tested and form a package, HDF-EOS/Vector. The alpha version of HDFEOS/Vector has been distributed through the HDF-EOS project web site at http://hdfeos.gsfc.nasa.gov. We are also developing translators between HDF-EOS vector format and variety of GIS formats, such as Shapefile. The HDF-EOS vector model enables EOS scientists to deliver EOS data in a way ready for Earth scientists to analyze using GIS software, and also provides EOS project a mechanism to store GIS data product in meaningful vector format with significant economy in storage.

Development and Validation of Remote Sensing-Based Surface Inundation Products for Vector-Borne Disease Risk in East Africa

NASA Astrophysics Data System (ADS)

Jensen, K.; McDonald, K. C.; Ceccato, P.; Schroeder, R.; Podest, E.

2014-12-01

The potential impact of climate variability and change on the spread of infectious disease is of increasingly critical concern to public health. Newly-available remote sensing datasets may be combined with predictive modeling to develop new capabilities to mitigate risks of vector-borne diseases such as malaria, leishmaniasis, and rift valley fever. We have developed improved remote sensing-based products for monitoring water bodies and inundation dynamics that have potential utility for improving risk forecasts of vector-borne disease epidemics. These products include daily and seasonal surface inundation based on the global mappings of inundated area fraction derived at the 25-km scale from active and passive microwave instruments ERS, QuikSCAT, ASCAT, and SSM/I data - the Satellite Water Microwave Product Series (SWAMPS). Focusing on the East African region, we present validation of this product using multi-temporal classification of inundated areas in this region derived from high resolution PALSAR (100m) and Landsat (30m) observations. We assess historical occurrence of malaria in the east African country of Eritrea with respect to the time series SWAMPS datasets, and we aim to construct a framework for use of these new datasets to improve prediction of future malaria risk in this region. This work is supported through funding from the NASA Applied Sciences Program, the NASA Terrestrial Ecology Program, and the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. This study is also supported and monitored by National Oceanic and Atmospheric Administration (NOAA) under Grant - CREST Grant # NA11SEC4810004. The statements contained within the manuscript/research article are not the opinions of the funding agency or the U.S. government, but reflect the authors' opinions. This work was conducted in part under the framework of the ALOS Kyoto and Carbon Initiative. ALOS PALSAR data were provided by JAXA EORC.

A review of the vector management methods to prevent and control outbreaks of West Nile virus infection and the challenge for Europe

PubMed Central

2014-01-01

West Nile virus infection is a growing concern in Europe. Vector management is often the primary option to prevent and control outbreaks of the disease. Its implementation is, however, complex and needs to be supported by integrated multidisciplinary surveillance systems and to be organized within the framework of predefined response plans. The impact of the vector control measures depends on multiple factors and the identification of the best combination of vector control methods is therefore not always straightforward. Therefore, this contribution aims at critically reviewing the existing vector control methods to prevent and control outbreaks of West Nile virus infection and to present the challenges for Europe. Most West Nile virus vector control experiences have been recently developed in the US, where ecological conditions are different from the EU and vector control is organized under a different regulatory frame. The extrapolation of information produced in North America to Europe might be limited because of the seemingly different epidemiology in the European region. Therefore, there is an urgent need to analyse the European experiences of the prevention and control of outbreaks of West Nile virus infection and to perform robust cost-benefit analysis that can guide the implementation of the appropriate control measures. Furthermore, to be effective, vector control programs require a strong organisational backbone relying on a previously defined plan, skilled technicians and operators, appropriate equipment, and sufficient financial resources. A decision making guide scheme is proposed which may assist in the process of implementation of vector control measures tailored on specific areas and considering the available information and possible scenarios. PMID:25015004

Family leader empowerment program using participatory learning process for dengue vector control.

PubMed

Pengvanich, Veerapong

2011-02-01

Assess the performance of the empowerment program using participatory learning process for the control of Dengue vector The program focuses on using the leaders of families as the main executer of the vector control protocol. This quasi-experimental research utilized the two-group pretest-posttest design. The sample group consisted of 120 family leaders from two communities in Mueang Municipality, Chachoengsao Province. The research was conducted during an 8-week period between April and June 2010. The data were collected and analyzed based on frequency, percentage, mean, paired t-test, and independent t-test. The result was evaluated by comparing the difference between the mean prevalence index of mosquito larvae before and after the process implementation in terms of the container index (CI) and the house index (HI). After spending eight weeks in the empowerment program, the family leader's behavior in the aspect of Dengue vector control has improved. The Container Index and the House Index were found to decrease with p = 0.05 statistical significance. The reduction of CI and HI suggested that the program worked well in the selected communities. The success of the Dengue vector control program depended on cooperation and participation of many groups, especially the families in the community When the family leaders have good attitude and are capable of carrying out the vector control protocol, the risk factor leading to the incidence of Dengue rims infection can be reduced.

A qualitative study of community perception and acceptance of biological larviciding for malaria mosquito control in rural Burkina Faso.

PubMed

Dambach, Peter; Jorge, Margarida Mendes; Traoré, Issouf; Phalkey, Revati; Sawadogo, Hélène; Zabré, Pascal; Kagoné, Moubassira; Sié, Ali; Sauerborn, Rainer; Becker, Norbert; Beiersmann, Claudia

2018-03-23

Vector and malaria parasite's rising resistance against pyrethroid-impregnated bed nets and antimalarial drugs highlight the need for additional control measures. Larviciding against malaria vectors is experiencing a renaissance with the availability of environmentally friendly and target species-specific larvicides. In this study, we analyse the perception and acceptability of spraying surface water collections with the biological larvicide Bacillus thuringiensis israelensis in a single health district in Burkina Faso. A total of 12 focus group discussions and 12 key informant interviews were performed in 10 rural villages provided with coverage of various larvicide treatments (all breeding sites treated, the most productive breeding sites treated, and untreated control). Respondents' knowledge about the major risk factors for malaria transmission was generally good. Most interviewees stated they performed personal protective measures against vector mosquitoes including the use of bed nets and sometimes mosquito coils and traditional repellents. The acceptance of larviciding in and around the villages was high and the majority of respondents reported a relief in mosquito nuisance and malarial episodes. There was high interest in the project and demand for future continuation. This study showed that larviciding interventions received positive resonance from the population. People showed a willingness to be involved and financially support the program. The positive environment with high acceptance for larviciding programs would facilitate routine implementation. An essential factor for the future success of such programs would be inclusion in regional or national malaria control guidelines.

Demonstration of Advanced EMI Models for Live-Site UXO Discrimination at Waikoloa, Hawaii

DTIC Science & Technology

2015-12-01

magnetic source models PNN Probabilistic Neural Network SERDP Strategic Environmental Research and Development Program SLO San Luis Obispo...SNR Signal to noise ratio SVM Support vector machine TD Time Domain TEMTADS Time Domain Electromagnetic Towed Array Detection System TOI... intrusive procedure, which was used by Parsons at WMA, failed to document accurately all intrusive results, or failed to detect and clear all UXO like

GLOBE Observer Mosquito Habitat Mapper: Geoscience and Public Health Connections

NASA Astrophysics Data System (ADS)

Low, R.; Boger, R. A.

2017-12-01

The global health crisis posed by vector-borne diseases is so great in scope that it is clearly insurmountable without the active help of tens-or hundreds- of thousands of individuals, working to identify and eradicate risk in communities around the world. Mobile devices equipped with data collection capabilities and visualization opportunities are lowering the barrier for participation in data collection efforts. The GLOBE Observer Mosquito Habitat Mapper (MHM) provides citizen scientists with an easy to use mobile platform to identify and locate mosquito breeding sites in their community. The app also supports the identification of vector taxa in the larvae development phase via a built-in key, which provides important information for scientists and public health officials tracking the rate of range expansion of invasive vector species and associated health threats. GO Mosquito is actively working with other citizen scientist programs across the world to ensure interoperability of data through standardization of metadata fields specific to vector monitoring, and through the development of APIs that allow for data exchange and shared data display through a UN-sponsored proof of concept project, Global Mosquito Alert. Avenues of application for mosquito vector data-both directly, by public health entities, and by modelers who employ remotely sensed environmental data to project mosquito population dynamics and epidemic disease will be featured.

Symbolic computer vector analysis

NASA Technical Reports Server (NTRS)

Stoutemyer, D. R.

1977-01-01

A MACSYMA program is described which performs symbolic vector algebra and vector calculus. The program can combine and simplify symbolic expressions including dot products and cross products, together with the gradient, divergence, curl, and Laplacian operators. The distribution of these operators over sums or products is under user control, as are various other expansions, including expansion into components in any specific orthogonal coordinate system. There is also a capability for deriving the scalar or vector potential of a vector field. Examples include derivation of the partial differential equations describing fluid flow and magnetohydrodynamics, for 12 different classic orthogonal curvilinear coordinate systems.

Evolutionary and dispersal history of Triatoma infestans, main vector of Chagas disease, by chromosomal markers.

PubMed

Panzera, Francisco; Ferreiro, María J; Pita, Sebastián; Calleros, Lucía; Pérez, Ruben; Basmadjián, Yester; Guevara, Yenny; Brenière, Simone Frédérique; Panzera, Yanina

2014-10-01

Chagas disease, one of the most important vector-borne diseases in the Americas, is caused by Trypanosoma cruzi and transmitted to humans by insects of the subfamily Triatominae. An effective control of this disease depends on elimination of vectors through spraying with insecticides. Genetic research can help insect control programs by identifying and characterizing vector populations. In southern Latin America, Triatoma infestans is the main vector and presents two distinct lineages, known as Andean and non-Andean chromosomal groups, that are highly differentiated by the amount of heterochromatin and genome size. Analyses with nuclear and mitochondrial sequences are not conclusive about resolving the origin and spread of T. infestans. The present paper includes the analyses of karyotypes, heterochromatin distribution and chromosomal mapping of the major ribosomal cluster (45S rDNA) to specimens throughout the distribution range of this species, including pyrethroid-resistant populations. A total of 417 specimens from seven different countries were analyzed. We show an unusual wide rDNA variability related to number and chromosomal position of the ribosomal genes, never before reported in species with holocentric chromosomes. Considering the chromosomal groups previously described, the ribosomal patterns are associated with a particular geographic distribution. Our results reveal that the differentiation process between both T. infestans chromosomal groups has involved significant genomic reorganization of essential coding sequences, besides the changes in heterochromatin and genomic size previously reported. The chromosomal markers also allowed us to detect the existence of a hybrid zone occupied by individuals derived from crosses between both chromosomal groups. Our genetic studies support the hypothesis of an Andean origin for T. infestans, and suggest that pyrethroid-resistant populations from the Argentinean-Bolivian border are most likely the result of recent secondary contact between both lineages. We suggest that vector control programs should make a greater effort in the entomological surveillance of those regions with both chromosomal groups to avoid rapid emergence of resistant individuals. Copyright © 2014 Elsevier B.V. All rights reserved.

The Automation System Censor Speech for the Indonesian Rude Swear Words Based on Support Vector Machine and Pitch Analysis

NASA Astrophysics Data System (ADS)

Endah, S. N.; Nugraheni, D. M. K.; Adhy, S.; Sutikno

2017-04-01

According to Law No. 32 of 2002 and the Indonesian Broadcasting Commission Regulation No. 02/P/KPI/12/2009 & No. 03/P/KPI/12/2009, stated that broadcast programs should not scold with harsh words, not harass, insult or demean minorities and marginalized groups. However, there are no suitable tools to censor those words automatically. Therefore, researches to develop a system of intelligent software to censor the words automatically are needed. To conduct censor, the system must be able to recognize the words in question. This research proposes the classification of speech divide into two classes using Support Vector Machine (SVM), first class is set of rude words and the second class is set of properly words. The speech pitch values as an input in SVM, it used for the development of the system for the Indonesian rude swear word. The results of the experiment show that SVM is good for this system.

Testing of the Support Vector Machine for Binary-Class Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew

2011-01-01

The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SMV as a method for classification. From trial to trial, SVM produces consistent results

Barriers and Opportunities to Advancing Women in Leadership Roles in Vector Control: Perspectives from a Stakeholder Survey.

PubMed

Hayden, Mary H; Barrett, Erika; Bernard, Guyah; Toko, Eunice N; Agawo, Maurice; Okello, Amanda M; Gunn, Jayleen K L; Ernst, Kacey C

2018-05-01

Increasing the active participation of professional women in vector control (VC) activities may help promote greater gender equity in the workplace and reduce the burden of vector-borne diseases. This stakeholder survey examined the current roles and perspective of professionals employed in the VC sector in Kenya, Indonesia, India, and other countries. The largest barriers that women face in pursuing leadership roles in the VC sector include lack of awareness of career opportunities, limitations based on cultural norms, and the belief that VC is men's work. These barriers could be addressed through improving education and recruitment campaigns, as well as supporting higher education and mentoring programs. Females were almost six times more likely to be encouraged to pursue leadership positions in their organization compared with male respondents (odds ratio = 5.9, P > 0.03, 95% confidence interval: 1.19, 29.42). These findings suggest that once women are recruited into the VC workforce, they face minimal discrimination and have increased leadership opportunities.

Multiclass Reduced-Set Support Vector Machines

NASA Technical Reports Server (NTRS)

Tang, Benyang; Mazzoni, Dominic

2006-01-01

There are well-established methods for reducing the number of support vectors in a trained binary support vector machine, often with minimal impact on accuracy. We show how reduced-set methods can be applied to multiclass SVMs made up of several binary SVMs, with significantly better results than reducing each binary SVM independently. Our approach is based on Burges' approach that constructs each reduced-set vector as the pre-image of a vector in kernel space, but we extend this by recomputing the SVM weights and bias optimally using the original SVM objective function. This leads to greater accuracy for a binary reduced-set SVM, and also allows vectors to be 'shared' between multiple binary SVMs for greater multiclass accuracy with fewer reduced-set vectors. We also propose computing pre-images using differential evolution, which we have found to be more robust than gradient descent alone. We show experimental results on a variety of problems and find that this new approach is consistently better than previous multiclass reduced-set methods, sometimes with a dramatic difference.

Vector critical points and generalized quasi-efficient solutions in nonsmooth multi-objective programming.

PubMed

Wang, Zhen; Li, Ru; Yu, Guolin

2017-01-01

In this work, several extended approximately invex vector-valued functions of higher order involving a generalized Jacobian are introduced, and some examples are presented to illustrate their existences. The notions of higher-order (weak) quasi-efficiency with respect to a function are proposed for a multi-objective programming. Under the introduced generalization of higher-order approximate invexities assumptions, we prove that the solutions of generalized vector variational-like inequalities in terms of the generalized Jacobian are the generalized quasi-efficient solutions of nonsmooth multi-objective programming problems. Moreover, the equivalent conditions are presented, namely, a vector critical point is a weakly quasi-efficient solution of higher order with respect to a function.

A Subdivision-Based Representation for Vector Image Editing.

PubMed

Liao, Zicheng; Hoppe, Hugues; Forsyth, David; Yu, Yizhou

2012-11-01

Vector graphics has been employed in a wide variety of applications due to its scalability and editability. Editability is a high priority for artists and designers who wish to produce vector-based graphical content with user interaction. In this paper, we introduce a new vector image representation based on piecewise smooth subdivision surfaces, which is a simple, unified and flexible framework that supports a variety of operations, including shape editing, color editing, image stylization, and vector image processing. These operations effectively create novel vector graphics by reusing and altering existing image vectorization results. Because image vectorization yields an abstraction of the original raster image, controlling the level of detail of this abstraction is highly desirable. To this end, we design a feature-oriented vector image pyramid that offers multiple levels of abstraction simultaneously. Our new vector image representation can be rasterized efficiently using GPU-accelerated subdivision. Experiments indicate that our vector image representation achieves high visual quality and better supports editing operations than existing representations.

Estimation of vector static magnetic field by a nitrogen-vacancy center with a single first-shell 13C nuclear (NV–13C) spin in diamond

NASA Astrophysics Data System (ADS)

Jiang, Feng-Jian; Ye, Jian-Feng; Jiao, Zheng; Huang, Zhi-Yong; Lv, Hai-Jiang

2018-05-01

We suggest an experimental scheme that a single nitrogen-vacancy (NV) center coupled to a nearest neighbor 13C nucleus as a sensor in diamond can be used to detect a static vector magnetic field. By means of optical detection magnetic resonance (ODMR) technique, both the strength and the direction of the vector field could be determined by relevant resonance frequencies of continuous wave (CW) and Ramsey spectrums. In addition, we give a method that determines the unique one of eight possible hyperfine tensors for an (NV–13C) system. Finally, we propose an unambiguous method to exclude the symmetrical solution from eight possible vector fields, which correspond to nearly identical resonance frequencies due to their mirror symmetry about 14N–Vacancy–13C (14N–V–13C) plane. Protect supported by the National Natural Science Foundation of China (Grant Nos. 11305074, 11135002, and 11275083), the Key Program of the Education Department Outstanding Youth Foundation of Anhui Province, China (Grant No. gxyqZD2017080), and the Natural Science Foundation of Anhui Province, China (Grant No. KJHS2015B09).

Insect cell transformation vectors that support high level expression and promoter assessment in insect cell culture

USDA-ARS?s Scientific Manuscript database

A somatic transformation vector, pDP9, was constructed that provides a simplified means of producing permanently transformed cultured insect cells that support high levels of protein expression of foreign genes. The pDP9 plasmid vector incorporates DNA sequences from the Junonia coenia densovirus th...

Population of Aedes sp in Highland of Wonosobo District and Its Competence as A Dengue Vector

NASA Astrophysics Data System (ADS)

Martini, Martini; Widjanarko, Bagoes; Hestiningsih, Retno; Purwantisari, Susiana; Yuliawati, Sri

2017-02-01

The increased cases of dengue fever have occurred in the highland of Wonosobo District, and the epidemic taken place in 2009 had 59.3 cases per 100,000 populations. This study aimed to describe of vector competence of the mosquitoes as a dengue vector in the highland of Wonosobo District, Central Java Province. The serial laboratory work was done to measure of vector competence complementary with vector bionomic study. The samples were 20 villages, which were located at Wonosobo sub district. Every village was observed about 15-20 houses. The observed variables were vector competition, bionomic and transovarial infection level, and titer of virus on the mosquitoes after injection. Immunohistochemistry or IHC methods were used to identify transovarial infection status. The number of Ae. aegypti and Ae. albopictus were almost similar and both were found indoors or outdoors. Based on HI and OI index, the larvae density in the highland was enough high than standard of the program. Transovarial infection was found on Ae. aegypti and Ae. albopictus. Environment parameters such as temperature and relative humidity fulfilled the optimum requirement to support the vectors’ life cycle. Transovarial infection has been proven, thus, it indicates that the local transmission has been occurred in this area. Titer of virus was also increasing after day per day. This indicate that the mosquitoes has the ability being vector. As used to do in other area, it is important to conduct breeding places elimination (PSN) indoors as well as outdoors, through active participation of the community in highland area.

Automated innovative diagnostic, data management and communication tool, for improving malaria vector control in endemic settings.

PubMed

Vontas, John; Mitsakakis, Konstantinos; Zengerle, Roland; Yewhalaw, Delenasaw; Sikaala, Chadwick Haadezu; Etang, Josiane; Fallani, Matteo; Carman, Bill; Müller, Pie; Chouaïbou, Mouhamadou; Coleman, Marlize; Coleman, Michael

2016-01-01

Malaria is a life-threatening disease that caused more than 400,000 deaths in sub-Saharan Africa in 2015. Mass prevention of the disease is best achieved by vector control which heavily relies on the use of insecticides. Monitoring mosquito vector populations is an integral component of control programs and a prerequisite for effective interventions. Several individual methods are used for this task; however, there are obstacles to their uptake, as well as challenges in organizing, interpreting and communicating vector population data. The Horizon 2020 project "DMC-MALVEC" consortium will develop a fully integrated and automated multiplex vector-diagnostic platform (LabDisk) for characterizing mosquito populations in terms of species composition, Plasmodium infections and biochemical insecticide resistance markers. The LabDisk will be interfaced with a Disease Data Management System (DDMS), a custom made data management software which will collate and manage data from routine entomological monitoring activities providing information in a timely fashion based on user needs and in a standardized way. The ResistanceSim, a serious game, a modern ICT platform that uses interactive ways of communicating guidelines and exemplifying good practices of optimal use of interventions in the health sector will also be a key element. The use of the tool will teach operational end users the value of quality data (relevant, timely and accurate) to make informed decisions. The integrated system (LabDisk, DDMS & ResistanceSim) will be evaluated in four malaria endemic countries, representative of the vector control challenges in sub-Saharan Africa, (Cameroon, Ivory Coast, Ethiopia and Zambia), highly representative of malaria settings with different levels of endemicity and vector control challenges, to support informed decision-making in vector control and disease management.

Parallel processors and nonlinear structural dynamics algorithms and software

NASA Technical Reports Server (NTRS)

Belytschko, Ted

1990-01-01

Techniques are discussed for the implementation and improvement of vectorization and concurrency in nonlinear explicit structural finite element codes. In explicit integration methods, the computation of the element internal force vector consumes the bulk of the computer time. The program can be efficiently vectorized by subdividing the elements into blocks and executing all computations in vector mode. The structuring of elements into blocks also provides a convenient way to implement concurrency by creating tasks which can be assigned to available processors for evaluation. The techniques were implemented in a 3-D nonlinear program with one-point quadrature shell elements. Concurrency and vectorization were first implemented in a single time step version of the program. Techniques were developed to minimize processor idle time and to select the optimal vector length. A comparison of run times between the program executed in scalar, serial mode and the fully vectorized code executed concurrently using eight processors shows speed-ups of over 25. Conjugate gradient methods for solving nonlinear algebraic equations are also readily adapted to a parallel environment. A new technique for improving convergence properties of conjugate gradients in nonlinear problems is developed in conjunction with other techniques such as diagonal scaling. A significant reduction in the number of iterations required for convergence is shown for a statically loaded rigid bar suspended by three equally spaced springs.

A vectorization of the Hess McDonnell Douglas potential flow program NUED for the STAR-100 computer

NASA Technical Reports Server (NTRS)

Boney, L. R.; Smith, R. E., Jr.

1979-01-01

The computer program NUED for analyzing potential flow about arbitrary three dimensional lifting bodies using the panel method was modified to use vector operations and run on the STAR-100 computer. A high speed of computation and ability to approximate the body surface with a large number of panels are characteristics of NUEDV. The new program shows that vector operations can be readily implemented in programs of this type to increase the computational speed on the STAR-100 computer. The virtual memory architecture of the STAR-100 facilitates the use of large numbers of panels to approximate the body surface.

[Support vector machine?assisted diagnosis of human malignant gastric tissues based on dielectric properties].

PubMed

Zhang, Sa; Li, Zhou; Xin, Xue-Gang

2017-12-20

To achieve differential diagnosis of normal and malignant gastric tissues based on discrepancies in their dielectric properties using support vector machine. The dielectric properties of normal and malignant gastric tissues at the frequency ranging from 42.58 to 500 MHz were measured by coaxial probe method, and the Cole?Cole model was used to fit the measured data. Receiver?operating characteristic (ROC) curve analysis was used to evaluate the discrimination capability with respect to permittivity, conductivity, and Cole?Cole fitting parameters. Support vector machine was used for discriminating normal and malignant gastric tissues, and the discrimination accuracy was calculated using k?fold cross? The area under the ROC curve was above 0.8 for permittivity at the 5 frequencies at the lower end of the measured frequency range. The combination of the support vector machine with the permittivity at all these 5 frequencies combined achieved the highest discrimination accuracy of 84.38% with a MATLAB runtime of 3.40 s. The support vector machine?assisted diagnosis is feasible for human malignant gastric tissues based on the dielectric properties.

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

NASA Astrophysics Data System (ADS)

Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan

2018-03-01

In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

The Design of a Templated C++ Small Vector Class for Numerical Computing

NASA Technical Reports Server (NTRS)

Moran, Patrick J.

2000-01-01

We describe the design and implementation of a templated C++ class for vectors. The vector class is templated both for vector length and vector component type; the vector length is fixed at template instantiation time. The vector implementation is such that for a vector of N components of type T, the total number of bytes required by the vector is equal to N * size of (T), where size of is the built-in C operator. The property of having a size no bigger than that required by the components themselves is key in many numerical computing applications, where one may allocate very large arrays of small, fixed-length vectors. In addition to the design trade-offs motivating our fixed-length vector design choice, we review some of the C++ template features essential to an efficient, succinct implementation. In particular, we highlight some of the standard C++ features, such as partial template specialization, that are not supported by all compilers currently. This report provides an inventory listing the relevant support currently provided by some key compilers, as well as test code one can use to verify compiler capabilities.

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

Sharma, Vishal C.; Gopalakrishnan, Ganesh; Krishnamoorthy, Sriram

The systems resilience research community has developed methods to manually insert additional source-program level assertions to trap errors, and also devised tools to conduct fault injection studies for scalar program codes. In this work, we contribute the first vector oriented LLVM-level fault injector VULFI to help study the effects of faults in vector architectures that are of growing importance, especially for vectorizing loops. Using VULFI, we conduct a resiliency study of nine real-world vector benchmarks using Intel’s AVX and SSE extensions as the target vector instruction sets, and offer the first reported understanding of how faults affect vector instruction sets.more » We take this work further toward automating the insertion of resilience assertions during compilation. This is based on our observation that during intermediate (e.g., LLVM-level) code generation to handle full and partial vectorization, modern compilers exploit (and explicate in their code-documentation) critical invariants. These invariants are turned into error-checking code. We confirm the efficacy of these automatically inserted low-overhead error detectors for vectorized for-loops.« less

An Efficient Wait-Free Vector

DOE PAGES

Feldman, Steven; Valera-Leon, Carlos; Dechev, Damian

2016-03-01

The vector is a fundamental data structure, which provides constant-time access to a dynamically-resizable range of elements. Currently, there exist no wait-free vectors. The only non-blocking version supports only a subset of the sequential vector API and exhibits significant synchronization overhead caused by supporting opposing operations. Since many applications operate in phases of execution, wherein each phase only a subset of operations are used, this overhead is unnecessary for the majority of the application. To address the limitations of the non-blocking version, we present a new design that is wait-free, supports more of the operations provided by the sequential vector,more » and provides alternative implementations of key operations. These alternatives allow the developer to balance the performance and functionality of the vector as requirements change throughout execution. Compared to the known non-blocking version and the concurrent vector found in Intel’s TBB library, our design outperforms or provides comparable performance in the majority of tested scenarios. Over all tested scenarios, the presented design performs an average of 4.97 times more operations per second than the non-blocking vector and 1.54 more than the TBB vector. In a scenario designed to simulate the filling of a vector, performance improvement increases to 13.38 and 1.16 times. This work presents the first ABA-free non-blocking vector. Finally, unlike the other non-blocking approach, all operations are wait-free and bounds-checked and elements are stored contiguously in memory.« less

High-performance computing — an overview

NASA Astrophysics Data System (ADS)

Marksteiner, Peter

1996-08-01

An overview of high-performance computing (HPC) is given. Different types of computer architectures used in HPC are discussed: vector supercomputers, high-performance RISC processors, various parallel computers like symmetric multiprocessors, workstation clusters, massively parallel processors. Software tools and programming techniques used in HPC are reviewed: vectorizing compilers, optimization and vector tuning, optimization for RISC processors; parallel programming techniques like shared-memory parallelism, message passing and data parallelism; and numerical libraries.

User's guide to Model Viewer, a program for three-dimensional visualization of ground-water model results

USGS Publications Warehouse

Hsieh, Paul A.; Winston, Richard B.

2002-01-01

Model Viewer is a computer program that displays the results of three-dimensional groundwater models. Scalar data (such as hydraulic head or solute concentration) may be displayed as a solid or a set of isosurfaces, using a red-to-blue color spectrum to represent a range of scalar values. Vector data (such as velocity or specific discharge) are represented by lines oriented to the vector direction and scaled to the vector magnitude. Model Viewer can also display pathlines, cells or nodes that represent model features such as streams and wells, and auxiliary graphic objects such as grid lines and coordinate axes. Users may crop the model grid in different orientations to examine the interior structure of the data. For transient simulations, Model Viewer can animate the time evolution of the simulated quantities. The current version (1.0) of Model Viewer runs on Microsoft Windows 95, 98, NT and 2000 operating systems, and supports the following models: MODFLOW-2000, MODFLOW-2000 with the Ground-Water Transport Process, MODFLOW-96, MOC3D (Version 3.5), MODPATH, MT3DMS, and SUTRA (Version 2D3D.1). Model Viewer is designed to directly read input and output files from these models, thus minimizing the need for additional postprocessing. This report provides an overview of Model Viewer. Complete instructions on how to use the software are provided in the on-line help pages.

Cost-effective malaria control in Brazil. Cost-effectiveness of a Malaria Control Program in the Amazon Basin of Brazil, 1988-1996.

PubMed

Akhavan, D; Musgrove, P; Abrantes, A; d'A Gusmão, R

1999-11-01

Malaria transmission was controlled elsewhere in Brazil by 1980, but in the Amazon Basin cases increased steadily until 1989, to almost half a million a year and the coefficient of mortality quadrupled in 1977-1988. The government's malaria control program almost collapsed financially in 1987-1989 and underwent a turbulent reorganization in 1991-1993. A World Bank project supported the program from late 1989 to mid-1996, and in 1992-1993, with help from the Pan American Health Organization, facilitated a change toward earlier and more aggressive case treatment and more concentrated vector control. The epidemic stopped expanding in 1990-1991 and reversed in 1992-1996. The total cost of the program from 1989 through mid-1996 was US$616 million: US$526 million for prevention and US$90 million for treatment. Compared to what would have happened in the absence of the program, nearly two million cases of malaria and 231,000 deaths were prevented; the lives saved were due almost equally to preventing infection and to case treatment. Converting the savings in lives and in morbidity into Disability-Adjusted Life Years yields almost nine million DALYs, 5.1 million from treatment and 3.9 million from prevention. Nearly all the gain came from controlling deaths and therefore from controlling falciparum. The overall cost-effectiveness was US$2672 per life saved or US$69 per DALY, which is low compared to most previous estimates and compares favorably to many other disease control interventions. Contrary to much previous experience, case treatment appears more cost-effective than vector control, particularly where falciparum is prevalent and unfocussed insecticide spraying is relatively ineffective. Halting the epidemic by better targeted vector control and emphasizing treatment paid off in much reduced mortality from malaria and in significantly lower costs per life saved.

The Role of Veterinary Medical Civic Action in the Low Intensity Conflict Environment.

DTIC Science & Technology

1988-06-03

increased or improved production of animal food products, insect/ rodent vector control, public health and sanitation, and food sanitation...peaks over 20,000 feet. South America is bounded by these mountains on the west and the 22 Amazon Jungle on the east. Infrastructure development such as...food was too scarce to support the people, let alone the animals. Likewise, a protein supplement program in Peru involving the raising of rabbits was

Research in computer science

NASA Technical Reports Server (NTRS)

Ortega, J. M.

1985-01-01

Synopses are given for NASA supported work in computer science at the University of Virginia. Some areas of research include: error seeding as a testing method; knowledge representation for engineering design; analysis of faults in a multi-version software experiment; implementation of a parallel programming environment; two computer graphics systems for visualization of pressure distribution and convective density particles; task decomposition for multiple robot arms; vectorized incomplete conjugate gradient; and iterative methods for solving linear equations on the Flex/32.

Multi-class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods.

PubMed

Lodhi, Huma; Muggleton, Stephen; Sternberg, Mike J E

2010-09-17

Toxicity prediction is essential for drug design and development of effective therapeutics. In this paper we present an in silico strategy, to identify the mode of action of toxic compounds, that is based on the use of a novel logic based kernel method. The technique uses support vector machines in conjunction with the kernels constructed from first order rules induced by an Inductive Logic Programming system. It constructs multi-class models by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. In order to evaluate the effectiveness of the approach for chemoinformatics problems like predictive toxicology, we apply it to toxicity classification in aquatic systems. The method is used to identify and classify 442 compounds with respect to the mode of action. The experimental results show that the technique successfully classifies toxic compounds and can be useful in assessing environmental risks. Experimental comparison of the performance of the proposed multi-class scheme with the standard multi-class Inductive Logic Programming algorithm and multi-class Support Vector Machine yields statistically significant results and demonstrates the potential power and benefits of the approach in identifying compounds of various toxic mechanisms. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Management of arthropod pathogen vectors in North America: Minimizing adverse effects on pollinators

USGS Publications Warehouse

Ginsberg, Howard; Bargar, Timothy A.; Hladik, Michelle L.; Lubelczyk, Charles

2017-01-01

Tick and mosquito management is important to public health protection. At the same time, growing concerns about declines of pollinator species raise the question of whether vector control practices might affect pollinator populations. We report the results of a task force of the North American Pollinator Protection Campaign (NAPPC) that examined potential effects of vector management practices on pollinators, and how these programs could be adjusted to minimize negative effects on pollinating species. The main types of vector control practices that might affect pollinators are landscape manipulation, biocontrol, and pesticide applications. Some current practices already minimize effects of vector control on pollinators (e.g., short-lived pesticides and application-targeting technologies). Nontarget effects can be further diminished by taking pollinator protection into account in the planning stages of vector management programs. Effects of vector control on pollinator species often depend on specific local conditions (e.g., proximity of locations with abundant vectors to concentrations of floral resources), so planning is most effective when it includes collaborations of local vector management professionals with local experts on pollinators. Interventions can then be designed to avoid pollinators (e.g., targeting applications to avoid blooming times and pollinator nesting habitats), while still optimizing public health protection. Research on efficient targeting of interventions, and on effects on pollinators of emerging technologies, will help mitigate potential deleterious effects on pollinators in future management programs. In particular, models that can predict effects of integrated pest management on vector-borne pathogen transmission, along with effects on pollinator populations, would be useful for collaborative decision-making.

CYBER-205 Devectorizer

NASA Technical Reports Server (NTRS)

Lakeotes, Christopher D.

1990-01-01

DEVECT (CYBER-205 Devectorizer) is CYBER-205 FORTRAN source-language-preprocessor computer program reducing vector statements to standard FORTRAN. In addition, DEVECT has many other standard and optional features simplifying conversion of vector-processor programs for CYBER 200 to other computers. Written in FORTRAN IV.

Evaluation of the discrete vortex wake cross flow model using vector computers. Part 1: Theory and application

NASA Technical Reports Server (NTRS)

1979-01-01

The current program had the objective to modify a discrete vortex wake method to efficiently compute the aerodynamic forces and moments on high fineness ratio bodies (f approximately 10.0). The approach is to increase computational efficiency by structuring the program to take advantage of new computer vector software and by developing new algorithms when vector software can not efficiently be used. An efficient program was written and substantial savings achieved. Several test cases were run for fineness ratios up to f = 16.0 and angles of attack up to 50 degrees.

Soft-sensing model of temperature for aluminum reduction cell on improved twin support vector regression

NASA Astrophysics Data System (ADS)

Li, Tao

2018-06-01

The complexity of aluminum electrolysis process leads the temperature for aluminum reduction cells hard to measure directly. However, temperature is the control center of aluminum production. To solve this problem, combining some aluminum plant's practice data, this paper presents a Soft-sensing model of temperature for aluminum electrolysis process on Improved Twin Support Vector Regression (ITSVR). ITSVR eliminates the slow learning speed of Support Vector Regression (SVR) and the over-fit risk of Twin Support Vector Regression (TSVR) by introducing a regularization term into the objective function of TSVR, which ensures the structural risk minimization principle and lower computational complexity. Finally, the model with some other parameters as auxiliary variable, predicts the temperature by ITSVR. The simulation result shows Soft-sensing model based on ITSVR has short time-consuming and better generalization.

SPOT Program

NASA Technical Reports Server (NTRS)

Smith, Jason T.; Welsh, Sam J.; Farinetti, Antonio L.; Wegner, Tim; Blakeslee, James; Deboeck, Toni F.; Dyer, Daniel; Corley, Bryan M.; Ollivierre, Jarmaine; Kramer, Leonard;

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

Feldman, Steven; Valera-Leon, Carlos; Dechev, Damian

The vector is a fundamental data structure, which provides constant-time access to a dynamically-resizable range of elements. Currently, there exist no wait-free vectors. The only non-blocking version supports only a subset of the sequential vector API and exhibits significant synchronization overhead caused by supporting opposing operations. Since many applications operate in phases of execution, wherein each phase only a subset of operations are used, this overhead is unnecessary for the majority of the application. To address the limitations of the non-blocking version, we present a new design that is wait-free, supports more of the operations provided by the sequential vector,more » and provides alternative implementations of key operations. These alternatives allow the developer to balance the performance and functionality of the vector as requirements change throughout execution. Compared to the known non-blocking version and the concurrent vector found in Intel’s TBB library, our design outperforms or provides comparable performance in the majority of tested scenarios. Over all tested scenarios, the presented design performs an average of 4.97 times more operations per second than the non-blocking vector and 1.54 more than the TBB vector. In a scenario designed to simulate the filling of a vector, performance improvement increases to 13.38 and 1.16 times. This work presents the first ABA-free non-blocking vector. Finally, unlike the other non-blocking approach, all operations are wait-free and bounds-checked and elements are stored contiguously in memory.« less

A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

PubMed

Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei

2015-02-01

We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

Predicting subcellular location of apoptosis proteins based on wavelet transform and support vector machine.

PubMed

Qiu, Jian-Ding; Luo, San-Hua; Huang, Jian-Hua; Sun, Xing-Yu; Liang, Ru-Ping

2010-04-01

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. As a result of genome and other sequencing projects, the gap between the number of known apoptosis protein sequences and the number of known apoptosis protein structures is widening rapidly. Because of this extremely unbalanced state, it would be worthwhile to develop a fast and reliable method to identify their subcellular locations so as to gain better insight into their biological functions. In view of this, a new method, in which the support vector machine combines with discrete wavelet transform, has been developed to predict the subcellular location of apoptosis proteins. The results obtained by the jackknife test were quite promising, and indicated that the proposed method can remarkably improve the prediction accuracy of subcellular locations, and might also become a useful high-throughput tool in characterizing other attributes of proteins, such as enzyme class, membrane protein type, and nuclear receptor subfamily according to their sequences.

Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

PubMed

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.

Sustainability of vector control strategies in the Gran Chaco Region: current challenges and possible approaches

PubMed Central

Gürtler, Ricardo E

2011-01-01

Sustainability has become a focal point of the international agenda. At the heart of its range of distribution in the Gran Chaco Region, the elimination of Triatoma infestans has failed, even in areas subject to intensive professional vector control efforts. Chagas disease control programs traditionally have been composed of two divorced entities: a vector control program in charge of routine field operations (bug detection and insecticide spraying) and a disease control program in charge of screening blood donors, diagnosis, etiologic treatment and providing medical care to chronic patients. The challenge of sustainable suppression of bug infestation and Trypanosoma cruzi transmission can be met through integrated disease management, in which vector control is combined with active case detection and treatment to increase impact, cost-effectiveness and public acceptance in resource-limited settings. Multi-stakeholder involvement may add sustainability and resilience to the surveillance system. Chagas vector control and disease management must remain a regional effort within the frame of sustainable development rather than being viewed exclusively as a matter of health pertinent to the health sector. Sustained and continuous coordination between governments, agencies, control programs, academia and the affected communities is critical. PMID:19753458

Community detection in complex networks using proximate support vector clustering

NASA Astrophysics Data System (ADS)

Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

2018-03-01

Community structure, one of the most attention attracting properties in complex networks, has been a cornerstone in advances of various scientific branches. A number of tools have been involved in recent studies concentrating on the community detection algorithms. In this paper, we propose a support vector clustering method based on a proximity graph, owing to which the introduced algorithm surpasses the traditional support vector approach both in accuracy and complexity. Results of extensive experiments undertaken on computer generated networks and real world data sets illustrate competent performances in comparison with the other counterparts.

A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

NASA Astrophysics Data System (ADS)

Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

2017-08-01

In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

Generic accelerated sequence alignment in SeqAn using vectorization and multi-threading.

PubMed

Rahn, René; Budach, Stefan; Costanza, Pascal; Ehrhardt, Marcel; Hancox, Jonny; Reinert, Knut

2018-05-03

Pairwise sequence alignment is undoubtedly a central tool in many bioinformatics analyses. In this paper, we present a generically accelerated module for pairwise sequence alignments applicable for a broad range of applications. In our module, we unified the standard dynamic programming kernel used for pairwise sequence alignments and extended it with a generalized inter-sequence vectorization layout, such that many alignments can be computed simultaneously by exploiting SIMD (Single Instruction Multiple Data) instructions of modern processors. We then extended the module by adding two layers of thread-level parallelization, where we a) distribute many independent alignments on multiple threads and b) inherently parallelize a single alignment computation using a work stealing approach producing a dynamic wavefront progressing along the minor diagonal. We evaluated our alignment vectorization and parallelization on different processors, including the newest Intel® Xeon® (Skylake) and Intel® Xeon Phi™ (KNL) processors, and use cases. The instruction set AVX512-BW (Byte and Word), available on Skylake processors, can genuinely improve the performance of vectorized alignments. We could run single alignments 1600 times faster on the Xeon Phi™ and 1400 times faster on the Xeon® than executing them with our previous sequential alignment module. The module is programmed in C++ using the SeqAn (Reinert et al., 2017) library and distributed with version 2.4. under the BSD license. We support SSE4, AVX2, AVX512 instructions and included UME::SIMD, a SIMD-instruction wrapper library, to extend our module for further instruction sets. We thoroughly test all alignment components with all major C++ compilers on various platforms. rene.rahn@fu-berlin.de.

Mission Accomplished? We Need a Guide to the 'Post Release' World of Wolbachia for Aedes-borne Disease Control.

PubMed

Ritchie, Scott A; van den Hurk, Andrew F; Smout, Michael J; Staunton, Kyran M; Hoffmann, Ary A

2018-03-01

Historically, sustained control of Aedes aegypti, the vector of dengue, chikungunya, yellow fever, and Zika viruses, has been largely ineffective. Subsequently, two novel 'rear and release' control strategies utilizing mosquitoes infected with Wolbachia are currently being developed and deployed widely. In the incompatible insect technique, male Aedes mosquitoes, infected with Wolbachia, suppress populations through unproductive mating. In the transinfection strategy, both male and female Wolbachia-infected Ae. aegypti mosquitoes rapidly infect the wild population with Wolbachia, blocking virus transmission. It is critical to monitor the long-term stability of Wolbachia in host populations, and also the ability of this bacterium to continually inhibit virus transmission. Ongoing release and monitoring programs must be future-proofed should political support weaken when these vectors are successfully controlled. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.

PubMed

Bisenius, Sandrine; Mueller, Karsten; Diehl-Schmid, Janine; Fassbender, Klaus; Grimmer, Timo; Jessen, Frank; Kassubek, Jan; Kornhuber, Johannes; Landwehrmeyer, Bernhard; Ludolph, Albert; Schneider, Anja; Anderl-Straub, Sarah; Stuke, Katharina; Danek, Adrian; Otto, Markus; Schroeter, Matthias L

2017-01-01

Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings.

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

SDMS: A scientific data management system

NASA Technical Reports Server (NTRS)

Massena, W. A.

1978-01-01

SDMS is a data base management system developed specifically to support scientific programming applications. It consists of a data definition program to define the forms of data bases, and FORTRAN-compatible subroutine calls to create and access data within them. Each SDMS data base contains one or more data sets. A data set has the form of a relation. Each column of a data set is defined to be either a key or data element. Key elements must be scalar. Data elements may also be vectors or matrices. The data elements in each row of the relation form an element set. SDMS permits direct storage and retrieval of an element set by specifying the corresponding key element values. To support the scientific environment, SDMS allows the dynamic creation of data bases via subroutine calls. It also allows intermediate or scratch data to be stored in temporary data bases which vanish at job end.

Support vector machines

NASA Technical Reports Server (NTRS)

Garay, Michael J.; Mazzoni, Dominic; Davies, Roger; Wagstaff, Kiri

2004-01-01

Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

Lysine acetylation sites prediction using an ensemble of support vector machine classifiers.

PubMed

Xu, Yan; Wang, Xiao-Bo; Ding, Jun; Wu, Ling-Yun; Deng, Nai-Yang

2010-05-07

Lysine acetylation is an essentially reversible and high regulated post-translational modification which regulates diverse protein properties. Experimental identification of acetylation sites is laborious and expensive. Hence, there is significant interest in the development of computational methods for reliable prediction of acetylation sites from amino acid sequences. In this paper we use an ensemble of support vector machine classifiers to perform this work. The experimentally determined acetylation lysine sites are extracted from Swiss-Prot database and scientific literatures. Experiment results show that an ensemble of support vector machine classifiers outperforms single support vector machine classifier and other computational methods such as PAIL and LysAcet on the problem of predicting acetylation lysine sites. The resulting method has been implemented in EnsemblePail, a web server for lysine acetylation sites prediction available at http://www.aporc.org/EnsemblePail/. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Product Quality Modelling Based on Incremental Support Vector Machine

NASA Astrophysics Data System (ADS)

Wang, J.; Zhang, W.; Qin, B.; Shi, W.

2012-05-01

Incremental Support vector machine (ISVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. It is suitable for the problem of sequentially arriving field data and has been widely used for product quality prediction and production process optimization. However, the traditional ISVM learning does not consider the quality of the incremental data which may contain noise and redundant data; it will affect the learning speed and accuracy to a great extent. In order to improve SVM training speed and accuracy, a modified incremental support vector machine (MISVM) is proposed in this paper. Firstly, the margin vectors are extracted according to the Karush-Kuhn-Tucker (KKT) condition; then the distance from the margin vectors to the final decision hyperplane is calculated to evaluate the importance of margin vectors, where the margin vectors are removed while their distance exceed the specified value; finally, the original SVs and remaining margin vectors are used to update the SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also can preserve the important samples. The MISVM has been experimented on two public data and one field data of zinc coating weight in strip hot-dip galvanizing, and the results shows that the proposed method can improve the prediction accuracy and the training speed effectively. Furthermore, it can provide the necessary decision supports and analysis tools for auto control of product quality, and also can extend to other process industries, such as chemical process and manufacturing process.

The radiative decay {{\\rm{D}}}^{{\\rm{0}}}\\to {\\bar{{\\rm{K}}}}^{{\\rm{* 0}}}\\gamma with vector meson dominance

NASA Astrophysics Data System (ADS)

Dias, J. M.; Debastiani, V. R.; Xie, Ju-Jun; Oset, E.

2018-04-01

Motivated by the experimental measurements of D0 radiative decay modes, we have proposed a model to study the {{{D}}}{{0}}\\to {\\bar{{{K}}}}{{* 0}}γ decay, by establishing a link with {{{D}}}{{0}}\\to {\\bar{{{K}}}}{{* 0}}{{V}} (V=ρ0, ω) decays through the vector meson dominance hypothesis. In order to do this properly, we have used the Lagrangians from the local hidden gauge symmetry approach to account for Vγ conversion. As a result, we have found the branching ratio {\\mathcal B} [{{{D}}}{{0}}\\to { \\bar{{{K}}}}{{* 0}}γ ]{{=}}({{1}}.{{55-3}}.{{44}})× {{{10}}}{{-4}}, which is in fair agreement with the experimental values reported by the Belle and BaBar collaborations. J. M. Dias would like to thank the Brazilian funding agency FAPESP for the financial support (2016/22561-2), V. R. Debastiani wishes to acknowledge the support from the Programa Santiago Grisolia of Generalitat Valenciana (Exp. GRISOLIA/2015/005). This work is also partly supported by the Spanish Ministerio de Economia y Competitividad and European FEDER funds (FIS2014-57026- REDT, FIS2014-51948-C2-1-P, FIS2014-51948-C2-2-P), and the Generalitat Valenciana in the program Prometeo II-2014/068. This work is partly supported by the National Natural Science Foundation of China (11475227, 11735003) and the Youth Innovation Promotion Association CAS (2016367)

Vectorized program architectures for supercomputer-aided circuit design

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

Rizzoli, V.; Ferlito, M.; Neri, A.

1986-01-01

Vector processors (supercomputers) can be effectively employed in MIC or MMIC applications to solve problems of large numerical size such as broad-band nonlinear design or statistical design (yield optimization). In order to fully exploit the capabilities of a vector hardware, any program architecture must be structured accordingly. This paper presents a possible approach to the ''semantic'' vectorization of microwave circuit design software. Speed-up factors of the order of 50 can be obtained on a typical vector processor (Cray X-MP), with respect to the most powerful scaler computers (CDC 7600), with cost reductions of more than one order of magnitude. Thismore » could broaden the horizon of microwave CAD techniques to include problems that are practically out of the reach of conventional systems.« less

Suggested Courseware for the Non-Calculus Physics Student: Measurement, Vectors, and One-Dimensional Motion.

ERIC Educational Resources Information Center

Mahoney, Joyce; And Others

1988-01-01

Evaluates 16 commercially available courseware packages covering topics for introductory physics. Discusses the price, sub-topics, program type, interaction, time, calculus required, graphics, and comments of each program. Recommends two packages in measurement and vectors, and one-dimensional motion respectively. (YP)

Solar electric propulsion thrust subsystem development

NASA Technical Reports Server (NTRS)

Masek, T. D.

1973-01-01

The Solar Electric Propulsion System developed under this program was designed to demonstrate all the thrust subsystem functions needed on an unmanned planetary vehicle. The demonstration included operation of the basic elements, power matching input and output voltage regulation, three-axis thrust vector control, subsystem automatic control including failure detection and correction capability (using a PDP-11 computer), operation of critical elements in thermal-vacuum-, zero-gravity-type propellant storage, and data outputs from all subsystem elements. The subsystem elements, functions, unique features, and test setup are described. General features and capabilities of the test-support data system are also presented. The test program culminated in a 1500-h computer-controlled, system-functional demonstration. This included simultaneous operation of two thruster/power conditioner sets. The results of this testing phase satisfied all the program goals.

Operations of the Far Ultraviolet Spectroscopic Explorer : A `Dynamic' Flux Calibration.

NASA Astrophysics Data System (ADS)

Ehrenreich, D.; Dupuis, J.; Dixon, W. V.; Sahnow, D. J.; Kruk, J. W.

2003-05-01

The FUSE flux calibration is based on model-atmosphere predictions of the spectra of well studied white-dwarf stars. Calibration operations, however, are a highly `dynamic' process consisting of repeatedly measuring these standard stars, deriving corrections, and integrating the results into CALFUSE, the FUSE science pipeline. With suitable scheduling, those calibration observation campaigns let us characterize short term and long term variations of the sensitivity. One particular issue addressed by these observations is monitoring possible degradation of the FUSE optical coatings by atomic oxygen present in the upper atmosphere. We have attempted to minimize this by avoiding pointing close to the instantaneous velocity vector of the spacecraft (the ram vector). Prior to Cycle 3, the minimum permitted angle between the line of sight and the ram vector was 20 degrees. This was reduced to 15 degrees during Cycle 3 to increase our sky coverage, and will be further reduced to 10 degrees for Cycle 4. This relaxation of ram constraints has been preceded by a tailored calibration program in which white dwarf measurements are obtained before and after observations performed for a limited time below the current ram vector constraint. This relaxation of the ram vector constraint will considerably expand the ability of FUSE to observe sources at low declination. This work is based on data obtained for the Guaranteed Time Team by the NASA-CNES-CSA FUSE mission operated by the Johns Hopkins University. Financial support to U.S. participants has been provided by NASA contract NAS5-32985.

Computer Simulation of Diffraction Patterns.

ERIC Educational Resources Information Center

Dodd, N. A.

1983-01-01

Describes an Apple computer program (listing available from author) which simulates Fraunhofer and Fresnel diffraction using vector addition techniques (vector chaining) and allows user to experiment with different shaped multiple apertures. Graphics output include vector resultants, phase difference, diffraction patterns, and the Cornu spiral…

Computer-Generated Diagrams for the Classroom.

ERIC Educational Resources Information Center

Carle, Mark A.; Greenslade, Thomas B., Jr.

1986-01-01

Describes 10 computer programs used to draw diagrams usually drawn on chalkboards, such as addition of three vectors, vector components, range of a projectile, lissajous figures, beats, isotherms, Snell's law, waves passing through a lens, magnetic field due to Helmholtz coils, and three curves. Several programming tips are included. (JN)

Effectiveness of Large-Scale Chagas Disease Vector Control Program in Nicaragua by Residual Insecticide Spraying Against Triatoma dimidiata.

PubMed

Yoshioka, Kota; Nakamura, Jiro; Pérez, Byron; Tercero, Doribel; Pérez, Lenin; Tabaru, Yuichiro

2015-12-01

Chagas disease is one of the most serious health problems in Latin America. Because the disease is transmitted mainly by triatomine vectors, a three-phase vector control strategy was used to reduce its vector-borne transmission. In Nicaragua, we implemented an indoor insecticide spraying program in five northern departments to reduce house infestation by Triatoma dimidiata. The spraying program was performed in two rounds. After each round, we conducted entomological evaluation to compare the vector infestation level before and after spraying. A total of 66,200 and 44,683 houses were sprayed in the first and second spraying rounds, respectively. The entomological evaluation showed that the proportion of houses infested by T. dimidiata was reduced from 17.0% to 3.0% after the first spraying, which was statistically significant (P < 0.0001). However, the second spraying round did not demonstrate clear effectiveness. Space-time analysis revealed that reinfestation of T. dimidiata is more likely to occur in clusters where the pre-spray infestation level is high. Here we discuss how large-scale insecticide spraying is neither effective nor affordable when T. dimidiata is widely distributed at low infestation levels. Further challenges involve research on T. dimidiata reinfestation, diversification of vector control strategies, and implementation of sustainable vector surveillance. © The American Society of Tropical Medicine and Hygiene.

[The application of gene expression programming in the diagnosis of heart disease].

PubMed

Dai, Wenbin; Zhang, Yuntao; Gao, Xingyu

2009-02-01

GEP (Gene expression programming) is a new genetic algorithm, and it has been proved to be excellent in function finding. In this paper, for the purpose of setting up a diagnostic model, GEP is used to deal with the data of heart disease. Eight variables, Sex, Chest pain, Blood pressure, Angina, Peak, Slope, Colored vessels and Thal, are picked out of thirteen variables to form a classified function. This function is used to predict a forecasting set of 100 samples, and the accuracy is 87%. Other algorithms such as SVM (Support vector machine) are applied to the same data and the forecasting results show that GEP is better than other algorithms.

Bayesian Kernel Methods for Non-Gaussian Distributions: Binary and Multi-class Classification Problems

DTIC Science & Technology

2013-05-28

those of the support vector machine and relevance vector machine, and the model runs more quickly than the other algorithms . When one class occurs...incremental support vector machine algorithm for online learning when fewer than 50 data points are available. (a) Papers published in peer-reviewed journals...learning environments, where data processing occurs one observation at a time and the classification algorithm improves over time with new

Development of computer program NAS3D using Vector processing for geometric nonlinear analysis of structures

NASA Technical Reports Server (NTRS)

Mangalgiri, P. D.; Prabhakaran, R.

1986-01-01

An algorithm for vectorized computation of stiffness matrices of an 8 noded isoparametric hexahedron element for geometric nonlinear analysis was developed. This was used in conjunction with the earlier 2-D program GAMNAS to develop the new program NAS3D for geometric nonlinear analysis. A conventional, modified Newton-Raphson process is used for the nonlinear analysis. New schemes for the computation of stiffness and strain energy release rates is presented. The organization the program is explained and some results on four sample problems are given. The study of CPU times showed that savings by a factor of 11 to 13 were achieved when vectorized computation was used for the stiffness instead of the conventional scalar one. Finally, the scheme of inputting data is explained.

Discovering rules for protein-ligand specificity using support vector inductive logic programming.

PubMed

Kelley, Lawrence A; Shrimpton, Paul J; Muggleton, Stephen H; Sternberg, Michael J E

2009-09-01

Structural genomics initiatives are rapidly generating vast numbers of protein structures. Comparative modelling is also capable of producing accurate structural models for many protein sequences. However, for many of the known structures, functions are not yet determined, and in many modelling tasks, an accurate structural model does not necessarily tell us about function. Thus, there is a pressing need for high-throughput methods for determining function from structure. The spatial arrangement of key amino acids in a folded protein, on the surface or buried in clefts, is often the determinants of its biological function. A central aim of molecular biology is to understand the relationship between such substructures or surfaces and biological function, leading both to function prediction and to function design. We present a new general method for discovering the features of binding pockets that confer specificity for particular ligands. Using a recently developed machine-learning technique which couples the rule-discovery approach of inductive logic programming with the statistical learning power of support vector machines, we are able to discriminate, with high precision (90%) and recall (86%) between pockets that bind FAD and those that bind NAD on a large benchmark set given only the geometry and composition of the backbone of the binding pocket without the use of docking. In addition, we learn rules governing this specificity which can feed into protein functional design protocols. An analysis of the rules found suggests that key features of the binding pocket may be tied to conformational freedom in the ligand. The representation is sufficiently general to be applicable to any discriminatory binding problem. All programs and data sets are freely available to non-commercial users at http://www.sbg.bio.ic.ac.uk/svilp_ligand/.

Exploiting Vector and Multicore Parallelsim for Recursive, Data- and Task-Parallel Programs

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

Ren, Bin; Krishnamoorthy, Sriram; Agrawal, Kunal

Modern hardware contains parallel execution resources that are well-suited for data-parallelism-vector units-and task parallelism-multicores. However, most work on parallel scheduling focuses on one type of hardware or the other. In this work, we present a scheduling framework that allows for a unified treatment of task- and data-parallelism. Our key insight is an abstraction, task blocks, that uniformly handles data-parallel iterations and task-parallel tasks, allowing them to be scheduled on vector units or executed independently as multicores. Our framework allows us to define schedulers that can dynamically select between executing task- blocks on vector units or multicores. We show that thesemore » schedulers are asymptotically optimal, and deliver the maximum amount of parallelism available in computation trees. To evaluate our schedulers, we develop program transformations that can convert mixed data- and task-parallel pro- grams into task block-based programs. Using a prototype instantiation of our scheduling framework, we show that, on an 8-core system, we can simultaneously exploit vector and multicore parallelism to achieve 14×-108× speedup over sequential baselines.« less

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

PubMed

Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan

2016-01-01

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

Feature generation using genetic programming with application to fault classification.

PubMed

Guo, Hong; Jack, Lindsay B; Nandi, Asoke K

2005-02-01

One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. In this paper, a GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover autimatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results--using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM--have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionaly, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.

Vector-model-supported approach in prostate plan optimization

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

Liu, Eva Sau Fan; Department of Health Technology and Informatics, The Hong Kong Polytechnic University; Wu, Vincent Wing Cheung

Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100more » previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration number without compromising the plan quality.« less

A helper virus-free HSV-1 vector containing the vesicular glutamate transporter-1 promoter supports expression preferentially in VGLUT1-containing glutamatergic neurons.

PubMed

Zhang, Guo-rong; Geller, Alfred I

2010-05-17

Multiple potential uses of direct gene transfer into neurons require restricting expression to specific classes of glutamatergic neurons. Thus, it is desirable to develop vectors containing glutamatergic class-specific promoters. The three vesicular glutamate transporters (VGLUTs) are expressed in distinct populations of neurons, and VGLUT1 is the predominant VGLUT in the neocortex, hippocampus, and cerebellar cortex. We previously reported a plasmid (amplicon) Herpes Simplex Virus (HSV-1) vector that placed the Lac Z gene under the regulation of the VGLUT1 promoter (pVGLUT1lac). Using helper virus-free vector stocks, we showed that this vector supported approximately 90% glutamatergic neuron-specific expression in postrhinal (POR) cortex, in rats sacrificed at either 4 days or 2 months after gene transfer. We now show that pVGLUT1lac supports expression preferentially in VGLUT1-containing glutamatergic neurons. pVGLUT1lac vector stock was injected into either POR cortex, which contains primarily VGLUT1-containing glutamatergic neurons, or into the ventral medial hypothalamus (VMH), which contains predominantly VGLUT2-containing glutamatergic neurons. Rats were sacrificed at 4 days after gene transfer, and the types of cells expressing ss-galactosidase were determined by immunofluorescent costaining. Cell counts showed that pVGLUT1lac supported expression in approximately 10-fold more cells in POR cortex than in the VMH, whereas a control vector supported expression in similar numbers of cells in these two areas. Further, in POR cortex, pVGLUT1lac supported expression predominately in VGLUT1-containing neurons, and, in the VMH, pVGLUT1lac showed an approximately 10-fold preference for the rare VGLUT1-containing neurons. VGLUT1-specific expression may benefit specific experiments on learning or specific gene therapy approaches, particularly in the neocortex. Copyright 2010 Elsevier B.V. All rights reserved.

Vector 33: A reduce program for vector algebra and calculus in orthogonal curvilinear coordinates

NASA Astrophysics Data System (ADS)

Harper, David

1989-06-01

This paper describes a package with enables REDUCE 3.3 to perform algebra and calculus operations upon vectors. Basic algebraic operations between vectors and between scalars and vectors are provided, including scalar (dot) product and vector (cross) product. The vector differential operators curl, divergence, gradient and Laplacian are also defined, and are valid in any orthogonal curvilinear coordinate system. The package is written in RLISP to allow algebra and calculus to be performed using notation identical to that for operations. Scalars and vectors can be mixed quite freely in the same expression. The package will be of interest to mathematicians, engineers and scientists who need to perform vector calculations in orthogonal curvilinear coordinates.

Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

NASA Astrophysics Data System (ADS)

Jia, Rui-Sheng; Sun, Hong-Mei; Peng, Yan-Jun; Liang, Yong-Quan; Lu, Xin-Ming

2017-07-01

Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

The NRC Research Associateship Program has Greatly Enhanced the Solar Research at Marshall Space Flight Center During the Last Quarter Century

NASA Technical Reports Server (NTRS)

Gary, G. A.

2003-01-01

Under the educational Resident Research Associateships (RRA) program, NASA Headquarters funds post-doctoral research scientists through a contract with the National Research Council (NRC). This short article reviews the important influence that the RRAs have had on solar research at NASA s Marshall Space Flight Center (MSFC). Through the RRA program the National Research Council under the National Academy of Sciences has provided the Marshall Space Flight Center s Solar Physics Group with 29 post-doctorial research associateships since 1975. This starting date corresponds with the increased research activity in solar physics at MSFC. A number of MSFC scientists had been working on and supporting NASA s Skylab Mission in operation from May 1973 until February 1974. This scientific effort included the development MSFC s X-ray telescope SO56 and the development of the United States first full-vector magnetograph. Numerous engineers and scientists at MSFC supported the development and operation of the cluster of solar telescopes on the Apollo Telescope Mount (ATM), a principal part of the Skylab orbiting workshop. With the enormous volume of new and exciting solar data of the solar corona, MSFC dedicated a group of scientists to analyze these data and develop new solar instruments and programs. With this new initiative, came the world- renowned solar prominence expert, Dr. Einar Tandberg-Hanssen, from the High Altitude Observatory in Boulder, Colorado and the support of the first two RRAs in support of solar physics research.

Vector control programs in Saint Johns County, Florida and Guayas, Ecuador: successes and barriers to integrated vector management.

PubMed

Naranjo, Diana P; Qualls, Whitney A; Jurado, Hugo; Perez, Juan C; Xue, Rui-De; Gomez, Eduardo; Beier, John C

2014-07-02

Vector-borne diseases (VBDs) and mosquito control programs (MCPs) diverge in settings and countries, and lead control specialists need to be aware of the most effective control strategies. Integrated Vector Management (IVM) strategies, once implemented in MCPs, aim to reduce cost and optimize protection of the populations against VBDs. This study presents a strengths, weaknesses, opportunities, and threats (SWOT) analysis to compare IVM strategies used by MCPs in Saint Johns County, Florida and Guayas, Ecuador. This research evaluates MCPs strategies to improve vector control activities. Methods included descriptive findings of the MCP operations. Information was obtained from vector control specialists, directors, and residents through field trips, surveys, and questionnaires. Evaluations of the strategies and assets of the control programs where obtained through SWOT analysis and within an IVM approach. Organizationally, the Floridian MCP is a tax-based District able to make decisions independently from county government officials, with the oversight of an elected board of commissioners. The Guayas program is directed by the country government and assessed by non-governmental organizations like the World health Organization. Operationally, the Floridian MCP conducts entomological surveillance and the Ecuadorian MCP focuses on epidemiological monitoring of human disease cases. Strengths of both MCPs were their community participation and educational programs. Weaknesses for both MCPs included limitations in budgets and technical capabilities. Opportunities, for both MCPs, are additional funding and partnerships with private, non-governmental, and governmental organizations. Threats experienced by both MCPs included political constraints and changes in the social and ecological environment that affect mosquito densities and control efforts. IVM pillars for policy making were used to compare the information among the programs. Differences included how the Ecuadorian MCP relies heavily on the community for vector control while the American MCP relies on technologies and research. IVM based recommendations direct health policy leaders toward improving surveillance systems both entomologically and epidemiologically, improving community risk perceptions by integrating components of community participation, maximizing resources though the use of applied research, and protecting the environment by selecting low-risk pesticides. Outcomes of the research revealed that inter-sectorial and multidisciplinary interventions are critical to improve public health.

Stochastic subset selection for learning with kernel machines.

PubMed

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

Engine inlet distortion in a 9.2 percent scaled vectored thrust STOVL model in ground effect

NASA Technical Reports Server (NTRS)

Johns, Albert L.; Neiner, George; Flood, J. D.; Amuedo, K. C.; Strock, T. W.

1989-01-01

Advanced Short Takeoff/Vertical Landing (STOVL) aircraft which can operate from remote locations, damaged runways, and small air capable ships are being pursued for deployment around the turn of the century. To achieve this goal, a cooperative program has been defined for testing in the NASA Lewis 9- by 15-foot Low Speed Wind Tunnel (LSWT) to establish a database for hot gas ingestion, one of the technologies critical to STOVL. This paper presents results showing the engine inlet distortions (both temperature and pressure) in a 9.2 percent scale Vectored Thrust STOVL model in ground effects. Results are shown for the forward nozzle splay angles of 0, -6, and 18 deg. The model support system had 4 deg of freedom, heated high pressure air for nozzle flow, and a suction system exhaust for inlet flow. The headwind (freestream) velocity was varied from 8 to 23 kn.

Yellow Fever Virus in Haemagogus leucocelaenus and Aedes serratus Mosquitoes, Southern Brazil, 2008

PubMed Central

Cardoso, Jáder da C.; de Almeida, Marco A.B.; dos Santos, Edmilson; da Fonseca, Daltro F.; Sallum, Maria A.M.; Noll, Carlos A.; Monteiro, Hamilton A. de O.; Cruz, Ana C.R.; Carvalho, Valéria L.; Pinto, Eliana V.; Castro, Francisco C.; Neto, Joaquim P. Nunes; Segura, Maria N.O.

2010-01-01

Yellow fever virus (YFV) was isolated from Haemagogus leucocelaenus mosquitoes during an epizootic in 2001 in the Rio Grande do Sul State in southern Brazil. In October 2008, a yellow fever outbreak was reported there, with nonhuman primate deaths and human cases. This latter outbreak led to intensification of surveillance measures for early detection of YFV and support for vaccination programs. We report entomologic surveillance in 2 municipalities that recorded nonhuman primate deaths. Mosquitoes were collected at ground level, identified, and processed for virus isolation and molecular analyses. Eight YFV strains were isolated (7 from pools of Hg. leucocelaenus mosquitoes and another from Aedes serratus mosquitoes); 6 were sequenced, and they grouped in the YFV South American genotype I. The results confirmed the role of Hg. leucocelaenus mosquitoes as the main YFV vector in southern Brazil and suggest that Ae. serratus mosquitoes may have a potential role as a secondary vector. PMID:21122222

Geary autocorrelation and DCCA coefficient: Application to predict apoptosis protein subcellular localization via PSSM

NASA Astrophysics Data System (ADS)

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2017-02-01

Apoptosis is a fundamental process controlling normal tissue homeostasis by regulating a balance between cell proliferation and death. Predicting subcellular location of apoptosis proteins is very helpful for understanding its mechanism of programmed cell death. Prediction of apoptosis protein subcellular location is still a challenging and complicated task, and existing methods mainly based on protein primary sequences. In this paper, we propose a new position-specific scoring matrix (PSSM)-based model by using Geary autocorrelation function and detrended cross-correlation coefficient (DCCA coefficient). Then a 270-dimensional (270D) feature vector is constructed on three widely used datasets: ZD98, ZW225 and CL317, and support vector machine is adopted as classifier. The overall prediction accuracies are significantly improved by rigorous jackknife test. The results show that our model offers a reliable and effective PSSM-based tool for prediction of apoptosis protein subcellular localization.

Yellow fever virus in Haemagogus leucocelaenus and Aedes serratus mosquitoes, southern Brazil, 2008.

PubMed

Cardoso, Jader da C; de Almeida, Marco A B; dos Santos, Edmilson; da Fonseca, Daltro F; Sallum, Maria A M; Noll, Carlos A; Monteiro, Hamilton A de O; Cruz, Ana C R; Carvalho, Valeria L; Pinto, Eliana V; Castro, Francisco C; Nunes Neto, Joaquim P; Segura, Maria N O; Vasconcelos, Pedro F C

2010-12-01

Yellow fever virus (YFV) was isolated from Haemagogus leucocelaenus mosquitoes during an epizootic in 2001 in the Rio Grande do Sul State in southern Brazil. In October 2008, a yellow fever outbreak was reported there, with nonhuman primate deaths and human cases. This latter outbreak led to intensification of surveillance measures for early detection of YFV and support for vaccination programs. We report entomologic surveillance in 2 municipalities that recorded nonhuman primate deaths. Mosquitoes were collected at ground level, identified, and processed for virus isolation and molecular analyses. Eight YFV strains were isolated (7 from pools of Hg. leucocelaenus mosquitoes and another from Aedes serratus mosquitoes); 6 were sequenced, and they grouped in the YFV South American genotype I. The results confirmed the role of Hg. leucocelaenus mosquitoes as the main YFV vector in southern Brazil and suggest that Ae. serratus mosquitoes may have a potential role as a secondary vector.

Detrended cross-correlation coefficient: Application to predict apoptosis protein subcellular localization.

PubMed

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2016-12-01

Apoptosis, or programed cell death, plays a central role in the development and homeostasis of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful for understanding the apoptosis mechanism. The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this paper, we introduce a new position-specific scoring matrix (PSSM)-based method by using detrended cross-correlation (DCCA) coefficient of non-overlapping windows. Then a 190-dimensional (190D) feature vector is constructed on two widely used datasets: CL317 and ZD98, and support vector machine is adopted as classifier. To evaluate the proposed method, objective and rigorous jackknife cross-validation tests are performed on the two datasets. The results show that our approach offers a novel and reliable PSSM-based tool for prediction of apoptosis protein subcellular localization. Copyright © 2016 Elsevier Inc. All rights reserved.

Pupal habitat productivity of Anopheles gambiae complex mosquitoes in a rural village in western Kenya.

PubMed

Mutuku, Francis M; Bayoh, M Nabie; Gimnig, John E; Vulule, John M; Kamau, Luna; Walker, Edward D; Kabiru, Ephantus; Hawley, William A

2006-01-01

The productivity of larval habitats of the malaria vector Anopheles gambiae for pupae (the stage preceding adult metamorphosis) is poorly known, yet adult emergence from habitats is the primary determinant of vector density. To assess it, we used absolute sampling methods in four studies involving daily sampling for 25 days in 6 habitat types in a village in western Kenya. Anopheles gambiae s.s. comprised 82.5% of emergent adults and Anopheles arabiensis the remainder. Pupal production occurred from a subset of habitats, primarily soil burrow pits, and was discontinuous in time, even when larvae occupied all habitats continuously. Habitat stability was positively associated with pupal productivity. In a dry season, pupal productivity was distributed between burrow pits and pools in streambeds. Overall, these data support the notion that source reduction measures against recognizably productive habitats would be a useful component of an integrated management program for An. gambiae in villages.

A hybrid approach to select features and classify diseases based on medical data

NASA Astrophysics Data System (ADS)

AbdelLatif, Hisham; Luo, Jiawei

2018-03-01

Feature selection is popular problem in the classification of diseases in clinical medicine. Here, we developing a hybrid methodology to classify diseases, based on three medical datasets, Arrhythmia, Breast cancer, and Hepatitis datasets. This methodology called k-means ANOVA Support Vector Machine (K-ANOVA-SVM) uses K-means cluster with ANOVA statistical to preprocessing data and selection the significant features, and Support Vector Machines in the classification process. To compare and evaluate the performance, we choice three classification algorithms, decision tree Naïve Bayes, Support Vector Machines and applied the medical datasets direct to these algorithms. Our methodology was a much better classification accuracy is given of 98% in Arrhythmia datasets, 92% in Breast cancer datasets and 88% in Hepatitis datasets, Compare to use the medical data directly with decision tree Naïve Bayes, and Support Vector Machines. Also, the ROC curve and precision with (K-ANOVA-SVM) Achieved best results than other algorithms

Alpharetroviral Self-inactivating Vectors: Long-term Transgene Expression in Murine Hematopoietic Cells and Low Genotoxicity

PubMed Central

Suerth, Julia D; Maetzig, Tobias; Brugman, Martijn H; Heinz, Niels; Appelt, Jens-Uwe; Kaufmann, Kerstin B; Schmidt, Manfred; Grez, Manuel; Modlich, Ute; Baum, Christopher; Schambach, Axel

2012-01-01

Comparative integrome analyses have highlighted alpharetroviral vectors with a relatively neutral, and thus favorable, integration spectrum. However, previous studies used alpharetroviral vectors harboring viral coding sequences and intact long-terminal repeats (LTRs). We recently developed self-inactivating (SIN) alpharetroviral vectors with an advanced split-packaging design. In a murine bone marrow (BM) transplantation model we now compared alpharetroviral, gammaretroviral, and lentiviral SIN vectors and showed that all vectors transduced hematopoietic stem cells (HSCs), leading to comparable, sustained multilineage transgene expression in primary and secondary transplanted mice. Alpharetroviral integrations were decreased near transcription start sites, CpG islands, and potential cancer genes compared with gammaretroviral, and decreased in genes compared with lentiviral integrations. Analyzing the transcriptome and intragenic integrations in engrafting cells, we observed stronger correlations between in-gene integration targeting and transcriptional activity for gammaretroviral and lentiviral vectors than for alpharetroviral vectors. Importantly, the relatively “extragenic” alpharetroviral integration pattern still supported long-term transgene expression upon serial transplantation. Furthermore, sensitive genotoxicity studies revealed a decreased immortalization incidence compared with gammaretroviral and lentiviral SIN vectors. We conclude that alpharetroviral SIN vectors have a favorable integration pattern which lowers the risk of insertional mutagenesis while supporting long-term transgene expression in the progeny of transplanted HSCs. PMID:22334016

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets.

PubMed

Marchese Robinson, Richard L; Palczewska, Anna; Palczewski, Jan; Kidley, Nathan

2017-08-28

The ability to interpret the predictions made by quantitative structure-activity relationships (QSARs) offers a number of advantages. While QSARs built using nonlinear modeling approaches, such as the popular Random Forest algorithm, might sometimes be more predictive than those built using linear modeling approaches, their predictions have been perceived as difficult to interpret. However, a growing number of approaches have been proposed for interpreting nonlinear QSAR models in general and Random Forest in particular. In the current work, we compare the performance of Random Forest to those of two widely used linear modeling approaches: linear Support Vector Machines (SVMs) (or Support Vector Regression (SVR)) and partial least-squares (PLS). We compare their performance in terms of their predictivity as well as the chemical interpretability of the predictions using novel scoring schemes for assessing heat map images of substructural contributions. We critically assess different approaches for interpreting Random Forest models as well as for obtaining predictions from the forest. We assess the models on a large number of widely employed public-domain benchmark data sets corresponding to regression and binary classification problems of relevance to hit identification and toxicology. We conclude that Random Forest typically yields comparable or possibly better predictive performance than the linear modeling approaches and that its predictions may also be interpreted in a chemically and biologically meaningful way. In contrast to earlier work looking at interpretation of nonlinear QSAR models, we directly compare two methodologically distinct approaches for interpreting Random Forest models. The approaches for interpreting Random Forest assessed in our article were implemented using open-source programs that we have made available to the community. These programs are the rfFC package ( https://r-forge.r-project.org/R/?group_id=1725 ) for the R statistical programming language and the Python program HeatMapWrapper [ https://doi.org/10.5281/zenodo.495163 ] for heat map generation.

Identifying saltcedar with hyperspectral data and support vector machines

USDA-ARS?s Scientific Manuscript database

Saltcedar (Tamarix spp.) are a group of dense phreatophytic shrubs and trees that are invasive to riparian areas throughout the United States. This study determined the feasibility of using hyperspectral data and a support vector machine (SVM) classifier to discriminate saltcedar from other cover t...

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Quantifying Melt Ponds in the Beaufort MIZ using Linear Support Vector Machines from High Resolution Panchromatic Images

NASA Astrophysics Data System (ADS)

Ortiz, M.; Graber, H. C.; Wilkinson, J.; Nyman, L. M.; Lund, B.

2017-12-01

Much work has been done on determining changes in summer ice albedo and morphological properties of melt ponds such as depth, shape and distribution using in-situ measurements and satellite-based sensors. Although these studies have dedicated much pioneering work in this area, there still lacks sufficient spatial and temporal scales. We present a prototype algorithm using Linear Support Vector Machines (LSVMs) designed to quantify the evolution of melt pond fraction from a recently government-declassified high-resolution panchromatic optical dataset. The study area of interest lies within the Beaufort marginal ice zone (MIZ), where several in-situ instruments were deployed by the British Antarctic Survey in joint with the MIZ Program, from April-September, 2014. The LSVM uses four dimensional feature data from the intensity image itself, and from various textures calculated from a modified first-order histogram technique using probability density of occurrences. We explore both the temporal evolution of melt ponds and spatial statistics such as pond fraction, pond area, and number pond density, to name a few. We also introduce a linear regression model that can potentially be used to estimate average pond area by ingesting several melt pond statistics and shape parameters.

Blood glucose level prediction based on support vector regression using mobile platforms.

PubMed

Reymann, Maximilian P; Dorschky, Eva; Groh, Benjamin H; Martindale, Christine; Blank, Peter; Eskofier, Bjoern M

2016-08-01

The correct treatment of diabetes is vital to a patient's health: Staying within defined blood glucose levels prevents dangerous short- and long-term effects on the body. Mobile devices informing patients about their future blood glucose levels could enable them to take counter-measures to prevent hypo or hyper periods. Previous work addressed this challenge by predicting the blood glucose levels using regression models. However, these approaches required a physiological model, representing the human body's response to insulin and glucose intake, or are not directly applicable to mobile platforms (smart phones, tablets). In this paper, we propose an algorithm for mobile platforms to predict blood glucose levels without the need for a physiological model. Using an online software simulator program, we trained a Support Vector Regression (SVR) model and exported the parameter settings to our mobile platform. The prediction accuracy of our mobile platform was evaluated with pre-recorded data of a type 1 diabetes patient. The blood glucose level was predicted with an error of 19 % compared to the true value. Considering the permitted error of commercially used devices of 15 %, our algorithm is the basis for further development of mobile prediction algorithms.

Applying spectral unmixing and support vector machine to airborne hyperspectral imagery for detecting giant reed

USDA-ARS?s Scientific Manuscript database

This study evaluated linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and support vector machine (SVM) techniques for detecting and mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems and riparian areas throughout the southern ...

Support vector machines classifiers of physical activities in preschoolers

USDA-ARS?s Scientific Manuscript database

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

Fabric wrinkle characterization and classification using modified wavelet coefficients and optimized support-vector-machine classifier

USDA-ARS?s Scientific Manuscript database

This paper presents a novel wrinkle evaluation method that uses modified wavelet coefficients and an optimized support-vector-machine (SVM) classification scheme to characterize and classify wrinkle appearance of fabric. Fabric images were decomposed with the wavelet transform (WT), and five parame...

Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points

EPA Science Inventory

Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two convention...

An overview of the NSCAT/N-ROSS program

NASA Technical Reports Server (NTRS)

Martin, B. D.; Freilich, Michael H.; Li, F. K.; Callahan, Phillip S.

1986-01-01

The NASA Scatterometer (NSCAT) to fly on the U.S. Navy Remote Ocean Sensing System (N-ROSS) mission is presented. The overall N-ROSS mission, the NSCAT flight instrument and groundbased data processing/distribution system, and NASA-supported science and verification activities are described. The N-ROSS system is designed to provide measurements of near-surface wind, ocean topography, wave height, sea-surface temperature, and atmospheric water content over the global oceans. The NSCAT is an improved version of the Seasat scatterometer. It will measure near surface vector winds.

A highly stable blood meal alternative for rearing Aedes and Anopheles mosquitoes.

PubMed

Baughman, Ted; Peterson, Chelsea; Ortega, Corrie; Preston, Sarah R; Paton, Christopher; Williams, Jessica; Guy, Amy; Omodei, Gavin; Johnson, Brian; Williams, Helen; O'Neill, Scott L; Ritchie, Scott A; Dobson, Stephen L; Madan, Damian

2017-12-01

We investigated alternatives to whole blood for blood feeding of mosquitoes with a focus on improved stability and compatibility with mass rearing programs. In contrast to whole blood, an artificial blood diet of ATP-supplemented plasma was effective in maintaining mosquito populations and was compatible with storage for extended periods refrigerated, frozen, and as a lyophilized powder. The plasma ATP diet supported rearing of both Anopheles and Aedes mosquitoes. It was also effective in rearing Wolbachia-infected Aedes mosquitoes, suggesting compatibility with vector control efforts.

Personal Computer Transport Analysis Program

NASA Technical Reports Server (NTRS)

DiStefano, Frank, III; Wobick, Craig; Chapman, Kirt; McCloud, Peter

2012-01-01

The Personal Computer Transport Analysis Program (PCTAP) is C++ software used for analysis of thermal fluid systems. The program predicts thermal fluid system and component transients. The output consists of temperatures, flow rates, pressures, delta pressures, tank quantities, and gas quantities in the air, along with air scrubbing component performance. PCTAP s solution process assumes that the tubes in the system are well insulated so that only the heat transfer between fluid and tube wall and between adjacent tubes is modeled. The system described in the model file is broken down into its individual components; i.e., tubes, cold plates, heat exchangers, etc. A solution vector is built from the components and a flow is then simulated with fluid being transferred from one component to the next. The solution vector of components in the model file is built at the initiation of the run. This solution vector is simply a list of components in the order of their inlet dependency on other components. The component parameters are updated in the order in which they appear in the list at every time step. Once the solution vectors have been determined, PCTAP cycles through the components in the solution vector, executing their outlet function for each time-step increment.

Simulation model of the F/A-18 high angle-of-attack research vehicle utilized for the design of advanced control laws

NASA Technical Reports Server (NTRS)

Strickland, Mark E.; Bundick, W. Thomas; Messina, Michael D.; Hoffler, Keith D.; Carzoo, Susan W.; Yeager, Jessie C.; Beissner, Fred L., Jr.

1996-01-01

The 'f18harv' six degree-of-freedom nonlinear batch simulation used to support research in advanced control laws and flight dynamics issues as part of NASA's High Alpha Technology Program is described in this report. This simulation models an F/A-18 airplane modified to incorporate a multi-axis thrust-vectoring system for augmented pitch and yaw control power and actuated forebody strakes for enhanced aerodynamic yaw control power. The modified configuration is known as the High Alpha Research Vehicle (HARV). The 'f18harv' simulation was an outgrowth of the 'f18bas' simulation which modeled the basic F/A-18 with a preliminary version of a thrust-vectoring system designed for the HARV. The preliminary version consisted of two thrust-vectoring vanes per engine nozzle compared with the three vanes per engine actually employed on the F/A-18 HARV. The modeled flight envelope is extensive in that the aerodynamic database covers an angle-of-attack range of -10 degrees to +90 degrees, sideslip range of -20 degrees to +20 degrees, a Mach Number range between 0.0 and 2.0, and an altitude range between 0 and 60,000 feet.

Loss of genetic diversity in Culex quinquefasciatus targeted by a lymphatic filariasis vector control program in Recife, Brazil.

PubMed

Cartaxo, Marina F S; Ayres, Constância F J; Weetman, David

2011-09-01

Recife is one of the largest cities in north-eastern Brazil and is endemic for lymphatic filariasis transmitted by Culex quinquefasciatus. Since 2003 a control program has targeted mosquito larvae by elimination of breeding sites and bimonthly application of Bacillus sphaericus. To assess the impact of this program on the local vector population we monitored the genetic diversity and differentiation of Cx. quinquefasciatus using microsatellites and a B. sphaericus-resistance associated mutation (cqm1(REC)) over a 3-year period. We detected a significant but gradual decline in allelic diversity, which, coupled with subtle temporal genetic structure, suggests a major impact of the control program on the vector population. Selection on cqm1(REC) does not appear to be involved with loss of neutral diversity from the population, with no temporal trend in resistant allele frequency and no correlation with microsatellite differentiation. The evidence for short-term genetic drift we detected suggests a low ratio of effective population size: census population size for Cx. quinquefasciatus, perhaps coupled with strong geographically-restricted population structure. Spatial definition of populations will be an important step for success of an expanded vector control program. Copyright © 2011 Royal Society of Tropical Medicine and Hygiene. Published by Elsevier Ltd. All rights reserved.

Vector-model-supported optimization in volumetric-modulated arc stereotactic radiotherapy planning for brain metastasis

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

Liu, Eva Sau Fan; Department of Health Technology and Informatics, The Hong Kong Polytechnic University; Wu, Vincent Wing Cheung

Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features with the current case. The prsent study aimed to develop a vector model that could reduce planning time by applying the optimization parameters from those retrieved reference cases. Thirty-six VMA-SRT cases of brain metastasis (gender, male [n = 23], female [n = 13]; age range, 32 to 81 years old) were collected and used as a reference database. Another 10 VMA-SRT cases were planned with both conventional optimization and vector-model-supported optimization, followingmore » the oncologists' clinical dose prescriptions. Planning time and plan quality measures were compared using the 2-sided paired Wilcoxon signed rank test with a significance level of 0.05, with positive false discovery rate (pFDR) of less than 0.05. With vector-model-supported optimization, there was a significant reduction in the median planning time, a 40% reduction from 3.7 to 2.2 hours (p = 0.002, pFDR = 0.032), and for the number of iterations, a 30% reduction from 8.5 to 6.0 (p = 0.006, pFDR = 0.047). The quality of plans from both approaches was comparable. From these preliminary results, vector-model-supported optimization can expedite the optimization of VMA-SRT for brain metastasis while maintaining plan quality.« less

The vibro-acoustic mapping of low gravity trajectories on a Learjet aircraft

NASA Technical Reports Server (NTRS)

Grodsinsky, C. M.; Sutliff, T. J.

1990-01-01

Terrestrial low gravity research techniques have been employed to gain a more thorough understanding of basic science and technology concepts. One technique frequently used involves flying parabolic trajectories aboard the NASA Lewis Research Center Learjet aircraft. A measurement program was developed to support an isolation system conceptual design. This program primarily was intended to measure time correlated high frequency accelerations (up to 100 Hz) present at various locations throughout the Learjet during a series of trajectories and flights. As suspected, the measurements obtained revealed that the environment aboard such an aircraft can not simply be described in terms of the static level low gravity g vector obtained, but that it also must account for both rigid body and high frequency vibro-acoustic dynamics.

PlasmoGEM, a database supporting a community resource for large-scale experimental genetics in malaria parasites.

PubMed

Schwach, Frank; Bushell, Ellen; Gomes, Ana Rita; Anar, Burcu; Girling, Gareth; Herd, Colin; Rayner, Julian C; Billker, Oliver

2015-01-01

The Plasmodium Genetic Modification (PlasmoGEM) database (http://plasmogem.sanger.ac.uk) provides access to a resource of modular, versatile and adaptable vectors for genome modification of Plasmodium spp. parasites. PlasmoGEM currently consists of >2000 plasmids designed to modify the genome of Plasmodium berghei, a malaria parasite of rodents, which can be requested by non-profit research organisations free of charge. PlasmoGEM vectors are designed with long homology arms for efficient genome integration and carry gene specific barcodes to identify individual mutants. They can be used for a wide array of applications, including protein localisation, gene interaction studies and high-throughput genetic screens. The vector production pipeline is supported by a custom software suite that automates both the vector design process and quality control by full-length sequencing of the finished vectors. The PlasmoGEM web interface allows users to search a database of finished knock-out and gene tagging vectors, view details of their designs, download vector sequence in different formats and view available quality control data as well as suggested genotyping strategies. We also make gDNA library clones and intermediate vectors available for researchers to produce vectors for themselves. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Vector Acoustics, Vector Sensors, and 3D Underwater Imaging

NASA Astrophysics Data System (ADS)

Lindwall, D.

2007-12-01

Vector acoustic data has two more dimensions of information than pressure data and may allow for 3D underwater imaging with much less data than with hydrophone data. The vector acoustic sensors measures the particle motions due to passing sound waves and, in conjunction with a collocated hydrophone, the direction of travel of the sound waves. When using a controlled source with known source and sensor locations, the reflection points of the sound field can be determined with a simple trigonometric calculation. I demonstrate this concept with an experiment that used an accelerometer based vector acoustic sensor in a water tank with a short-pulse source and passive scattering targets. The sensor consists of a three-axis accelerometer and a matched hydrophone. The sound source was a standard transducer driven by a short 7 kHz pulse. The sensor was suspended in a fixed location and the hydrophone was moved about the tank by a robotic arm to insonify the tank from many locations. Several floats were placed in the tank as acoustic targets at diagonal ranges of approximately one meter. The accelerometer data show the direct source wave as well as the target scattered waves and reflections from the nearby water surface, tank bottom and sides. Without resorting to the usual methods of seismic imaging, which in this case is only two dimensional and relied entirely on the use of a synthetic source aperture, the two targets, the tank walls, the tank bottom, and the water surface were imaged. A directional ambiguity inherent to vector sensors is removed by using collocated hydrophone data. Although this experiment was in a very simple environment, it suggests that 3-D seismic surveys may be achieved with vector sensors using the same logistics as a 2-D survey that uses conventional hydrophones. This work was supported by the Office of Naval Research, program element 61153N.

Multi-country assessment of residual bio-efficacy of insecticides used for indoor residual spraying in malaria control on different surface types: results from program monitoring in 17 PMI/USAID-supported IRS countries.

PubMed

Dengela, Dereje; Seyoum, Aklilu; Lucas, Bradford; Johns, Benjamin; George, Kristen; Belemvire, Allison; Caranci, Angela; Norris, Laura C; Fornadel, Christen M

2018-01-30

Indoor residual spraying (IRS) is the application of insecticide to the interior walls of household structures that often serve as resting sites for mosquito vectors of malaria. Human exposure to malaria vectors is reduced when IRS involves proper application of pre-determined concentrations of the active ingredient specific to the insecticide formulation of choice. The impact of IRS can be affected by the dosage of insecticide, spray coverage, vector behavior, vector susceptibility to insecticides, and the residual efficacy of the insecticide applied. This report compiles data on the residual efficacy of insecticides used in IRS campaigns implemented by the United States President's Malaria Initiative (PMI)/United States Agency for International Development (USAID) in 17 African countries and compares observed length of efficacy to ranges proposed in World Health Organization (WHO) guidelines. Additionally, this study provides initial analysis on variation of mosquito mortality depending on the surface material of sprayed structures, country spray program, year of implementation, source of tested mosquitoes, and type of insecticide. Residual efficacy of the insecticides used for PMI/USAID-supported IRS campaigns was measured in Benin, Burkina Faso, Ethiopia, Ghana, Kenya, Liberia, Madagascar, Malawi, Mali, Mozambique, Nigeria, Rwanda, Senegal, Tanzania, Uganda, Zambia and Zimbabwe. The WHO cone bioassay tests were used to assess the mortality rate of mosquitoes exposed to insecticide-treated mud, wood, cement, and other commonly used housing materials. Baseline tests were performed within weeks of IRS application and follow-up tests were continued until the mortality of exposed mosquitoes dropped below 80% or the program monitoring period ended. Residual efficacy in months was then evaluated with respect to WHO guidelines that provide suggested ranges of residual efficacy for insecticide formulations recommended for use in IRS. Where the data allowed, direct comparisons of mosquito mortality rates were then made to determine any significant differences when comparing insecticide formulation, country, year, surface type, and the source of the mosquitoes used in testing. The residual efficacy of alpha-cypermethrin ranged from 4 to 10 months (average = 6.4 months), with no reported incidents of underperformance when compared to the efficacy range provided in WHO guidelines. Deltamethrin residual efficacy results reported a range of 1 to 10 months (average = 4.9 months), with two instances of underperformance. The residual efficacy of bendiocarb ranged from 2 weeks to 7 months (average = 2.8 months) and failed to achieve proposed minimum efficacy on 14 occasions. Lastly, long-lasting pirimiphos-methyl efficacy ranged from 2 months to 9 months (average = 5.3 months), but reported 13 incidents of underperformance. Much of the data used to determine application rate and expected efficacy of insecticides approved for use in IRS programs are collected in controlled laboratory or pilot field studies. However, the generalizability of the results obtained under controlled conditions are limited and unlikely to account for variation in locally sourced housing materials, climate, and the myriad other factors that may influence the bio-efficacy of insecticides. Here, data are presented that confirm the variation in residual efficacy observed when monitoring household surfaces sprayed during PMI/USAID-supported IRS campaigns. All insecticides except alpha-cypermethrin showed evidence of failing to meet the minimum range of residual efficacy proposed in WHO criteria at least once. However, this initial effort in characterizing program-wide insecticide bio-efficacy indicates that some insecticides, such as bendiocarb and pirimiphos-methyl, may be vulnerable to variations in the local environment. Additionally, the comparative analysis performed in this study provides evidence that mosquito mortality rates differ with respect to factors including: the types of insecticide sprayed, surface material, geographical location, year of spraying, and tested mosquitoes. It is, therefore, important to locally assess the residual efficacy of insecticides on various surfaces to inform IRS programming.

Guidelines for developing vectorizable computer programs

NASA Technical Reports Server (NTRS)

Miner, E. W.

1982-01-01

Some fundamental principles for developing computer programs which are compatible with array-oriented computers are presented. The emphasis is on basic techniques for structuring computer codes which are applicable in FORTRAN and do not require a special programming language or exact a significant penalty on a scalar computer. Researchers who are using numerical techniques to solve problems in engineering can apply these basic principles and thus develop transportable computer programs (in FORTRAN) which contain much vectorizable code. The vector architecture of the ASC is discussed so that the requirements of array processing can be better appreciated. The "vectorization" of a finite-difference viscous shock-layer code is used as an example to illustrate the benefits and some of the difficulties involved. Increases in computing speed with vectorization are illustrated with results from the viscous shock-layer code and from a finite-element shock tube code. The applicability of these principles was substantiated through running programs on other computers with array-associated computing characteristics, such as the Hewlett-Packard (H-P) 1000-F.

Agro-ecology, household economics and malaria in Uganda: empirical correlations between agricultural and health outcomes

PubMed Central

2014-01-01

Background This paper establishes empirical evidence relating the agriculture and health sectors in Uganda. The analysis explores linkages between agricultural management, malaria and implications for improving community health outcomes in rural Uganda. The goal of this exploratory work is to expand the evidence-base for collaboration between the agricultural and health sectors in Uganda. Methods The paper presents an analysis of data from the 2006 Uganda National Household Survey using a parametric multivariate Two-Limit Tobit model to identify correlations between agro-ecological variables including geographically joined daily seasonal precipitation records and household level malaria risk. The analysis of agricultural and environmental factors as they affect household malaria rates, disaggregated by age-group, is inspired by a complimentary review of existing agricultural malaria literature indicating a gap in evidence with respect to agricultural management as a form of malaria vector management. Crop choices and agricultural management practices may contribute to vector control through the simultaneous effects of reducing malaria transmission, improving housing and nutrition through income gains, and reducing insecticide resistance in both malaria vectors and agricultural pests. Results The econometric results show the existence of statistically significant correlations between crops, such as sweet potatoes/yams, beans, millet and sorghum, with household malaria risk. Local environmental factors are also influential- daily maximum temperature is negatively correlated with malaria, while daily minimum temperature is positively correlated with malaria, confirming trends in the broader literature are applicable to the Ugandan context. Conclusions Although not necessarily causative, the findings provide sufficient evidence to warrant purposefully designed work to test for agriculture health causation in vector management. A key constraint to modeling the agricultural basis of malaria transmission is the lack of data integrating both the health and agricultural information necessary to satisfy the differing methodologies used by the two sectors. A national platform for collaboration between the agricultural and health sectors could help align programs to achieve better measurements of agricultural interactions with vector reproduction and evaluate the potential for agricultural policy and programs to support rural malaria control. PMID:24990158

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

PubMed Central

HUANG, SHUJUN; CAI, NIANGUANG; PACHECO, PEDRO PENZUTI; NARANDES, SHAVIRA; WANG, YANG; XU, WAYNE

2017-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. PMID:29275361

Genetic shifting: a novel approach for controlling vector-borne diseases.

PubMed

Powell, Jeffrey R; Tabachnick, Walter J

2014-06-01

Rendering populations of vectors of diseases incapable of transmitting pathogens through genetic methods has long been a goal of vector geneticists. We outline a method to achieve this goal that does not involve the introduction of any new genetic variants to the target population. Rather we propose that shifting the frequencies of naturally occurring alleles that confer refractoriness to transmission can reduce transmission below a sustainable level. The program employs methods successfully used in plant and animal breeding. Because no artificially constructed genetically modified organisms (GMOs) are introduced into the environment, the method is minimally controversial. We use Aedes aegypti and dengue virus (DENV) for illustrative purposes but point out that the proposed program is generally applicable to vector-borne disease control. Copyright © 2014 Elsevier Ltd. All rights reserved.

Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates

USDA-ARS?s Scientific Manuscript database

Methods based on sequence data analysis facilitate the tracking of disease outbreaks, allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are used postfactum after an outbreak has happened. Here, we show that support vector machine a...

Support vector machine incremental learning triggered by wrongly predicted samples

NASA Astrophysics Data System (ADS)

Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

2018-05-01

According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

Prediction of Spirometric Forced Expiratory Volume (FEV1) Data Using Support Vector Regression

NASA Astrophysics Data System (ADS)

Kavitha, A.; Sujatha, C. M.; Ramakrishnan, S.

2010-01-01

In this work, prediction of forced expiratory volume in 1 second (FEV1) in pulmonary function test is carried out using the spirometer and support vector regression analysis. Pulmonary function data are measured with flow volume spirometer from volunteers (N=175) using a standard data acquisition protocol. The acquired data are then used to predict FEV1. Support vector machines with polynomial kernel function with four different orders were employed to predict the values of FEV1. The performance is evaluated by computing the average prediction accuracy for normal and abnormal cases. Results show that support vector machines are capable of predicting FEV1 in both normal and abnormal cases and the average prediction accuracy for normal subjects was higher than that of abnormal subjects. Accuracy in prediction was found to be high for a regularization constant of C=10. Since FEV1 is the most significant parameter in the analysis of spirometric data, it appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

Quantum Support Vector Machine for Big Data Classification

NASA Astrophysics Data System (ADS)

Rebentrost, Patrick; Mohseni, Masoud; Lloyd, Seth

2014-09-01

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

All That Glisters Is Not Gold: Sampling-Process Uncertainty in Disease-Vector Surveys with False-Negative and False-Positive Detections

PubMed Central

Abad-Franch, Fernando; Valença-Barbosa, Carolina; Sarquis, Otília; Lima, Marli M.

2014-01-01

Background Vector-borne diseases are major public health concerns worldwide. For many of them, vector control is still key to primary prevention, with control actions planned and evaluated using vector occurrence records. Yet vectors can be difficult to detect, and vector occurrence indices will be biased whenever spurious detection/non-detection records arise during surveys. Here, we investigate the process of Chagas disease vector detection, assessing the performance of the surveillance method used in most control programs – active triatomine-bug searches by trained health agents. Methodology/Principal Findings Control agents conducted triplicate vector searches in 414 man-made ecotopes of two rural localities. Ecotope-specific ‘detection histories’ (vectors or their traces detected or not in each individual search) were analyzed using ordinary methods that disregard detection failures and multiple detection-state site-occupancy models that accommodate false-negative and false-positive detections. Mean (±SE) vector-search sensitivity was ∼0.283±0.057. Vector-detection odds increased as bug colonies grew denser, and were lower in houses than in most peridomestic structures, particularly woodpiles. False-positive detections (non-vector fecal streaks misidentified as signs of vector presence) occurred with probability ∼0.011±0.008. The model-averaged estimate of infestation (44.5±6.4%) was ∼2.4–3.9 times higher than naïve indices computed assuming perfect detection after single vector searches (11.4–18.8%); about 106–137 infestation foci went undetected during such standard searches. Conclusions/Significance We illustrate a relatively straightforward approach to addressing vector detection uncertainty under realistic field survey conditions. Standard vector searches had low sensitivity except in certain singular circumstances. Our findings suggest that many infestation foci may go undetected during routine surveys, especially when vector density is low. Undetected foci can cause control failures and induce bias in entomological indices; this may confound disease risk assessment and mislead program managers into flawed decision making. By helping correct bias in naïve indices, the approach we illustrate has potential to critically strengthen vector-borne disease control-surveillance systems. PMID:25233352

Vector control programs in Saint Johns County, Florida and Guayas, Ecuador: successes and barriers to integrated vector management

PubMed Central

2014-01-01

Background Vector-borne diseases (VBDs) and mosquito control programs (MCPs) diverge in settings and countries, and lead control specialists need to be aware of the most effective control strategies. Integrated Vector Management (IVM) strategies, once implemented in MCPs, aim to reduce cost and optimize protection of the populations against VBDs. This study presents a strengths, weaknesses, opportunities, and threats (SWOT) analysis to compare IVM strategies used by MCPs in Saint Johns County, Florida and Guayas, Ecuador. This research evaluates MCPs strategies to improve vector control activities. Methods Methods included descriptive findings of the MCP operations. Information was obtained from vector control specialists, directors, and residents through field trips, surveys, and questionnaires. Evaluations of the strategies and assets of the control programs where obtained through SWOT analysis and within an IVM approach. Results Organizationally, the Floridian MCP is a tax-based District able to make decisions independently from county government officials, with the oversight of an elected board of commissioners. The Guayas program is directed by the country government and assessed by non-governmental organizations like the World health Organization. Operationally, the Floridian MCP conducts entomological surveillance and the Ecuadorian MCP focuses on epidemiological monitoring of human disease cases. Strengths of both MCPs were their community participation and educational programs. Weaknesses for both MCPs included limitations in budgets and technical capabilities. Opportunities, for both MCPs, are additional funding and partnerships with private, non-governmental, and governmental organizations. Threats experienced by both MCPs included political constraints and changes in the social and ecological environment that affect mosquito densities and control efforts. IVM pillars for policy making were used to compare the information among the programs. Differences included how the Ecuadorian MCP relies heavily on the community for vector control while the American MCP relies on technologies and research. Conclusion IVM based recommendations direct health policy leaders toward improving surveillance systems both entomologically and epidemiologically, improving community risk perceptions by integrating components of community participation, maximizing resources though the use of applied research, and protecting the environment by selecting low-risk pesticides. Outcomes of the research revealed that inter-sectorial and multidisciplinary interventions are critical to improve public health. PMID:24990155

Summer Study Program in Geophysical Fluid Dynamics - The Influence of Convection on Large-Scale Circulations - 1988

DTIC Science & Technology

1989-07-01

the vector of the body force." lo., ,P /’P l> 16 __ __ _ __ ___P . 19 U In the first lecture we define the buoyancy force, develop a simplified...force and l’is a unit vector along the motion vector . Integrating Bernoulli’s law over a closed loop one gets: I also [ C by integrating along the...convection. It is conveiient to write these equations as evolution equations for a atate vector U(x, z, t) where x is the horizontal coordinate vector

Predicting complications of percutaneous coronary intervention using a novel support vector method.

PubMed

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer-Lemeshow χ(2) value (seven cases) and the mean cross-entropy error (eight cases). The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.

Predicting complications of percutaneous coronary intervention using a novel support vector method

PubMed Central

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

Objective To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Materials and methods Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. Results The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer–Lemeshow χ2 value (seven cases) and the mean cross-entropy error (eight cases). Conclusions The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains. PMID:23599229

A support vector machine approach for classification of welding defects from ultrasonic signals

NASA Astrophysics Data System (ADS)

Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming

2014-07-01

Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.

PubMed

Ben Salem, Samira; Bacha, Khmais; Chaari, Abdelkader

2012-09-01

In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

GRASS GIS: The first Open Source Temporal GIS

NASA Astrophysics Data System (ADS)

Gebbert, Sören; Leppelt, Thomas

2015-04-01

GRASS GIS is a full featured, general purpose Open Source geographic information system (GIS) with raster, 3D raster and vector processing support[1]. Recently, time was introduced as a new dimension that transformed GRASS GIS into the first Open Source temporal GIS with comprehensive spatio-temporal analysis, processing and visualization capabilities[2]. New spatio-temporal data types were introduced in GRASS GIS version 7, to manage raster, 3D raster and vector time series. These new data types are called space time datasets. They are designed to efficiently handle hundreds of thousands of time stamped raster, 3D raster and vector map layers of any size. Time stamps can be defined as time intervals or time instances in Gregorian calendar time or relative time. Space time datasets are simplifying the processing and analysis of large time series in GRASS GIS, since these new data types are used as input and output parameter in temporal modules. The handling of space time datasets is therefore equal to the handling of raster, 3D raster and vector map layers in GRASS GIS. A new dedicated Python library, the GRASS GIS Temporal Framework, was designed to implement the spatio-temporal data types and their management. The framework provides the functionality to efficiently handle hundreds of thousands of time stamped map layers and their spatio-temporal topological relations. The framework supports reasoning based on the temporal granularity of space time datasets as well as their temporal topology. It was designed in conjunction with the PyGRASS [3] library to support parallel processing of large datasets, that has a long tradition in GRASS GIS [4,5]. We will present a subset of more than 40 temporal modules that were implemented based on the GRASS GIS Temporal Framework, PyGRASS and the GRASS GIS Python scripting library. These modules provide a comprehensive temporal GIS tool set. The functionality range from space time dataset and time stamped map layer management over temporal aggregation, temporal accumulation, spatio-temporal statistics, spatio-temporal sampling, temporal algebra, temporal topology analysis, time series animation and temporal topology visualization to time series import and export capabilities with support for NetCDF and VTK data formats. We will present several temporal modules that support parallel processing of raster and 3D raster time series. [1] GRASS GIS Open Source Approaches in Spatial Data Handling In Open Source Approaches in Spatial Data Handling, Vol. 2 (2008), pp. 171-199, doi:10.1007/978-3-540-74831-19 by M. Neteler, D. Beaudette, P. Cavallini, L. Lami, J. Cepicky edited by G. Brent Hall, Michael G. Leahy [2] Gebbert, S., Pebesma, E., 2014. A temporal GIS for field based environmental modeling. Environ. Model. Softw. 53, 1-12. [3] Zambelli, P., Gebbert, S., Ciolli, M., 2013. Pygrass: An Object Oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). ISPRS Intl Journal of Geo-Information 2, 201-219. [4] Löwe, P., Klump, J., Thaler, J. (2012): The FOSS GIS Workbench on the GFZ Load Sharing Facility compute cluster, (Geophysical Research Abstracts Vol. 14, EGU2012-4491, 2012), General Assembly European Geosciences Union (Vienna, Austria 2012). [5] Akhter, S., Aida, K., Chemin, Y., 2010. "GRASS GIS on High Performance Computing with MPI, OpenMP and Ninf-G Programming Framework". ISPRS Conference, Kyoto, 9-12 August 2010

Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs.

PubMed

Shamim, Mohammad Tabrez Anwar; Anwaruddin, Mohammad; Nagarajaram, H A

2007-12-15

Fold recognition is a key step in the protein structure discovery process, especially when traditional sequence comparison methods fail to yield convincing structural homologies. Although many methods have been developed for protein fold recognition, their accuracies remain low. This can be attributed to insufficient exploitation of fold discriminatory features. We have developed a new method for protein fold recognition using structural information of amino acid residues and amino acid residue pairs. Since protein fold recognition can be treated as a protein fold classification problem, we have developed a Support Vector Machine (SVM) based classifier approach that uses secondary structural state and solvent accessibility state frequencies of amino acids and amino acid pairs as feature vectors. Among the individual properties examined secondary structural state frequencies of amino acids gave an overall accuracy of 65.2% for fold discrimination, which is better than the accuracy by any method reported so far in the literature. Combination of secondary structural state frequencies with solvent accessibility state frequencies of amino acids and amino acid pairs further improved the fold discrimination accuracy to more than 70%, which is approximately 8% higher than the best available method. In this study we have also tested, for the first time, an all-together multi-class method known as Crammer and Singer method for protein fold classification. Our studies reveal that the three multi-class classification methods, namely one versus all, one versus one and Crammer and Singer method, yield similar predictions. Dataset and stand-alone program are available upon request.

A tool for developing an automatic insect identification system based on wing outlines

PubMed Central

Yang, He-Ping; Ma, Chun-Sen; Wen, Hui; Zhan, Qing-Bin; Wang, Xin-Li

2015-01-01

For some insect groups, wing outline is an important character for species identification. We have constructed a program as the integral part of an automated system to identify insects based on wing outlines (DAIIS). This program includes two main functions: (1) outline digitization and Elliptic Fourier transformation and (2) classifier model training by pattern recognition of support vector machines and model validation. To demonstrate the utility of this program, a sample of 120 owlflies (Neuroptera: Ascalaphidae) was split into training and validation sets. After training, the sample was sorted into seven species using this tool. In five repeated experiments, the mean accuracy for identification of each species ranged from 90% to 98%. The accuracy increased to 99% when the samples were first divided into two groups based on features of their compound eyes. DAIIS can therefore be a useful tool for developing a system of automated insect identification. PMID:26251292

The new program OPAL for molecular dynamics simulations and energy refinements of biological macromolecules.

PubMed

Luginbühl, P; Güntert, P; Billeter, M; Wüthrich, K

1996-09-01

A new program for molecular dynamics (MD) simulation and energy refinement of biological macromolecules, OPAL, is introduced. Combined with the supporting program TRAJEC for the analysis of MD trajectories, OPAL affords high efficiency and flexibility for work with different force fields, and offers a user-friendly interface and extensive trajectory analysis capabilities. Salient features are computational speeds of up to 1.5 GFlops on vector supercomputers such as the NEC SX-3, ellipsoidal boundaries to reduce the system size for studies in explicit solvents, and natural treatment of the hydrostatic pressure. Practical applications of OPAL are illustrated with MD simulations of pure water, energy minimization of the NMR structure of the mixed disulfide of a mutant E. coli glutaredoxin with glutathione in different solvent models, and MD simulations of a small protein, pheromone Er-2, using either instantaneous or time-averaged NMR restraints, or no restraints.

The Vector Space as a Unifying Concept in School Mathematics.

ERIC Educational Resources Information Center

Riggle, Timothy Andrew

The purpose of this study was to show how the concept of vector space can serve as a unifying thread for mathematics programs--elementary school to pre-calculus college level mathematics. Indicated are a number of opportunities to demonstrate how emphasis upon the vector space structure can enhance the organization of the mathematics curriculum.…

Estimating normal mixture parameters from the distribution of a reduced feature vector

NASA Technical Reports Server (NTRS)

Guseman, L. F.; Peters, B. C., Jr.; Swasdee, M.

1976-01-01

A FORTRAN computer program was written and tested. The measurements consisted of 1000 randomly chosen vectors representing 1, 2, 3, 7, and 10 subclasses in equal portions. In the first experiment, the vectors are computed from the input means and covariances. In the second experiment, the vectors are 16 channel measurements. The starting covariances were constructed as if there were no correlation between separate passes. The biases obtained from each run are listed.

Bayesian data assimilation provides rapid decision support for vector-borne diseases.

PubMed

Jewell, Chris P; Brown, Richard G

2015-07-06

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host-vector-pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Support Vector Machines: Relevance Feedback and Information Retrieval.

ERIC Educational Resources Information Center

Drucker, Harris; Shahrary, Behzad; Gibbon, David C.

2002-01-01

Compares support vector machines (SVMs) to Rocchio, Ide regular and Ide dec-hi algorithms in information retrieval (IR) of text documents using relevancy feedback. If the preliminary search is so poor that one has to search through many documents to find at least one relevant document, then SVM is preferred. Includes nine tables. (Contains 24…

Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression

Treesearch

Jeffrey T. Walton

2008-01-01

Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM+ imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (...

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

PubMed

Huang, Shujun; Cai, Nianguang; Pacheco, Pedro Penzuti; Narrandes, Shavira; Wang, Yang; Xu, Wayne

2018-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Dual linear structured support vector machine tracking method via scale correlation filter

NASA Astrophysics Data System (ADS)

Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

2018-01-01

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

Object recognition of ladar with support vector machine

NASA Astrophysics Data System (ADS)

Sun, Jian-Feng; Li, Qi; Wang, Qi

2005-01-01

Intensity, range and Doppler images can be obtained by using laser radar. Laser radar can detect much more object information than other detecting sensor, such as passive infrared imaging and synthetic aperture radar (SAR), so it is well suited as the sensor of object recognition. Traditional method of laser radar object recognition is extracting target features, which can be influenced by noise. In this paper, a laser radar recognition method-Support Vector Machine is introduced. Support Vector Machine (SVM) is a new hotspot of recognition research after neural network. It has well performance on digital written and face recognition. Two series experiments about SVM designed for preprocessing and non-preprocessing samples are performed by real laser radar images, and the experiments results are compared.

nu-Anomica: A Fast Support Vector Based Novelty Detection Technique

NASA Technical Reports Server (NTRS)

Das, Santanu; Bhaduri, Kanishka; Oza, Nikunj C.; Srivastava, Ashok N.

2009-01-01

In this paper we propose nu-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector Machines algorithm. In -Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision plane using fewer training points and without losing much of the generalization performance of the classical approach. We have tested the proposed algorithm on a variety of continuous data sets under different conditions. We show that under all test conditions the developed procedure closely preserves the accuracy of standard one-class Support Vector Machines while reducing both the training time and the test time by 5 - 20 times.

ℓ p-Norm Multikernel Learning Approach for Stock Market Price Forecasting

PubMed Central

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ 1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ p-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ 1-norm multiple support vector regression model. PMID:23365561

Support vector machine for automatic pain recognition

NASA Astrophysics Data System (ADS)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Design of 2D time-varying vector fields.

PubMed

Chen, Guoning; Kwatra, Vivek; Wei, Li-Yi; Hansen, Charles D; Zhang, Eugene

2012-10-01

Design of time-varying vector fields, i.e., vector fields that can change over time, has a wide variety of important applications in computer graphics. Existing vector field design techniques do not address time-varying vector fields. In this paper, we present a framework for the design of time-varying vector fields, both for planar domains as well as manifold surfaces. Our system supports the creation and modification of various time-varying vector fields with desired spatial and temporal characteristics through several design metaphors, including streamlines, pathlines, singularity paths, and bifurcations. These design metaphors are integrated into an element-based design to generate the time-varying vector fields via a sequence of basis field summations or spatial constrained optimizations at the sampled times. The key-frame design and field deformation are also introduced to support other user design scenarios. Accordingly, a spatial-temporal constrained optimization and the time-varying transformation are employed to generate the desired fields for these two design scenarios, respectively. We apply the time-varying vector fields generated using our design system to a number of important computer graphics applications that require controllable dynamic effects, such as evolving surface appearance, dynamic scene design, steerable crowd movement, and painterly animation. Many of these are difficult or impossible to achieve via prior simulation-based methods. In these applications, the time-varying vector fields have been applied as either orientation fields or advection fields to control the instantaneous appearance or evolving trajectories of the dynamic effects.

Techniques utilized in the simulated altitude testing of a 2D-CD vectoring and reversing nozzle

NASA Technical Reports Server (NTRS)

Block, H. Bruce; Bryant, Lively; Dicus, John H.; Moore, Allan S.; Burns, Maureen E.; Solomon, Robert F.; Sheer, Irving

1988-01-01

Simulated altitude testing of a two-dimensional, convergent-divergent, thrust vectoring and reversing exhaust nozzle was accomplished. An important objective of this test was to develop test hardware and techniques to properly operate a vectoring and reversing nozzle within the confines of an altitude test facility. This report presents detailed information on the major test support systems utilized, the operational performance of the systems and the problems encountered, and test equipment improvements recommended for future tests. The most challenging support systems included the multi-axis thrust measurement system, vectored and reverse exhaust gas collection systems, and infrared temperature measurement systems used to evaluate and monitor the nozzle. The feasibility of testing a vectoring and reversing nozzle of this type in an altitude chamber was successfully demonstrated. Supporting systems performed as required. During reverser operation, engine exhaust gases were successfully captured and turned downstream. However, a small amount of exhaust gas spilled out the collector ducts' inlet openings when the reverser was opened more than 60 percent. The spillage did not affect engine or nozzle performance. The three infrared systems which viewed the nozzle through the exhaust collection system worked remarkably well considering the harsh environment.

Adenovirus Vectors Target Several Cell Subtypes of Mammalian Inner Ear In Vivo

PubMed Central

Li, Wenyan; Shen, Jun

2016-01-01

Mammalian inner ear harbors diverse cell types that are essential for hearing and balance. Adenovirus is one of the major vectors to deliver genes into the inner ear for functional studies and hair cell regeneration. To identify adenovirus vectors that target specific cell subtypes in the inner ear, we studied three adenovirus vectors, carrying a reporter gene encoding green fluorescent protein (GFP) from two vendors or with a genome editing gene Cre recombinase (Cre), by injection into postnatal days 0 (P0) and 4 (P4) mouse cochlea through scala media by cochleostomy in vivo. We found three adenovirus vectors transduced mouse inner ear cells with different specificities and expression levels, depending on the type of adenoviral vectors and the age of mice. The most frequently targeted region was the cochlear sensory epithelium, including auditory hair cells and supporting cells. Adenovirus with GFP transduced utricular supporting cells as well. This study shows that adenovirus vectors are capable of efficiently and specifically transducing different cell types in the mammalian inner ear and provides useful tools to study inner ear gene function and to evaluate gene therapy to treat hearing loss and vestibular dysfunction. PMID:28116172

Bayesian data assimilation provides rapid decision support for vector-borne diseases

PubMed Central

Jewell, Chris P.; Brown, Richard G.

2015-01-01

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. PMID:26136225

Stable Local Volatility Calibration Using Kernel Splines

NASA Astrophysics Data System (ADS)

Coleman, Thomas F.; Li, Yuying; Wang, Cheng

2010-09-01

We propose an optimization formulation using L1 norm to ensure accuracy and stability in calibrating a local volatility function for option pricing. Using a regularization parameter, the proposed objective function balances the calibration accuracy with the model complexity. Motivated by the support vector machine learning, the unknown local volatility function is represented by a kernel function generating splines and the model complexity is controlled by minimizing the 1-norm of the kernel coefficient vector. In the context of the support vector regression for function estimation based on a finite set of observations, this corresponds to minimizing the number of support vectors for predictability. We illustrate the ability of the proposed approach to reconstruct the local volatility function in a synthetic market. In addition, based on S&P 500 market index option data, we demonstrate that the calibrated local volatility surface is simple and resembles the observed implied volatility surface in shape. Stability is illustrated by calibrating local volatility functions using market option data from different dates.

Use of Mapping and Spatial and Space-Time Modeling Approaches in Operational Control of Aedes aegypti and Dengue

PubMed Central

Eisen, Lars; Lozano-Fuentes, Saul

2009-01-01

The aims of this review paper are to 1) provide an overview of how mapping and spatial and space-time modeling approaches have been used to date to visualize and analyze mosquito vector and epidemiologic data for dengue; and 2) discuss the potential for these approaches to be included as routine activities in operational vector and dengue control programs. Geographical information system (GIS) software are becoming more user-friendly and now are complemented by free mapping software that provide access to satellite imagery and basic feature-making tools and have the capacity to generate static maps as well as dynamic time-series maps. Our challenge is now to move beyond the research arena by transferring mapping and GIS technologies and spatial statistical analysis techniques in user-friendly packages to operational vector and dengue control programs. This will enable control programs to, for example, generate risk maps for exposure to dengue virus, develop Priority Area Classifications for vector control, and explore socioeconomic associations with dengue risk. PMID:19399163

Progress and challenges in viral vector manufacturing

PubMed Central

van der Loo, Johannes C.M.; Wright, J. Fraser

2016-01-01

Promising results in several clinical studies have emphasized the potential of gene therapy to address important medical needs and initiated a surge of investments in drug development and commercialization. This enthusiasm is driven by positive data in clinical trials including gene replacement for Hemophilia B, X-linked Severe Combined Immunodeficiency, Leber's Congenital Amaurosis Type 2 and in cancer immunotherapy trials for hematological malignancies using chimeric antigen receptor T cells. These results build on the recent licensure of the European gene therapy product Glybera for the treatment of lipoprotein lipase deficiency. The progress from clinical development towards product licensure of several programs presents challenges to gene therapy product manufacturing. These include challenges in viral vector-manufacturing capacity, where an estimated 1–2 orders of magnitude increase will likely be needed to support eventual commercial supply requirements for many of the promising disease indications. In addition, the expanding potential commercial product pipeline and the continuously advancing development of recombinant viral vectors for gene therapy require that products are well characterized and consistently manufactured to rigorous tolerances of purity, potency and safety. Finally, there is an increase in regulatory scrutiny that affects manufacturers of investigational drugs for early-phase clinical trials engaged in industry partnerships. Along with the recent increase in biopharmaceutical funding in gene therapy, industry partners are requiring their academic counterparts to meet higher levels of GMP compliance at earlier stages of clinical development. This chapter provides a brief overview of current progress in the field and discusses challenges in vector manufacturing. PMID:26519140

Three-dimensional vector modeling and restoration of flat finite wave tank radiometric measurements

NASA Technical Reports Server (NTRS)

Truman, W. M.; Balanis, C. A.

1977-01-01

The three-dimensional vector interaction between a microwave radiometer and a wave tank was modeled. Computer programs for predicting the response of the radiometer to the brightness temperature characteristics of the surroundings were developed along with a computer program that can invert (restore) the radiometer measurements. It is shown that the computer programs can be used to simulate the viewing of large bodies of water, and is applicable to radiometer measurements received from satellites monitoring the ocean. The water temperature, salinity, and wind speed can be determined.

Global Status of DDT and Its Alternatives for Use in Vector Control to Prevent Disease

PubMed Central

van den Berg, Henk

2009-01-01

Objective I review the status of dichlorodiphenyltrichloroethane (DDT), used for disease vector control, along with current evidence on its benefits and risks in relation to the available alternatives. Data sources and extraction Contemporary data on DDT use were largely obtained from questionnaires and reports. I also conducted a Scopus search to retrieve published articles. Data synthesis DDT has been recommended as part of the arsenal of insecticides available for indoor residual spraying until suitable alternatives are available. Approximately 14 countries use DDT for disease control, and several countries are preparing to reintroduce DDT. The effectiveness of DDT depends on local settings and merits close consideration in relation to the alternatives. Concerns about the continued use of DDT are fueled by recent reports of high levels of human exposure associated with indoor spraying amid accumulating evidence on chronic health effects. There are signs that more malaria vectors are becoming resistant to the toxic action of DDT, and that resistance is spreading to new countries. A comprehensive cost assessment of DDT versus its alternatives that takes side effects into account is missing. Effective chemical methods are available as immediate alternatives to DDT, but the choice of insecticide class is limited, and in certain areas the development of resistance is undermining the efficacy of insecticidal tools. New insecticides are not expected in the short term. Nonchemical methods are potentially important, but their effectiveness at program level needs urgent study. Conclusions To reduce reliance on DDT, support is needed for integrated and multipartner strategies of vector control and for the continued development of new technologies. Integrated vector management provides a framework for developing and implementing effective technologies and strategies as sustainable alternatives to reliance on DDT. PMID:20049114

Slicken 1.0: Program for calculating the orientation of shear on reactivated faults

NASA Astrophysics Data System (ADS)

Xu, Hong; Xu, Shunshan; Nieto-Samaniego, Ángel F.; Alaniz-Álvarez, Susana A.

2017-07-01

The slip vector on a fault is an important parameter in the study of the movement history of a fault and its faulting mechanism. Although there exist many graphical programs to represent the shear stress (or slickenline) orientations on faults, programs to quantitatively calculate the orientation of fault slip based on a given stress field are scarce. In consequence, we develop Slicken 1.0, a software to rapidly calculate the orientation of maximum shear stress on any fault plane. For this direct method of calculating the resolved shear stress on a planar surface, the input data are the unit vector normal to the involved plane, the unit vectors of the three principal stress axes, and the stress ratio. The advantage of this program is that the vertical or horizontal principal stresses are not necessarily required. Due to its nimble design using Java SE 8.0, it runs on most operating systems with the corresponding Java VM. The software program will be practical for geoscience students, geologists and engineers and will help resolve a deficiency in field geology, and structural and engineering geology.

Design of a multiple kernel learning algorithm for LS-SVM by convex programming.

PubMed

Jian, Ling; Xia, Zhonghang; Liang, Xijun; Gao, Chuanhou

2011-06-01

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Quantitative structure-activity relationships studies of CCR5 inhibitors and toxicity of aromatic compounds using gene expression programming.

PubMed

Shi, Weimin; Zhang, Xiaoya; Shen, Qi

2010-01-01

Quantitative structure-activity relationship (QSAR) study of chemokine receptor 5 (CCR5) binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas and toxicity of aromatic compounds have been performed. The gene expression programming (GEP) was used to select variables and produce nonlinear QSAR models simultaneously using the selected variables. In our GEP implementation, a simple and convenient method was proposed to infer the K-expression from the number of arguments of the function in a gene, without building the expression tree. The results were compared to those obtained by artificial neural network (ANN) and support vector machine (SVM). It has been demonstrated that the GEP is a useful tool for QSAR modeling. Copyright 2009 Elsevier Masson SAS. All rights reserved.

Public health applications of remote sensing of vector borne and parasitic diseases

NASA Technical Reports Server (NTRS)

1976-01-01

Results of an investigation of the potential application of remote sensing to various fields of public health are presented. Specific topics discussed include: detection of snail habitats in connection with the epidemiology of schistosomiasis; the detection of certain Anopheles breeding sites, and location of transient human populations, both in connection with malaria eradication programs; and detection of overwintering population sites for the primary screwworm (Cochliomyia americana). Emphasis was placed on the determination of ground truth data on the biological, chemical, and physical characteristics of ground waters which would or would not support the growth of significant populations of mosquitoes.

Titan 3E/Centaur D-1T Systems Summary

NASA Technical Reports Server (NTRS)

1973-01-01

A systems and operational summary of the Titan 3E/Centaur D-1T program is presented which describes vehicle assembly facilities, launch facilities, and management responsibilities, and also provides detailed information on the following separate systems: (1) mechanical systems, including structural components, insulation, propulsion units, reaction control, thrust vector control, hydraulic systems, and pneumatic equipment; (2) astrionics systems, such as instrumentation and telemetry, navigation and guidance, C-Band tracking system, and range safety command system; (3) digital computer unit software; (4) flight control systems; (5) electrical/electronic systems; and (6) ground support equipment, including checkout equipment.

Magnetic Footpoint Velocities: A Combination Of Minimum Energy Fit AndLocal Correlation Tracking

NASA Astrophysics Data System (ADS)

Belur, Ravindra; Longcope, D.

2006-06-01

Many numerical and time dependent MHD simulations of the solar atmosphererequire the underlying velocity fields which should be consistent with theinduction equation. Recently, Longcope (2004) introduced a new techniqueto infer the photospheric velocity field from sequence of vector magnetogramswhich are in agreement with the induction equation. The method, the Minimum Energy Fit (MEF), determines a set of velocities and selects the velocity which is smallest overall flow speed by minimizing an energy functional. The inferred velocity can be further constrained by information aboutthe velocity inferred from other techniques. With this adopted techniquewe would expect that the inferred velocity will be close to the photospheric velocity of magnetic footpoints. Here, we demonstrate that the inferred horizontal velocities from LCT can be used to constrain the MEFvelocities. We also apply this technique to actual vector magnetogramsequences and compare these velocities with velocities from LCT alone.This work is supported by DoD MURI and NSF SHINE programs.

Thermal Transport in Nd-doped CeCoIn5

NASA Astrophysics Data System (ADS)

Kim, Duk Y.; Lin, Shi-Zeng; Weickert, Franziska; Rosa, P. F. S.; Bauer, Eric D.; Ronning, Filip; Thompson, J. D.; Movshovich, Roman

Heavy-fermion superconductor CeCoIn5 shows spin-density-wave (SDW) magnetic order in its superconducting state when a high magnetic field is applied. In this Q-phase, the antiferromagnetic order has a single ordering wave vector, and switches its orientation very sharply as magnetic field is rotated within the ab -plane around the [100] (anti-nodal) direction. This hypersensitivity induces a sharp jump of the thermal conductivity. Recently, the SDW with the same ordering wave vector was observed in Nd-doped CeCoIn5 in zero magnetic field. We have measured the thermal conductivity of 5% Nd-doped CeCoIn5 in the magnetic field rotating within the ab -plane. The anisotropy is significantly smaller in the doped material, and the switching transition is much broader. The superconducting transition near Hc 2 is first order, as for the pure CeCoIn5, which indicates the Pauli limited superconductivity. We gratefully acknowledge the support of the U.S. Department of Energy through the LANL/LDRD Program.

MAGSAT data processing: A report for investigators

NASA Technical Reports Server (NTRS)

Langel, R. A.; Berbert, J.; Jennings, T.; Horner, R. (Principal Investigator)

1981-01-01

The in-flight attitude and vector magnetometer data bias recovery techniques and results are described. The attitude bias recoveries are based on comparisons with a magnetic field model and are thought to be accurate to 20 arcsec. The vector magnetometer bias recoveries are based on comparisons with the scalar magnetometer data and are thought to be accurate to 3 nT or better. The MAGSAT position accuracy goals of 60 m radially and 300 m horizontally were achieved for all but the last 3 weeks of Magsat lifetime. This claim is supported by ephemeris overlap statistics and by comparisons with ephemerides computed with an independent orbit program using data from an independent tracking network. MAGSAT time determination accuracy is estimated at 1 ms. Several errors in prelaunch assumptions regarding data time tags, which escaped detection in prelaunch data tests, and were discovered and corrected postlaunch are described. Data formats and products, especially the Investigator-B tapes, which contain auxiliary parameters in addition to the basic magnetometer and ephemeris data, are described.

Estimation of Teacher Practices Based on Text Transcripts of Teacher Speech Using a Support Vector Machine Algorithm

ERIC Educational Resources Information Center

Araya, Roberto; Plana, Francisco; Dartnell, Pablo; Soto-Andrade, Jorge; Luci, Gina; Salinas, Elena; Araya, Marylen

2012-01-01

Teacher practice is normally assessed by observers who watch classes or videos of classes. Here, we analyse an alternative strategy that uses text transcripts and a support vector machine classifier. For each one of the 710 videos of mathematics classes from the 2005 Chilean National Teacher Assessment Programme, a single 4-minute slice was…

Ada Linear-Algebra Program

NASA Technical Reports Server (NTRS)

Klumpp, A. R.; Lawson, C. L.

1988-01-01

Routines provided for common scalar, vector, matrix, and quaternion operations. Computer program extends Ada programming language to include linear-algebra capabilities similar to HAS/S programming language. Designed for such avionics applications as software for Space Station.

Fast support vector data descriptions for novelty detection.

PubMed

Liu, Yi-Hung; Liu, Yan-Chen; Chen, Yen-Jen

2010-08-01

Support vector data description (SVDD) has become a very attractive kernel method due to its good results in many novelty detection problems. However, the decision function of SVDD is expressed in terms of the kernel expansion, which results in a run-time complexity linear in the number of support vectors. For applications where fast real-time response is needed, how to speed up the decision function is crucial. This paper aims at dealing with the issue of reducing the testing time complexity of SVDD. A method called fast SVDD (F-SVDD) is proposed. Unlike the traditional methods which all try to compress a kernel expansion into one with fewer terms, the proposed F-SVDD directly finds the preimage of a feature vector, and then uses a simple relationship between this feature vector and the SVDD sphere center to re-express the center with a single vector. The decision function of F-SVDD contains only one kernel term, and thus the decision boundary of F-SVDD is only spherical in the original space. Hence, the run-time complexity of the F-SVDD decision function is no longer linear in the support vectors, but is a constant, no matter how large the training set size is. In this paper, we also propose a novel direct preimage-finding method, which is noniterative and involves no free parameters. The unique preimage can be obtained in real time by the proposed direct method without taking trial-and-error. For demonstration, several real-world data sets and a large-scale data set, the extended MIT face data set, are used in experiments. In addition, a practical industry example regarding liquid crystal display micro-defect inspection is also used to compare the applicability of SVDD and our proposed F-SVDD when faced with mass data input. The results are very encouraging.

Support Vector Machine-Based Endmember Extraction

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

Filippi, Anthony M; Archibald, Richard K

Introduced in this paper is the utilization of Support Vector Machines (SVMs) to automatically perform endmember extraction from hyperspectral data. The strengths of SVM are exploited to provide a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions. Once this representation is computed, the number of distributions can be determined without prior knowledge. For each distribution, an optimal transform can be determined that preserves informational content while reducing the data dimensionality, and hence, the computational cost. Finally, endmember extraction for the whole data set is accomplished. Results indicate that this Support Vector Machine-Based Endmembermore » Extraction (SVM-BEE) algorithm has the capability of autonomously determining endmembers from multiple clusters with computational speed and accuracy, while maintaining a robust tolerance to noise.« less

An integrated simulation and optimization approach for managing human health risks of atmospheric pollutants by coal-fired power plants.

PubMed

Dai, C; Cai, X H; Cai, Y P; Guo, H C; Sun, W; Tan, Q; Huang, G H

2014-06-01

This research developed a simulation-aided nonlinear programming model (SNPM). This model incorporated the consideration of pollutant dispersion modeling, and the management of coal blending and the related human health risks within a general modeling framework In SNPM, the simulation effort (i.e., California puff [CALPUFF]) was used to forecast the fate of air pollutants for quantifying the health risk under various conditions, while the optimization studies were to identify the optimal coal blending strategies from a number of alternatives. To solve the model, a surrogate-based indirect search approach was proposed, where the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the function relationships between coal-blending operating conditions and health risks. Through replacing the CALPUFF and the corresponding hazard quotient equation with the surrogates, the computation efficiency could be improved. The developed SNPM was applied to minimize the human health risk associated with air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicated that it could be used for reducing the health risk of the public in the vicinity of the two power plants, identifying desired coal blending strategies for decision makers, and considering a proper balance between coal purchase cost and human health risk. A simulation-aided nonlinear programming model (SNPM) is developed. It integrates the advantages of CALPUFF and nonlinear programming model. To solve the model, a surrogate-based indirect search approach based on the combination of support vector regression and genetic algorithm is proposed. SNPM is applied to reduce the health risk caused by air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicate that it is useful for generating coal blending schemes, reducing the health risk of the public, reflecting the trade-offbetween coal purchase cost and health risk.

The expanding role of military entomologists in stability and counterinsurgency operations.

PubMed

Robert, Leon L; Rankin, Steven E

2011-01-01

Military entomologists function as part of medical civil-military operations and are an essential combat multiplier direction supporting COIN operations. They not only directly support US and coalition military forces by performing their traditional wartime mission of protecting personnel from vector-borne and rodent-borne diseases but also enhance the legitimacy of medical services by the host nation government such as controlling diseases promulgated by food, water, vectors, and rodents. These unique COIN missions demand a new skill set required of military entomologists that are not learned from existing training courses and programs. New training opportunities must be afforded military entomologists to familiarize them with how to interact with and synergize the efforts of host nation assets, other governmental agencies, nongovernmental organizations, and international military partners. Teamwork with previously unfamiliar groups and organizations is an essential component of working in the COIN environment and can present unfamiliar tasks for entomologists. This training should start with initial entry training and be a continual process throughout a military entomologist's career. Current COIN operations require greater tactical and operational flexibility and diverse entomological expertise. The skills required for today's full spectrum medical operations are different from those of the past. Counterinsurgency medical operations demand greater agility, rapid task-switching, and the ability to adequately address unfamiliar situations and challenges.

Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine.

PubMed

Li, Hang; Wang, Maolin; Gong, Ya-Nan; Yan, Aixia

2016-01-01

β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in Alzheimer`s disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six classification models to discriminate active and weakly active inhibitors using Kohonen's Self-Organizing Map (SOM) method and Support Vector Machine (SVM) method. Each molecular descriptor was calculated using the program ADRIANA.Code. We adopted two different methods: random method and Self-Organizing Map method, for training/test set split. The descriptors were selected by F-score and stepwise linear regression analysis. The best SVM model Model2C has a good prediction performance on test set with prediction accuracy, sensitivity (SE), specificity (SP) and Matthews correlation coefficient (MCC) of 89.02%, 90%, 88%, 0.78, respectively. Model 1A is the best SOM model, whose accuracy and MCC of the test set were 94.57% and 0.98, respectively. The lone pair electronegativity and polarizability related descriptors importantly contributed to bioactivity of BACE1 inhibitor. The Extended-Connectivity Finger-Prints_4 (ECFP_4) analysis found some vitally key substructural features, which could be helpful for further drug design research. The SOM and SVM models built in this study can be obtained from the authors by email or other contacts.

Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions.

PubMed

Aksu, Yaman; Miller, David J; Kesidis, George; Yang, Qing X

2010-05-01

Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom--the hyperplane's intercept and its squared 2-norm--with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.

Humoral Immunity to Primary Smallpox Vaccination: Impact of Childhood versus Adult Immunization on Vaccinia Vector Vaccine Development in Military Populations.

PubMed

Slike, Bonnie M; Creegan, Matthew; Marovich, Mary; Ngauy, Viseth

2017-01-01

Modified Vaccinia virus has been shown to be a safe and immunogenic vector platform for delivery of HIV vaccines. Use of this vector is of particular importance to the military, with the implementation of a large scale smallpox vaccination campaign in 2002 in active duty and key civilian personnel in response to potential bioterrorist activities. Humoral immunity to smallpox vaccination was previously shown to be long lasting (up to 75 years) and protective. However, using vaccinia-vectored vaccine delivery for other diseases on a background of anti-vector antibodies (i.e. pre-existing immunity) may limit their use as a vaccine platform, especially in the military. In this pilot study, we examined the durability of vaccinia antibody responses in adult primary vaccinees in a healthy military population using a standard ELISA assay and a novel dendritic cell neutralization assay. We found binding and neutralizing antibody (NAb) responses to vaccinia waned after 5-10 years in a group of 475 active duty military, born after 1972, who were vaccinated as adults with Dryvax®. These responses decreased from a geometric mean titer (GMT) of 250 to baseline (<20) after 10-20 years post vaccination. This contrasted with a comparator group of adults, ages 35-49, who were vaccinated with Dryvax® as children. In the childhood vaccinees, titers persisted for >30 years with a GMT of 210 (range 112-3234). This data suggests limited durability of antibody responses in adult vaccinees compared to those vaccinated in childhood and further that adult vaccinia recipients may benefit similarly from receipt of a vaccinia based vaccine as those who are vaccinia naïve. Our findings may have implications for the smallpox vaccination schedule and support the ongoing development of this promising viral vector in a military vaccination program.

Characterization of the spatial and temporal dynamics of the dengue vector population established in urban areas of Fernando de Noronha, a Brazilian oceanic island.

PubMed

Regis, Lêda N; Acioli, Ridelane Veiga; Silveira, José Constantino; de Melo-Santos, Maria Alice Varjal; da Cunha, Mércia Cristiane Santana; Souza, Fátima; Batista, Carlos Alberto Vieira; Barbosa, Rosângela Maria Rodrigues; de Oliveira, Cláudia Maria Fontes; Ayres, Constância Flávia Junqueira; Monteiro, Antonio Miguel Vieira; Souza, Wayner Vieira

2014-09-01

Aedes aegypti has played a major role in the dramatic expansion of dengue worldwide. The failure of control programs in reducing the rhythm of global dengue expansion through vector control suggests the need for studies to support more appropriated control strategies. We report here the results of a longitudinal study on Ae. aegypti population dynamics through continuous egg sampling aiming to characterize the infestation of urban areas of a Brazilian oceanic island, Fernando de Noronha. The spatial and temporal distribution of the dengue vector population in urban areas of the island was described using a monitoring system (SMCP-Aedes) based on a 103-trap network for Aedes egg sampling, using GIS and spatial statistics analysis tools. Mean egg densities were estimated over a 29-month period starting in 2011 and producing monthly maps of mosquito abundance. The system detected continuous Ae. aegypti oviposition in most traps. The high global positive ovitrap index (POI=83.7% of 2815 events) indicated the frequent presence of blood-fed-egg laying females at every sampling station. Egg density (eggs/ovitrap/month) reached peak values of 297.3 (0 - 2020) in May and 295 (0 - 2140) in August 2012. The presence of a stable Ae. aegypti population established throughout the inhabited areas of the island was demonstrated. A strong association between egg abundance and rainfall with a 2-month lag was observed, which combined with a first-order autocorrelation observed in the series of egg counts can provide an important forecasting tool. This first description of the characteristics of the island infestation by the dengue vector provides baseline information to analyze relationships between the spatial distribution of the vector and dengue cases, and to the development of integrated vector control strategies. Copyright © 2014 Elsevier B.V. All rights reserved.

Microsatellites Reveal a High Population Structure in Triatoma infestans from Chuquisaca, Bolivia

PubMed Central

Pizarro, Juan Carlos; Gilligan, Lauren M.; Stevens, Lori

2008-01-01

Background For Chagas disease, the most serious infectious disease in the Americas, effective disease control depends on elimination of vectors through spraying with insecticides. Molecular genetic research can help vector control programs by identifying and characterizing vector populations and then developing effective intervention strategies. Methods and Findings The population genetic structure of Triatoma infestans (Hemiptera: Reduviidae), the main vector of Chagas disease in Bolivia, was investigated using a hierarchical sampling strategy. A total of 230 adults and nymphs from 23 localities throughout the department of Chuquisaca in Southern Bolivia were analyzed at ten microsatellite loci. Population structure, estimated using analysis of molecular variance (AMOVA) to estimate FST (infinite alleles model) and RST (stepwise mutation model), was significant between western and eastern regions within Chuquisaca and between insects collected in domestic and peri-domestic habitats. Genetic differentiation at three different hierarchical geographic levels was significant, even in the case of adjacent households within a single locality (R ST = 0.14, F ST = 0.07). On the largest geographic scale, among five communities up to 100 km apart, R ST = 0.12 and F ST = 0.06. Cluster analysis combined with assignment tests identified five clusters within the five communities. Conclusions Some houses are colonized by insects from several genetic clusters after spraying, whereas other households are colonized predominately by insects from a single cluster. Significant population structure, measured by both R ST and F ST, supports the hypothesis of poor dispersal ability and/or reduced migration of T. infestans. The high degree of genetic structure at small geographic scales, inferences from cluster analysis and assignment tests, and demographic data suggest reinfesting vectors are coming from nearby and from recrudescence (hatching of eggs that were laid before insecticide spraying). Suggestions for using these results in vector control strategies are made. PMID:18365033

Humoral Immunity to Primary Smallpox Vaccination: Impact of Childhood versus Adult Immunization on Vaccinia Vector Vaccine Development in Military Populations

PubMed Central

Slike, Bonnie M.; Creegan, Matthew

2017-01-01

Modified Vaccinia virus has been shown to be a safe and immunogenic vector platform for delivery of HIV vaccines. Use of this vector is of particular importance to the military, with the implementation of a large scale smallpox vaccination campaign in 2002 in active duty and key civilian personnel in response to potential bioterrorist activities. Humoral immunity to smallpox vaccination was previously shown to be long lasting (up to 75 years) and protective. However, using vaccinia-vectored vaccine delivery for other diseases on a background of anti-vector antibodies (i.e. pre-existing immunity) may limit their use as a vaccine platform, especially in the military. In this pilot study, we examined the durability of vaccinia antibody responses in adult primary vaccinees in a healthy military population using a standard ELISA assay and a novel dendritic cell neutralization assay. We found binding and neutralizing antibody (NAb) responses to vaccinia waned after 5–10 years in a group of 475 active duty military, born after 1972, who were vaccinated as adults with Dryvax®. These responses decreased from a geometric mean titer (GMT) of 250 to baseline (<20) after 10–20 years post vaccination. This contrasted with a comparator group of adults, ages 35–49, who were vaccinated with Dryvax® as children. In the childhood vaccinees, titers persisted for >30 years with a GMT of 210 (range 112–3234). This data suggests limited durability of antibody responses in adult vaccinees compared to those vaccinated in childhood and further that adult vaccinia recipients may benefit similarly from receipt of a vaccinia based vaccine as those who are vaccinia naïve. Our findings may have implications for the smallpox vaccination schedule and support the ongoing development of this promising viral vector in a military vaccination program. PMID:28046039

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

Constraining primordial vector mode from B-mode polarization

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

Saga, Shohei; Ichiki, Kiyotomo; Shiraishi, Maresuke, E-mail: saga.shohei@nagoya-u.jp, E-mail: maresuke.shiraishi@pd.infn.it, E-mail: ichiki@a.phys.nagoya-u.ac.jp

The B-mode polarization spectrum of the Cosmic Microwave Background (CMB) may be the smoking gun of not only the primordial tensor mode but also of the primordial vector mode. If there exist nonzero vector-mode metric perturbations in the early Universe, they are known to be supported by anisotropic stress fluctuations of free-streaming particles such as neutrinos, and to create characteristic signatures on both the CMB temperature, E-mode, and B-mode polarization anisotropies. We place constraints on the properties of the primordial vector mode characterized by the vector-to-scalar ratio r{sub v} and the spectral index n{sub v} of the vector-shear power spectrum,more » from the Planck and BICEP2 B-mode data. We find that, for scale-invariant initial spectra, the ΛCDM model including the vector mode fits the data better than the model including the tensor mode. The difference in χ{sup 2} between the vector and tensor models is Δχ{sup 2} = 3.294, because, on large scales the vector mode generates smaller temperature fluctuations than the tensor mode, which is preferred for the data. In contrast, the tensor mode can fit the data set equally well if we allow a significantly blue-tilted spectrum. We find that the best-fitting tensor mode has a large blue tilt and leads to an indistinct reionization bump on larger angular scales. The slightly red-tilted vector mode supported by the current data set can also create O(10{sup -22})-Gauss magnetic fields at cosmological recombination. Our constraints should motivate research that considers models of the early Universe that involve the vector mode.« less

Automatic differentiation for design sensitivity analysis of structural systems using multiple processors

NASA Technical Reports Server (NTRS)

Nguyen, Duc T.; Storaasli, Olaf O.; Qin, Jiangning; Qamar, Ramzi

1994-01-01

An automatic differentiation tool (ADIFOR) is incorporated into a finite element based structural analysis program for shape and non-shape design sensitivity analysis of structural systems. The entire analysis and sensitivity procedures are parallelized and vectorized for high performance computation. Small scale examples to verify the accuracy of the proposed program and a medium scale example to demonstrate the parallel vector performance on multiple CRAY C90 processors are included.

Explicit time integration of finite element models on a vectorized, concurrent computer with shared memory

NASA Technical Reports Server (NTRS)

Gilbertsen, Noreen D.; Belytschko, Ted

1990-01-01

The implementation of a nonlinear explicit program on a vectorized, concurrent computer with shared memory is described and studied. The conflict between vectorization and concurrency is described and some guidelines are given for optimal block sizes. Several example problems are summarized to illustrate the types of speed-ups which can be achieved by reprogramming as compared to compiler optimization.

Current vector control challenges in the fight against malaria.

PubMed

Benelli, Giovanni; Beier, John C

2017-10-01

The effective and eco-friendly control of Anopheles vectors plays a key role in any malaria management program. Integrated Vector Management (IVM) suggests making use of the full range of vector control tools available. The strategies for IVM require novel technologies to control outdoor transmission of malaria. Despite the wide number of promising control tools tested against mosquitoes, current strategies for malaria vector control used in most African countries are not sufficient to achieve successful malaria control. The majority of National Malaria Control Programs in Africa still rely on indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs). These methods reduce malaria incidence but generally have little impact on malaria prevalence. In addition to outdoor transmission, growing levels of insecticide resistance in targeted vectors threaten the efficacy of LLINs and IRS. Larvicidal treatments can be useful, but are not recommended for rural areas. The research needed to improve the quality and delivery of mosquito vector control should focus on (i) optimization of processes and methods for vector control delivery; (ii) monitoring of vector populations and biting activity with reliable techniques; (iii) the development of effective and eco-friendly tools to reduce the burden or locally eliminate malaria and other mosquito-borne diseases; (iv) the careful evaluation of field suitability and efficacy of new mosquito control tools to prove their epidemiological impact; (v) the continuous monitoring of environmental changes which potentially affect malaria vector populations; (vi) the cooperation among different disciplines, with main emphasis on parasitology, tropical medicine, ecology, entomology, and ecotoxicology. A better understanding of behavioral ecology of malaria vectors is required. Key ecological obstacles that limit the effectiveness of vector control include the variation in mosquito behavior, development of insecticide resistance, presence of behavioral avoidance, high vector biodiversity, competitive and food web interactions, lack of insights on mosquito dispersal and mating behavior, and the impact of environmental changes on mosquito ecological traits. Overall, the trans-disciplinary cooperation among parasitologists and entomologists is crucial to ensure proper evaluation of the epidemiological impact triggered by novel mosquito vector control strategies. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of Vector Competence of Aedes mediovittatus and Aedes aegypti for Dengue Virus: Implications for Dengue Control in the Caribbean

PubMed Central

Poole-Smith, B. Katherine; Hemme, Ryan R.; Delorey, Mark; Felix, Gilberto; Gonzalez, Andrea L.; Amador, Manuel; Hunsperger, Elizabeth A.; Barrera, Roberto

2015-01-01

Background Aedes mediovittatus mosquitoes are found throughout the Greater Antilles in the Caribbean and often share the same larval habitats with Ae. Aegypti, the primary vector for dengue virus (DENV). Implementation of vector control measures to control dengue that specifically target Ae. Aegypti may not control DENV transmission in Puerto Rico (PR). Even if Ae. Aegypti is eliminated or DENV refractory mosquitoes are released, DENV transmission may not cease when other competent mosquito species like Ae. Mediovittatus are present. To compare vector competence of Ae. Mediovittatus and Ae. Aegypti mosquitoes, we studied relative infection and transmission rates for all four DENV serotypes. Methods To compare the vector competence of Ae. Mediovittatus and Ae. Aegypti, mosquitoes were exposed to DENV 1–4 per os at viral titers of 5–6 logs plaque-forming unit (pfu) equivalents. At 14 days post infectious bloodmeal, viral RNA was extracted and tested by qRT-PCR to determine infection and transmission rates. Infection and transmission rates were analyzed with a generalized linear model assuming a binomial distribution. Results Ae. Aegypti had significantly higher DENV-4 infection and transmission rates than Ae. mediovittatus. Conclusions This study determined that Ae. Mediovittatus is a competent DENV vector. Therefore dengue prevention programs in PR and the Caribbean should consider both Ae. Mediovittatus and Ae. Aegypti mosquitoes in their vector control programs. PMID:25658951

Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2010-01-01

Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

A Compendium of Wind Statistics and Models for the NASA Space Shuttle and Other Aerospace Vehicle Programs

NASA Technical Reports Server (NTRS)

Smith, O. E.; Adelfang, S. I.

1998-01-01

The wind profile with all of its variations with respect to altitude has been, is now, and will continue to be important for aerospace vehicle design and operations. Wind profile databases and models are used for the vehicle ascent flight design for structural wind loading, flight control systems, performance analysis, and launch operations. This report presents the evolution of wind statistics and wind models from the empirical scalar wind profile model established for the Saturn Program through the development of the vector wind profile model used for the Space Shuttle design to the variations of this wind modeling concept for the X-33 program. Because wind is a vector quantity, the vector wind models use the rigorous mathematical probability properties of the multivariate normal probability distribution. When the vehicle ascent steering commands (ascent guidance) are wind biased to the wind profile measured on the day-of-launch, ascent structural wind loads are reduced and launch probability is increased. This wind load alleviation technique is recommended in the initial phase of vehicle development. The vehicle must fly through the largest load allowable versus altitude to achieve its mission. The Gumbel extreme value probability distribution is used to obtain the probability of exceeding (or not exceeding) the load allowable. The time conditional probability function is derived from the Gumbel bivariate extreme value distribution. This time conditional function is used for calculation of wind loads persistence increments using 3.5-hour Jimsphere wind pairs. These increments are used to protect the commit-to-launch decision. Other topics presented include the Shuttle Shuttle load-response to smoothed wind profiles, a new gust model, and advancements in wind profile measuring systems. From the lessons learned and knowledge gained from past vehicle programs, the development of future launch vehicles can be accelerated. However, new vehicle programs by their very nature will require specialized support for new databases and analyses for wind, atmospheric parameters (pressure, temperature, and density versus altitude), and weather. It is for this reason that project managers are encouraged to collaborate with natural environment specialists early in the conceptual design phase. Such action will give the lead time necessary to meet the natural environment design and operational requirements, and thus, reduce development costs.

Progressive Classification Using Support Vector Machines

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri; Kocurek, Michael

2009-01-01

An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user can halt this reclassification process at any point, thereby obtaining the best possible result for a given amount of computation time. Alternatively, the results can be displayed as they are generated, providing the user with real-time feedback about the current accuracy of classification.

Automated image segmentation using support vector machines

NASA Astrophysics Data System (ADS)

Powell, Stephanie; Magnotta, Vincent A.; Andreasen, Nancy C.

2007-03-01

Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging. Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen (0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework. Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using 15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was 0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater reliability between manual raters and can be achieved without rater intervention.

Use of Bibliometric Analysis to Assess the Scientific Productivity and Impact of the Global Emerging Infections Surveillance and Response System Program, 2006-2012.

PubMed

Reaves, Erik J; Valle, Ruben; Chandrasekera, Ruvani M; Soto, Giselle; Burke, Ronald L; Cummings, James F; Bausch, Daniel G; Kasper, Matthew R

2017-05-01

Scientific publication in academic literature is a key venue in which the U.S. Department of Defense's Global Emerging Infections Surveillance and Response System (GEIS) program disseminates infectious disease surveillance data. Bibliometric analyses are tools to evaluate scientific productivity and impact of published research, yet are not routinely used for disease surveillance. Our objective was to incorporate bibliometric indicators to measure scientific productivity and impact of GEIS-funded infectious disease surveillance, and assess their utility in the management of the GEIS surveillance program. Metrics on GEIS program scientific publications, project funding, and countries of collaborating institutions from project years 2006 to 2012 were abstracted from annual reports and program databases and organized by the six surveillance priority focus areas: respiratory infections, gastrointestinal infections, febrile and vector-borne infections, antimicrobial resistance, sexually transmitted infections, and capacity building and outbreak response. Scientific productivity was defined as the number of scientific publications in peer-reviewed literature derived from GEIS-funded projects. Impact was defined as the number of citations of a GEIS-funded publication by other peer-reviewed publications, and the Thomson Reuters 2-year journal impact factor. Indicators were retrieved from the Web of Science and Journal Citation Report. To determine the global network of international collaborations between GEIS partners, countries were organized by the locations of collaborating institutions. Between 2006 and 2012, GEIS distributed approximately US $330 million to support 921 total projects. On average, GEIS funded 132 projects (range 96-160) with $47 million (range $43 million-$53 million), annually. The predominant surveillance focus areas were respiratory infections with 317 (34.4%) projects and $225 million, and febrile and vector-borne infections with 274 (29.8%) projects and $45 million. The number of annual respiratory infections-related projects peaked in 2006 and 2009. The number of febrile and vector-borne infections projects increased from 29 projects in 2006 to 58 in 2012. There were 651 articles published in 147 different peer-reviewed journals, with an average Thomson Reuters 2-year journal impact factor of 4.2 (range 0.3-53.5). On average, 93 articles were published per year (range 67-117) with $510,000 per publication. Febrile and vector-borne, respiratory, and gastrointestinal infections had 287, 167, and 73 articles published, respectively. Of the 651 articles published, 585 (89.9%) articles were cited at least once (range 1-1,045). Institutions from 90 countries located in all six World Health Organization regions collaborated with surveillance projects. These findings summarize the GEIS-funded surveillance portfolio between 2006 and 2012, and demonstrate the scientific productivity and impact of the program in each of the six disease surveillance priority focus areas. GEIS might benefit from further financial investment in both the febrile and vector-borne and sexually transmitted infections surveillance priority focus areas and increasing peer-reviewed publications of surveillance data derived from respiratory infections projects. Bibliometric indicators are useful to measure scientific productivity and impact in surveillance systems; and this methodology can be utilized as a management tool to assess future changes to GEIS surveillance priorities. Additional metrics should be developed when peer-reviewed literature is not used to disseminate noteworthy accomplishments. Reprint & Copyright © 2017 Association of Military Surgeons of the U.S.

Ada Namelist Package

NASA Technical Reports Server (NTRS)

Klumpp, Allan R.

1991-01-01

Ada Namelist Package, developed for Ada programming language, enables calling program to read and write FORTRAN-style namelist files. Features are: handling of any combination of types defined by user; ability to read vectors, matrices, and slices of vectors and matrices; handling of mismatches between variables in namelist file and those in programmed list of namelist variables; and ability to avoid searching entire input file for each variable. Principle benefits derived by user: ability to read and write namelist-readable files, ability to detect most file errors in initialization phase, and organization keeping number of instantiated units to few packages rather than to many subprograms.

Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C.

PubMed

Stoean, Ruxandra; Stoean, Catalin; Lupsor, Monica; Stefanescu, Horia; Badea, Radu

2011-01-01

Hepatic fibrosis, the principal pointer to the development of a liver disease within chronic hepatitis C, can be measured through several stages. The correct evaluation of its degree, based on recent different non-invasive procedures, is of current major concern. The latest methodology for assessing it is the Fibroscan and the effect of its employment is impressive. However, the complex interaction between its stiffness indicator and the other biochemical and clinical examinations towards a respective degree of liver fibrosis is hard to be manually discovered. In this respect, the novel, well-performing evolutionary-powered support vector machines are proposed towards an automated learning of the relationship between medical attributes and fibrosis levels. The traditional support vector machines have been an often choice for addressing hepatic fibrosis, while the evolutionary option has been validated on many real-world tasks and proven flexibility and good performance. The evolutionary approach is simple and direct, resulting from the hybridization of the learning component within support vector machines and the optimization engine of evolutionary algorithms. It discovers the optimal coefficients of surfaces that separate instances of distinct classes. Apart from a detached manner of establishing the fibrosis degree for new cases, a resulting formula also offers insight upon the correspondence between the medical factors and the respective outcome. What is more, a feature selection genetic algorithm can be further embedded into the method structure, in order to dynamically concentrate search only on the most relevant attributes. The data set refers 722 patients with chronic hepatitis C infection and 24 indicators. The five possible degrees of fibrosis range from F0 (no fibrosis) to F4 (cirrhosis). Since the standard support vector machines are among the most frequently used methods in recent artificial intelligence studies for hepatic fibrosis staging, the evolutionary method is viewed in comparison to the traditional one. The multifaceted discrimination into all five degrees of fibrosis and the slightly less difficult common separation into solely three related stages are both investigated. The resulting performance proves the superiority over the standard support vector classification and the attained formula is helpful in providing an immediate calculation of the liver stage for new cases, while establishing the presence/absence and comprehending the weight of each medical factor with respect to a certain fibrosis level. The use of the evolutionary technique for fibrosis degree prediction triggers simplicity and offers a direct expression of the influence of dynamically selected indicators on the corresponding stage. Perhaps most importantly, it significantly surpasses the classical support vector machines, which are both widely used and technically sound. All these therefore confirm the promise of the new methodology towards a dependable support within the medical decision-making. Copyright © 2010 Elsevier B.V. All rights reserved.

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

A novel logic-based approach for quantitative toxicology prediction.

PubMed

Amini, Ata; Muggleton, Stephen H; Lodhi, Huma; Sternberg, Michael J E

2007-01-01

There is a pressing need for accurate in silico methods to predict the toxicity of molecules that are being introduced into the environment or are being developed into new pharmaceuticals. Predictive toxicology is in the realm of structure activity relationships (SAR), and many approaches have been used to derive such SAR. Previous work has shown that inductive logic programming (ILP) is a powerful approach that circumvents several major difficulties, such as molecular superposition, faced by some other SAR methods. The ILP approach reasons with chemical substructures within a relational framework and yields chemically understandable rules. Here, we report a general new approach, support vector inductive logic programming (SVILP), which extends the essentially qualitative ILP-based SAR to quantitative modeling. First, ILP is used to learn rules, the predictions of which are then used within a novel kernel to derive a support-vector generalization model. For a highly heterogeneous dataset of 576 molecules with known fathead minnow fish toxicity, the cross-validated correlation coefficients (R2CV) from a chemical descriptor method (CHEM) and SVILP are 0.52 and 0.66, respectively. The ILP, CHEM, and SVILP approaches correctly predict 55, 58, and 73%, respectively, of toxic molecules. In a set of 165 unseen molecules, the R2 values from the commercial software TOPKAT and SVILP are 0.26 and 0.57, respectively. In all calculations, SVILP showed significant improvements in comparison with the other methods. The SVILP approach has a major advantage in that it uses ILP automatically and consistently to derive rules, mostly novel, describing fragments that are toxicity alerts. The SVILP is a general machine-learning approach and has the potential of tackling many problems relevant to chemoinformatics including in silico drug design.

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming.

PubMed

Zhang, Huiling; Huang, Qingsheng; Bei, Zhendong; Wei, Yanjie; Floudas, Christodoulos A

2016-03-01

In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/. © 2016 Wiley Periodicals, Inc.

CASAnova: a multiclass support vector machine model for the classification of human sperm motility patterns.

PubMed

Goodson, Summer G; White, Sarah; Stevans, Alicia M; Bhat, Sanjana; Kao, Chia-Yu; Jaworski, Scott; Marlowe, Tamara R; Kohlmeier, Martin; McMillan, Leonard; Zeisel, Steven H; O'Brien, Deborah A

2017-11-01

The ability to accurately monitor alterations in sperm motility is paramount to understanding multiple genetic and biochemical perturbations impacting normal fertilization. Computer-aided sperm analysis (CASA) of human sperm typically reports motile percentage and kinematic parameters at the population level, and uses kinematic gating methods to identify subpopulations such as progressive or hyperactivated sperm. The goal of this study was to develop an automated method that classifies all patterns of human sperm motility during in vitro capacitation following the removal of seminal plasma. We visually classified CASA tracks of 2817 sperm from 18 individuals and used a support vector machine-based decision tree to compute four hyperplanes that separate five classes based on their kinematic parameters. We then developed a web-based program, CASAnova, which applies these equations sequentially to assign a single classification to each motile sperm. Vigorous sperm are classified as progressive, intermediate, or hyperactivated, and nonvigorous sperm as slow or weakly motile. This program correctly classifies sperm motility into one of five classes with an overall accuracy of 89.9%. Application of CASAnova to capacitating sperm populations showed a shift from predominantly linear patterns of motility at initial time points to more vigorous patterns, including hyperactivated motility, as capacitation proceeds. Both intermediate and hyperactivated motility patterns were largely eliminated when sperm were incubated in noncapacitating medium, demonstrating the sensitivity of this method. The five CASAnova classifications are distinctive and reflect kinetic parameters of washed human sperm, providing an accurate, quantitative, and high-throughput method for monitoring alterations in motility. © The Authors 2017. Published by Oxford University Press on behalf of Society for the Study of Reproduction. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Output-only modal parameter estimator of linear time-varying structural systems based on vector TAR model and least squares support vector machine

NASA Astrophysics Data System (ADS)

Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei

2018-01-01

Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Vista/F-16 Multi-Axis Thrust Vectoring (MATV) control law design and evaluation

NASA Technical Reports Server (NTRS)

Zwerneman, W. D.; Eller, B. G.

1994-01-01

For the Multi-Axis Thrust Vectoring (MATV) program, a new control law was developed using multi-axis thrust vectoring to augment the aircraft's aerodynamic control power to provide maneuverability above the normal F-16 angle of attack limit. The control law architecture was developed using Lockheed Fort Worth's offline and piloted simulation capabilities. The final flight control laws were used in flight test to demonstrate tactical benefits gained by using thrust vectoring in air-to-air combat. Differences between the simulator aerodynamics data base and the actual aircraft aerodynamics led to significantly different lateral-directional flying qualities during the flight test program than those identified during piloted simulation. A 'dial-a-gain' flight test control law update was performed in the middle of the flight test program. This approach allowed for inflight optimization of the aircraft's flying qualities. While this approach is not preferred over updating the simulator aerodynamic data base and then updating the control laws, the final selected gain set did provide adequate lateral-directional flying qualities over the MATV flight envelope. The resulting handling qualities and the departure resistance of the aircraft allowed the 422nd_squadron pilots to focus entirely on evaluating the aircraft's tactical utility.

Assessing Undergraduate Curriculum Through Student Exit Vectors

NASA Astrophysics Data System (ADS)

Keane, C. M.; Gonzales, L.; Martinez, C.

2008-12-01

One aspect of assessing the undergraduate curriculum is recognizing that the exit vector of the student is a metric in the absence of a structured assessment program. Detailed knowledge across all geosciences departments regarding the disposition of their recent baccalaureate recipients has been at best inconsistent, and in the case of about half of geoscience programs, non-existent. However, through examining of multiple datasets, a pattern of disposition of geosciences BS recipients emerges, providing a snapshot of the system- wide response to the system-wide "average" program. This pattern can also be juxtaposed against several frameworks of desired skill sets for recent graduates and the employment sectors likely to hire them. The question remains is can one deduce the effectiveness of the undergraduate program in placing graduates in their next step, whether in graduate school or the workplace. Likewise, with an increasing scrutiny on the "value" of an education, is the resulting economic gain sufficient for the student, such that programs will be viewed as sustainable. A factor in answering this question is the importance of the undergraduate program in the ultimate destination of the professional. Clear pathways exist for "optimal" schools for the production of new faculty and new industry professionals, but is it possible to identify those trends further up the educational pipeline? One major mechanism to examine the undergraduate program effectiveness related to exit vectors is to look at hiring trends witnessed related to markedly different program structures, such as those at universities outside of the United States. Rectifying academic programs between the United States and other national systems is often a challenge, but even given the substantial differences between depth of technical knowledge and breadth of education across these programs, in the end, the sum product is often viewed as roughly comparable. This paper will look at end-of-baccalaureate vectors in several countries, including Australia and South Africa, and how it reflects on the structure of their programs, how the programs align with the country's professional needs, and the ability for the undergraduate geosciences system to provide the key intellectual feedstock for sustaining the geosciences discipline in these countries.

Massively parallel data processing for quantitative total flow imaging with optical coherence microscopy and tomography

NASA Astrophysics Data System (ADS)

Sylwestrzak, Marcin; Szlag, Daniel; Marchand, Paul J.; Kumar, Ashwin S.; Lasser, Theo

2017-08-01

We present an application of massively parallel processing of quantitative flow measurements data acquired using spectral optical coherence microscopy (SOCM). The need for massive signal processing of these particular datasets has been a major hurdle for many applications based on SOCM. In view of this difficulty, we implemented and adapted quantitative total flow estimation algorithms on graphics processing units (GPU) and achieved a 150 fold reduction in processing time when compared to a former CPU implementation. As SOCM constitutes the microscopy counterpart to spectral optical coherence tomography (SOCT), the developed processing procedure can be applied to both imaging modalities. We present the developed DLL library integrated in MATLAB (with an example) and have included the source code for adaptations and future improvements. Catalogue identifier: AFBT_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AFBT_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU GPLv3 No. of lines in distributed program, including test data, etc.: 913552 No. of bytes in distributed program, including test data, etc.: 270876249 Distribution format: tar.gz Programming language: CUDA/C, MATLAB. Computer: Intel x64 CPU, GPU supporting CUDA technology. Operating system: 64-bit Windows 7 Professional. Has the code been vectorized or parallelized?: Yes, CPU code has been vectorized in MATLAB, CUDA code has been parallelized. RAM: Dependent on users parameters, typically between several gigabytes and several tens of gigabytes Classification: 6.5, 18. Nature of problem: Speed up of data processing in optical coherence microscopy Solution method: Utilization of GPU for massively parallel data processing Additional comments: Compiled DLL library with source code and documentation, example of utilization (MATLAB script with raw data) Running time: 1,8 s for one B-scan (150 × faster in comparison to the CPU data processing time)

Support vector machine for the diagnosis of malignant mesothelioma

NASA Astrophysics Data System (ADS)

Ushasukhanya, S.; Nithyakalyani, A.; Sivakumar, V.

2018-04-01

Harmful mesothelioma is an illness in which threatening (malignancy) cells shape in the covering of the trunk or stomach area. Being presented to asbestos can influence the danger of threatening mesothelioma. Signs and side effects of threatening mesothelioma incorporate shortness of breath and agony under the rib confine. Tests that inspect within the trunk and belly are utilized to recognize (find) and analyse harmful mesothelioma. Certain elements influence forecast (shot of recuperation) and treatment choices. In this review, Support vector machine (SVM) classifiers were utilized for Mesothelioma sickness conclusion. SVM output is contrasted by concentrating on Mesothelioma’s sickness and findings by utilizing similar information set. The support vector machine algorithm gives 92.5% precision acquired by means of 3-overlap cross-approval. The Mesothelioma illness dataset were taken from an organization reports from Turkey.

An implementation of support vector machine on sentiment classification of movie reviews

NASA Astrophysics Data System (ADS)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine.

PubMed

Wahba, Maram A; Ashour, Amira S; Napoleon, Sameh A; Abd Elnaby, Mustafa M; Guo, Yanhui

2017-12-01

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

The optional selection of micro-motion feature based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Bo; Ren, Hongmei; Xiao, Zhi-he; Sheng, Jing

2017-11-01

Micro-motion form of target is multiple, different micro-motion forms are apt to be modulated, which makes it difficult for feature extraction and recognition. Aiming at feature extraction of cone-shaped objects with different micro-motion forms, this paper proposes the best selection method of micro-motion feature based on support vector machine. After the time-frequency distribution of radar echoes, comparing the time-frequency spectrum of objects with different micro-motion forms, features are extracted based on the differences between the instantaneous frequency variations of different micro-motions. According to the methods based on SVM (Support Vector Machine) features are extracted, then the best features are acquired. Finally, the result shows the method proposed in this paper is feasible under the test condition of certain signal-to-noise ratio(SNR).

Vaxvec: The first web-based recombinant vaccine vector database and its data analysis

PubMed Central

Deng, Shunzhou; Martin, Carly; Patil, Rasika; Zhu, Felix; Zhao, Bin; Xiang, Zuoshuang; He, Yongqun

2015-01-01

A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development. PMID:26403370

Rotman Lens Sidewall Design and Optimization with Hybrid Hardware/Software Based Programming

DTIC Science & Technology

2015-01-09

conventional MoM and stored in memory. The components of Zfar are computed as needed through a fast matrix vector multiplication ( MVM ), which...V vector. Iterative methods, e.g. BiCGSTAB, are employed for solving the linear equation. The matrix-vector multiplications ( MVMs ), which dominate...most of the computation in the solving phase, consists of calculating near and far MVMs . The far MVM comprises aggregation, translation, and

Resurgent vector-borne diseases as a global health problem.

PubMed Central

Gubler, D. J.

1998-01-01

Vector-borne infectious diseases are emerging or resurging as a result of changes in public health policy, insecticide and drug resistance, shift in emphasis from prevention to emergency response, demographic and societal changes, and genetic changes in pathogens. Effective prevention strategies can reverse this trend. Research on vaccines, environmentally safe insecticides, alternative approaches to vector control, and training programs for health-care workers are needed. PMID:9716967

Insecticide Resistance Management

DTIC Science & Technology

2013-01-01

been a side effect of insect vector control programs since 1914, and insect disease vectors in over 45 countries are resistant to at least one...the CDC and WHO bioassays can be performed on various insects , the remainder of the guide will focus specifically on how to detect resistance in...mosquito vector populations. For a description of how to develop a bioassay for resistance testing in other groups of insects , refer to the following

Malaria Prevention by New Technology: Vectored Delivery of Antibody Genes

DTIC Science & Technology

2017-10-01

AWARD NUMBER: W81XWH-15-1-0401 TITLE: Malaria Prevention by New Technology : Vectored Delivery of Antibody Genes PRINCIPAL INVESTIGATOR: Gary...CONTRACT NUMBER Malaria Prevention by New Technology : Vectored Delivery of Antibody Genes 5b. GRANT NUMBER W81XWH-15-1-0401 5c. PROGRAM ELEMENT...whole animals. Using a specific technology originally applied to expression of HIV antibodies, we demonstrated that mice can be protected from

Identification of Human Semiochemicals Attractive to the Major Vectors of Onchocerciasis

PubMed Central

Young, Ryan M.; Burkett-Cadena, Nathan D.; McGaha, Tommy W.; Rodriguez-Perez, Mario A.; Toé, Laurent D.; Adeleke, Monsuru A.; Sanfo, Moussa; Soungalo, Traore; Katholi, Charles R.; Noblet, Raymond; Fadamiro, Henry; Torres-Estrada, Jose L.; Salinas-Carmona, Mario C.; Baker, Bill; Unnasch, Thomas R.; Cupp, Eddie W.

2015-01-01

Background Entomological indicators are considered key metrics to document the interruption of transmission of Onchocerca volvulus, the etiological agent of human onchocerciasis. Human landing collection is the standard employed for collection of the vectors for this parasite. Recent studies reported the development of traps that have the potential for replacing humans for surveillance of O. volvulus in the vector population. However, the key chemical components of human odor that are attractive to vector black flies have not been identified. Methodology/Principal Findings Human sweat compounds were analyzed using GC-MS analysis and compounds common to three individuals identified. These common compounds, with others previously identified as attractive to other hematophagous arthropods were evaluated for their ability to stimulate and attract the major onchocerciasis vectors in Africa (Simulium damnosum sensu lato) and Latin America (Simulium ochraceum s. l.) using electroantennography and a Y tube binary choice assay. Medium chain length carboxylic acids and aldehydes were neurostimulatory for S. damnosum s.l. while S. ochraceum s.l. was stimulated by short chain aliphatic alcohols and aldehydes. Both species were attracted to ammonium bicarbonate and acetophenone. The compounds were shown to be attractive to the relevant vector species in field studies, when incorporated into a formulation that permitted a continuous release of the compound over time and used in concert with previously developed trap platforms. Conclusions/Significance The identification of compounds attractive to the major vectors of O. volvulus will permit the development of optimized traps. Such traps may replace the use of human vector collectors for monitoring the effectiveness of onchocerciasis elimination programs and could find use as a contributing component in an integrated vector control/drug program aimed at eliminating river blindness in Africa. PMID:25569240

Identification of human semiochemicals attractive to the major vectors of onchocerciasis.

PubMed

Young, Ryan M; Burkett-Cadena, Nathan D; McGaha, Tommy W; Rodriguez-Perez, Mario A; Toé, Laurent D; Adeleke, Monsuru A; Sanfo, Moussa; Soungalo, Traore; Katholi, Charles R; Noblet, Raymond; Fadamiro, Henry; Torres-Estrada, Jose L; Salinas-Carmona, Mario C; Baker, Bill; Unnasch, Thomas R; Cupp, Eddie W

2015-01-01

Entomological indicators are considered key metrics to document the interruption of transmission of Onchocerca volvulus, the etiological agent of human onchocerciasis. Human landing collection is the standard employed for collection of the vectors for this parasite. Recent studies reported the development of traps that have the potential for replacing humans for surveillance of O. volvulus in the vector population. However, the key chemical components of human odor that are attractive to vector black flies have not been identified. Human sweat compounds were analyzed using GC-MS analysis and compounds common to three individuals identified. These common compounds, with others previously identified as attractive to other hematophagous arthropods were evaluated for their ability to stimulate and attract the major onchocerciasis vectors in Africa (Simulium damnosum sensu lato) and Latin America (Simulium ochraceum s. l.) using electroantennography and a Y tube binary choice assay. Medium chain length carboxylic acids and aldehydes were neurostimulatory for S. damnosum s.l. while S. ochraceum s.l. was stimulated by short chain aliphatic alcohols and aldehydes. Both species were attracted to ammonium bicarbonate and acetophenone. The compounds were shown to be attractive to the relevant vector species in field studies, when incorporated into a formulation that permitted a continuous release of the compound over time and used in concert with previously developed trap platforms. The identification of compounds attractive to the major vectors of O. volvulus will permit the development of optimized traps. Such traps may replace the use of human vector collectors for monitoring the effectiveness of onchocerciasis elimination programs and could find use as a contributing component in an integrated vector control/drug program aimed at eliminating river blindness in Africa.

Is Vector Control Sufficient to Limit Pathogen Spread in Vineyards?

PubMed

Daugherty, M P; O'Neill, S; Byrne, F; Zeilinger, A

2015-06-01

Vector control is widely viewed as an integral part of disease management. Yet epidemiological theory suggests that the effectiveness of control programs at limiting pathogen spread depends on a variety of intrinsic and extrinsic aspects of a pathosystem. Moreover, control programs rarely evaluate whether reductions in vector density or activity translate into reduced disease prevalence. In areas of California invaded by the glassy-winged sharpshooter (Homalodisca vitripennis Germar), Pierce's disease management relies heavily on chemical control of this vector, primarily via systemic conventional insecticides (i.e., imidacloprid). But, data are lacking that attribute reduced vector pressure and pathogen spread to sharpshooter control. We surveyed 34 vineyards over successive years to assess the epidemiological value of within-vineyard chemical control. The results showed that imidacloprid reduced vector pressure without clear nontarget effects or secondary pest outbreaks. Effects on disease prevalence were more nuanced. Treatment history over the preceding 5 yr affected disease prevalence, with significantly more diseased vines in untreated compared with regularly or intermittently treated vineyards. Yet, the change in disease prevalence between years was low, with no significant effects of insecticide treatment or vector abundance. Collectively, the results suggest that within-vineyard applications of imidacloprid can reduce pathogen spread, but with benefits that may take multiple seasons to become apparent. The relatively modest effect of vector control on disease prevalence in this system may be attributable in part to the currently low regional sharpshooter population densities stemming from area-wide control, without which the need for within-vineyard vector control would be more pronounced. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

Weighted K-means support vector machine for cancer prediction.

PubMed

Kim, SungHwan

2016-01-01

To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.

PubMed

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates "privacy-insensitive" intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner.

ms2: A molecular simulation tool for thermodynamic properties

NASA Astrophysics Data System (ADS)

Deublein, Stephan; Eckl, Bernhard; Stoll, Jürgen; Lishchuk, Sergey V.; Guevara-Carrion, Gabriela; Glass, Colin W.; Merker, Thorsten; Bernreuther, Martin; Hasse, Hans; Vrabec, Jadran

2011-11-01

This work presents the molecular simulation program ms2 that is designed for the calculation of thermodynamic properties of bulk fluids in equilibrium consisting of small electro-neutral molecules. ms2 features the two main molecular simulation techniques, molecular dynamics (MD) and Monte-Carlo. It supports the calculation of vapor-liquid equilibria of pure fluids and multi-component mixtures described by rigid molecular models on the basis of the grand equilibrium method. Furthermore, it is capable of sampling various classical ensembles and yields numerous thermodynamic properties. To evaluate the chemical potential, Widom's test molecule method and gradual insertion are implemented. Transport properties are determined by equilibrium MD simulations following the Green-Kubo formalism. ms2 is designed to meet the requirements of academia and industry, particularly achieving short response times and straightforward handling. It is written in Fortran90 and optimized for a fast execution on a broad range of computer architectures, spanning from single processor PCs over PC-clusters and vector computers to high-end parallel machines. The standard Message Passing Interface (MPI) is used for parallelization and ms2 is therefore easily portable to different computing platforms. Feature tools facilitate the interaction with the code and the interpretation of input and output files. The accuracy and reliability of ms2 has been shown for a large variety of fluids in preceding work. Program summaryProgram title:ms2 Catalogue identifier: AEJF_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJF_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Special Licence supplied by the authors No. of lines in distributed program, including test data, etc.: 82 794 No. of bytes in distributed program, including test data, etc.: 793 705 Distribution format: tar.gz Programming language: Fortran90 Computer: The simulation tool ms2 is usable on a wide variety of platforms, from single processor machines over PC-clusters and vector computers to vector-parallel architectures. (Tested with Fortran compilers: gfortran, Intel, PathScale, Portland Group and Sun Studio.) Operating system: Unix/Linux, Windows Has the code been vectorized or parallelized?: Yes. Message Passing Interface (MPI) protocol Scalability. Excellent scalability up to 16 processors for molecular dynamics and >512 processors for Monte-Carlo simulations. RAM:ms2 runs on single processors with 512 MB RAM. The memory demand rises with increasing number of processors used per node and increasing number of molecules. Classification: 7.7, 7.9, 12 External routines: Message Passing Interface (MPI) Nature of problem: Calculation of application oriented thermodynamic properties for rigid electro-neutral molecules: vapor-liquid equilibria, thermal and caloric data as well as transport properties of pure fluids and multi-component mixtures. Solution method: Molecular dynamics, Monte-Carlo, various classical ensembles, grand equilibrium method, Green-Kubo formalism. Restrictions: No. The system size is user-defined. Typical problems addressed by ms2 can be solved by simulating systems containing typically 2000 molecules or less. Unusual features: Feature tools are available for creating input files, analyzing simulation results and visualizing molecular trajectories. Additional comments: Sample makefiles for multiple operation platforms are provided. Documentation is provided with the installation package and is available at http://www.ms-2.de. Running time: The running time of ms2 depends on the problem set, the system size and the number of processes used in the simulation. Running four processes on a "Nehalem" processor, simulations calculating VLE data take between two and twelve hours, calculating transport properties between six and 24 hours.

Digital orthoimagery base specification V1.0

USGS Publications Warehouse

Rufe, Philip P.

2014-01-01

The resolution requirement for orthoimagery in support of the The National Map of the U.S. Geological Survey (USGS) is 1 meter. However, as the Office of Management and Budget A-16 designated Federal agency responsible for base orthoimagery, the USGS National Geospatial Program (NGP) has developed this base specification to include higher resolution orthoimagery. Many Federal, State, and local programs use high-resolution orthoimagery for various purposes including critical infrastructure management, vector data updates, land-use analysis, natural resource inventory, and extraction of data. The complex nature of large-area orthoimagery datasets, combined with the broad interest in orthoimagery, which is of consistent quality and spatial accuracy, requires high-resolution orthoimagery to meet or exceed the format and content outlined in this specification. The USGS intends to use this specification primarily to create consistency across all NGP funded and managed orthoimagery collections, in particular, collections in support of the National Digital Orthoimagery Program (NDOP). In the absence of other comprehensive specifications or standards, the USGS intends that this specification will, to the highest degree practical, be adopted by other USGS programs and mission areas, and by other Federal agencies. This base specification, defining minimum parameters for orthoimagery data collection. Local conditions in any given project area, specialized applications for the data, or the preferences of cooperators, may mandate more stringent requirements. The USGS fully supports the acquisition of more detailed, accurate, or value-added data that exceed the base specification outlined herein. A partial list of common “buy-up” options is provided in appendix 1 for those areas and projects that require more stringent or expanded specifications.

The ASC Sequoia Programming Model

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

Seager, M

2008-08-06

In the late 1980's and early 1990's, Lawrence Livermore National Laboratory was deeply engrossed in determining the next generation programming model for the Integrated Design Codes (IDC) beyond vectorization for the Cray 1s series of computers. The vector model, developed in mid 1970's first for the CDC 7600 and later extended from stack based vector operation to memory to memory operations for the Cray 1s, lasted approximately 20 years (See Slide 5). The Cray vector era was deemed an extremely long lived era as it allowed vector codes to be developed over time (the Cray 1s were faster in scalarmore » mode than the CDC 7600) with vector unit utilization increasing incrementally over time. The other attributes of the Cray vector era at LLNL were that we developed, supported and maintained the Operating System (LTSS and later NLTSS), communications protocols (LINCS), Compilers (Civic Fortran77 and Model), operating system tools (e.g., batch system, job control scripting, loaders, debuggers, editors, graphics utilities, you name it) and math and highly machine optimized libraries (e.g., SLATEC, and STACKLIB). Although LTSS was adopted by Cray for early system generations, they later developed COS and UNICOS operating systems and environment on their own. In the late 1970s and early 1980s two trends appeared that made the Cray vector programming model (described above including both the hardware and system software aspects) seem potentially dated and slated for major revision. These trends were the appearance of low cost CMOS microprocessors and their attendant, departmental and mini-computers and later workstations and personal computers. With the wide spread adoption of Unix in the early 1980s, it appeared that LLNL (and the other DOE Labs) would be left out of the mainstream of computing without a rapid transition to these 'Killer Micros' and modern OS and tools environments. The other interesting advance in the period is that systems were being developed with multiple 'cores' in them and called Symmetric Multi-Processor or Shared Memory Processor (SMP) systems. The parallel revolution had begun. The Laboratory started a small 'parallel processing project' in 1983 to study the new technology and its application to scientific computing with four people: Tim Axelrod, Pete Eltgroth, Paul Dubois and Mark Seager. Two years later, Eugene Brooks joined the team. This team focused on Unix and 'killer micro' SMPs. Indeed, Eugene Brooks was credited with coming up with the 'Killer Micro' term. After several generations of SMP platforms (e.g., Sequent Balance 8000 with 8 33MHz MC32032s, Allian FX8 with 8 MC68020 and FPGA based Vector Units and finally the BB&N Butterfly with 128 cores), it became apparent to us that the killer micro revolution would indeed take over Crays and that we definitely needed a new programming and systems model. The model developed by Mark Seager and Dale Nielsen focused on both the system aspects (Slide 3) and the code development aspects (Slide 4). Although now succinctly captured in two attached slides, at the time there was tremendous ferment in the research community as to what parallel programming model would emerge, dominate and survive. In addition, we wanted a model that would provide portability between platforms of a single generation but also longevity over multiple--and hopefully--many generations. Only after we developed the 'Livermore Model' and worked it out in considerable detail did it become obvious that what we came up with was the right approach. In a nutshell, the applications programming model of the Livermore Model posited that SMP parallelism would ultimately not scale indefinitely and one would have to bite the bullet and implement MPI parallelism within the Integrated Design Code (IDC). We also had a major emphasis on doing everything in a completely standards based, portable methodology with POSIX/Unix as the target environment. We decided against specialized libraries like STACKLIB for performance, but kept as many general purpose, portable math libraries as were needed by the codes. Third, we assumed that the SMPs in clusters would evolve in time to become more powerful, feature rich and, in particular, offer more cores. Thus, we focused on OpenMP, and POSIX PThreads for programming SMP parallelism. These code porting efforts were lead by Dale Nielsen, A-Division code group leader, and Randy Christensen, B-Division code group leader. Most of the porting effort revolved removing 'Crayisms' in the codes: artifacts of LTSS/NLTSS, Civic compiler extensions beyond Fortran77, IO libraries and dealing with new code control languages (we switched to Perl and later to Python). Adding MPI to the codes was initially problematic and error prone because the programmers used MPI directly and sprinkled the calls throughout the code.« less

Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.

PubMed

Song, Jiangning; Yuan, Zheng; Tan, Hao; Huber, Thomas; Burrage, Kevin

2007-12-01

Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications. We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects. The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide

Devising novel strategies against vector mosquitoes and house flies

USDA-ARS?s Scientific Manuscript database

In 1932, the United States Department of Agriculture established an entomological research laboratory in Orlando, Florida. The initial focus of the program was on investigations of mosquitoes (including malaria vectors under conditions “simulating those of South Pacific jungles”) and other insects ...

Radiation hardened microprocessor for small payloads

NASA Technical Reports Server (NTRS)

Shah, Ravi

1993-01-01

The RH-3000 program is developing a rad-hard space qualified 32-bit MIPS R-3000 RISC processor under the Naval Research Lab sponsorship. In addition, under IR&D Harris is developing RHC-3000 for embedded control applications where low cost and radiation tolerance are primary concerns. The development program leverages heavily from commercial development of the MIPS R-3000. The commercial R-3000 has a large installed user base and several foundry partners are currently producing a wide variety of R-3000 derivative products. One of the MIPS derivative products, the LR33000 from LSI Logic, was used as the basis for the design of the RH-3000 chipset. The RH-3000 chipset consists of three core chips and two support chips. The core chips include the CPU, which is the R-3000 integer unit and the FPA/MD chip pair, which performs the R-3010 floating point functions. The two support whips contain all the support functions required for fault tolerance support, real-time support, memory management, timers, and other functions. The Harris development effort had first passed silicon success in June, 1992 with the first rad-hard 32-bit RH-3000 CPU chip. The CPU device is 30 kgates, has a 508 mil by 503 mil die size and is fabricated at Harris Semiconductor on the rad-hard CMOS Silicon on Sapphire (SOS) process. The CPU device successfully passed tesing against 600,000 test vectors derived directly on the LSI/MIPS test suite and has been operational as a single board computer running C code for the past year. In addition, the RH-3000 program has developed the methodology for converting commercially developed designs utilizing logic synthesis techniques based on a combination of VHDK and schematic data bases.

T-ray relevant frequencies for osteosarcoma classification

NASA Astrophysics Data System (ADS)

Withayachumnankul, W.; Ferguson, B.; Rainsford, T.; Findlay, D.; Mickan, S. P.; Abbott, D.

2006-01-01

We investigate the classification of the T-ray response of normal human bone cells and human osteosarcoma cells, grown in culture. Given the magnitude and phase responses within a reliable spectral range as features for input vectors, a trained support vector machine can correctly classify the two cell types to some extent. Performance of the support vector machine is deteriorated by the curse of dimensionality, resulting from the comparatively large number of features in the input vectors. Feature subset selection methods are used to select only an optimal number of relevant features for inputs. As a result, an improvement in generalization performance is attainable, and the selected frequencies can be used for further describing different mechanisms of the cells, responding to T-rays. We demonstrate a consistent classification accuracy of 89.6%, while the only one fifth of the original features are retained in the data set.

Recombinase-Mediated Cassette Exchange Using Adenoviral Vectors.

PubMed

Kolb, Andreas F; Knowles, Christopher; Pultinevicius, Patrikas; Harbottle, Jennifer A; Petrie, Linda; Robinson, Claire; Sorrell, David A

2017-01-01

Site-specific recombinases are important tools for the modification of mammalian genomes. In conjunction with viral vectors, they can be utilized to mediate site-specific gene insertions in animals and in cell lines which are difficult to transfect. Here we describe a method for the generation and analysis of an adenovirus vector supporting a recombinase-mediated cassette exchange reaction and discuss the advantages and limitations of this approach.

Magnetic and gravity anomalies in the Americas

NASA Technical Reports Server (NTRS)

Braile, L. W.; Hinze, W. J.; Vonfrese, R. R. B. (Principal Investigator)

1981-01-01

The cleaning and magnetic tape storage of spherical Earth processing programs are reported. These programs include: NVERTSM which inverts total or vector magnetic anomaly data on a distribution of point dipoles in spherical coordinates; SMFLD which utilizes output from NVERTSM to compute total or vector magnetic anomaly fields for a distribution of point dipoles in spherical coordinates; NVERTG; and GFLD. Abstracts are presented for papers dealing with the mapping and modeling of magnetic and gravity anomalies, and with the verification of crustal components in satellite data.

Global Positioning Systems (GPS) Technology to Study Vector-Pathogen-Host Interactions

DTIC Science & Technology

2016-12-01

Award Number: W81XWH-11-2-0175 TITLE: Global Positioning Systems (GPS) Technology to Study Vector-Pathogen-Host Interactions PRINCIPAL...Positioning Systems (GPS) Technology to Study Vector-Pathogen-Host Interactions 5b. GRANT NUMBER W81XWH-11-2-0175 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S...objective of this project is to examine the evolutionary consequences of introducing a tetravalent live- attenuated dengue virus vaccine into children in

Automatic recognition of vector and parallel operations in a higher level language

NASA Technical Reports Server (NTRS)

Schneck, P. B.

1971-01-01

A compiler for recognizing statements of a FORTRAN program which are suited for fast execution on a parallel or pipeline machine such as Illiac-4, Star or ASC is described. The technique employs interval analysis to provide flow information to the vector/parallel recognizer. Where profitable the compiler changes scalar variables to subscripted variables. The output of the compiler is an extension to FORTRAN which shows parallel and vector operations explicitly.

Achieving High Performance on the i860 Microprocessor

NASA Technical Reports Server (NTRS)

Lee, King; Kutler, Paul (Technical Monitor)

1998-01-01

The i860 is a high performance microprocessor used in the Intel Touchstone project. This paper proposes a paradigm for programming the i860 that is modelled on the vector instructions of the Cray computers. Fortran callable assembler subroutines were written that mimic the concurrent vector instructions of the Cray. Cache takes the place of vector registers. Using this paradigm we have achieved twice the performance of compiled code on a traditional solve.

United States Air Force Guide to Operational Surveillance of Medically Important Vectors and Pests Operational Entomology

DTIC Science & Technology

2006-08-15

Programs Section 3. Sampling Equipment Sampling Equipment Solid-State Army Miniature (SSAM) trap ABC style trap Encephalitis Vector Survey Trap CDC...Baseline Survey - these are conducted to determine the types of vectors and pests occurring in the area of operations, their respective breeding sites...or source habitat, and seasonal activity patterns. Operational Survey - data collected in an operational survey are used specifically to aid pest

[Adolpho Lutz's collection of black flies (Diptera - Simuliidae), its history and importance].

PubMed

Ribeiro do Amaral-Calvão, Ana Margarida; Maia-Herzog, Marilza

2003-01-01

This is part of a master's thesis currently being written under the auspices of the Post-Graduate Program in Animal Biology of the Federal University of Rio de Janeiro, with support from CAPES. It presents the species of black flies in Adolpho Lutz's collection, held at the Laboratory of Black Flies and Oncocercosis of the Department of Entomology of the Instituto Oswaldo Cruz. A pioneer in the study of these dipterons, Lutz described about 25 species from different places in Brazil. A vector of round worm, the black fly's importance to public health was recognized at the end of the 1920s.

A Parallel Vector Machine for the PM Programming Language

NASA Astrophysics Data System (ADS)

Bellerby, Tim

2016-04-01

PM is a new programming language which aims to make the writing of computational geoscience models on parallel hardware accessible to scientists who are not themselves expert parallel programmers. It is based around the concept of communicating operators: language constructs that enable variables local to a single invocation of a parallelised loop to be viewed as if they were arrays spanning the entire loop domain. This mechanism enables different loop invocations (which may or may not be executing on different processors) to exchange information in a manner that extends the successful Communicating Sequential Processes idiom from single messages to collective communication. Communicating operators avoid the additional synchronisation mechanisms, such as atomic variables, required when programming using the Partitioned Global Address Space (PGAS) paradigm. Using a single loop invocation as the fundamental unit of concurrency enables PM to uniformly represent different levels of parallelism from vector operations through shared memory systems to distributed grids. This paper describes an implementation of PM based on a vectorised virtual machine. On a single processor node, concurrent operations are implemented using masked vector operations. Virtual machine instructions operate on vectors of values and may be unmasked, masked using a Boolean field, or masked using an array of active vector cell locations. Conditional structures (such as if-then-else or while statement implementations) calculate and apply masks to the operations they control. A shift in mask representation from Boolean to location-list occurs when active locations become sufficiently sparse. Parallel loops unfold data structures (or vectors of data structures for nested loops) into vectors of values that may additionally be distributed over multiple computational nodes and then split into micro-threads compatible with the size of the local cache. Inter-node communication is accomplished using standard OpenMP and MPI. Performance analyses of the PM vector machine, demonstrating its scaling properties with respect to domain size and the number of processor nodes will be presented for a range of hardware configurations. The PM software and language definition are being made available under unrestrictive MIT and Creative Commons Attribution licenses respectively: www.pm-lang.org.

Vaxvec: The first web-based recombinant vaccine vector database and its data analysis.

PubMed

Deng, Shunzhou; Martin, Carly; Patil, Rasika; Zhu, Felix; Zhao, Bin; Xiang, Zuoshuang; He, Yongqun

2015-11-27

A recombinant vector vaccine uses an attenuated virus, bacterium, or parasite as the carrier to express a heterologous antigen(s). Many recombinant vaccine vectors and related vaccines have been developed and extensively investigated. To compare and better understand recombinant vectors and vaccines, we have generated Vaxvec (http://www.violinet.org/vaxvec), the first web-based database that stores various recombinant vaccine vectors and those experimentally verified vaccines that use these vectors. Vaxvec has now included 59 vaccine vectors that have been used in 196 recombinant vector vaccines against 66 pathogens and cancers. These vectors are classified to 41 viral vectors, 15 bacterial vectors, 1 parasitic vector, and 1 fungal vector. The most commonly used viral vaccine vectors are double-stranded DNA viruses, including herpesviruses, adenoviruses, and poxviruses. For example, Vaxvec includes 63 poxvirus-based recombinant vaccines for over 20 pathogens and cancers. Vaxvec collects 30 recombinant vector influenza vaccines that use 17 recombinant vectors and were experimentally tested in 7 animal models. In addition, over 60 protective antigens used in recombinant vector vaccines are annotated and analyzed. User-friendly web-interfaces are available for querying various data in Vaxvec. To support data exchange, the information of vaccine vectors, vaccines, and related information is stored in the Vaccine Ontology (VO). Vaxvec is a timely and vital source of vaccine vector database and facilitates efficient vaccine vector research and development. Copyright © 2015 Elsevier Ltd. All rights reserved.

Knowledge and practices related to dengue and its vector: a community-based study from Southeast Brazil.

PubMed

Alves, Adorama Candido; Fabbro, Amaury Lelis Dal; Passos, Afonso Dinis Costa; Carneiro, Ariadne Fernanda Tesarin Mendes; Jorge, Tatiane Martins; Martinez, Edson Zangiacomi

2016-04-01

This study investigated the knowledge of users of primary healthcare services living in Ribeirão Preto, Brazil, about dengue and its vector. A cross-sectional survey of 605 people was conducted following a major dengue outbreak in 2013. Participants with higher levels of education were more likely to identify correctly the vector of the disease. The results emphasize the relevance of health education programs, the continuous promotion of educational campaigns in the media, the role of the television as a source of information, and the importance of motivating the population to control the vector.

Fuzzy support vector machines for adaptive Morse code recognition.

PubMed

Yang, Cheng-Hong; Jin, Li-Cheng; Chuang, Li-Yeh

2006-11-01

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature.

Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

NASA Astrophysics Data System (ADS)

Wu, Qi

2010-03-01

Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

Evaluation and recognition of skin images with aging by support vector machine

NASA Astrophysics Data System (ADS)

Hu, Liangjun; Wu, Shulian; Li, Hui

2016-10-01

Aging is a very important issue not only in dermatology, but also cosmetic science. Cutaneous aging involves both chronological and photoaging aging process. The evaluation and classification of aging is an important issue with the medical cosmetology workers nowadays. The purpose of this study is to assess chronological-age-related and photo-age-related of human skin. The texture features of skin surface skin, such as coarseness, contrast were analyzed by Fourier transform and Tamura. And the aim of it is to detect the object hidden in the skin texture in difference aging skin. Then, Support vector machine was applied to train the texture feature. The different age's states were distinguished by the support vector machine (SVM) classifier. The results help us to further understand the mechanism of different aging skin from texture feature and help us to distinguish the different aging states.

Scattering transform and LSPTSVM based fault diagnosis of rotating machinery

NASA Astrophysics Data System (ADS)

Ma, Shangjun; Cheng, Bo; Shang, Zhaowei; Liu, Geng

2018-05-01

This paper proposes an algorithm for fault diagnosis of rotating machinery to overcome the shortcomings of classical techniques which are noise sensitive in feature extraction and time consuming for training. Based on the scattering transform and the least squares recursive projection twin support vector machine (LSPTSVM), the method has the advantages of high efficiency and insensitivity for noise signal. Using the energy of the scattering coefficients in each sub-band, the features of the vibration signals are obtained. Then, an LSPTSVM classifier is used for fault diagnosis. The new method is compared with other common methods including the proximal support vector machine, the standard support vector machine and multi-scale theory by using fault data for two systems, a motor bearing and a gear box. The results show that the new method proposed in this study is more effective for fault diagnosis of rotating machinery.

Classification of Stellar Spectra with Fuzzy Minimum Within-Class Support Vector Machine

NASA Astrophysics Data System (ADS)

Zhong-bao, Liu; Wen-ai, Song; Jing, Zhang; Wen-juan, Zhao

2017-06-01

Classification is one of the important tasks in astronomy, especially in spectra analysis. Support Vector Machine (SVM) is a typical classification method, which is widely used in spectra classification. Although it performs well in practice, its classification accuracies can not be greatly improved because of two limitations. One is it does not take the distribution of the classes into consideration. The other is it is sensitive to noise. In order to solve the above problems, inspired by the maximization of the Fisher's Discriminant Analysis (FDA) and the SVM separability constraints, fuzzy minimum within-class support vector machine (FMWSVM) is proposed in this paper. In FMWSVM, the distribution of the classes is reflected by the within-class scatter in FDA and the fuzzy membership function is introduced to decrease the influence of the noise. The comparative experiments with SVM on the SDSS datasets verify the effectiveness of the proposed classifier FMWSVM.

Geographical traceability of Marsdenia tenacissima by Fourier transform infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Li, Chao; Yang, Sheng-Chao; Guo, Qiao-Sheng; Zheng, Kai-Yan; Wang, Ping-Li; Meng, Zhen-Gui

2016-01-01

A combination of Fourier transform infrared spectroscopy with chemometrics tools provided an approach for studying Marsdenia tenacissima according to its geographical origin. A total of 128 M. tenacissima samples from four provinces in China were analyzed with FTIR spectroscopy. Six pattern recognition methods were used to construct the discrimination models: support vector machine-genetic algorithms, support vector machine-particle swarm optimization, K-nearest neighbors, radial basis function neural network, random forest and support vector machine-grid search. Experimental results showed that K-nearest neighbors was superior to other mathematical algorithms after data were preprocessed with wavelet de-noising, with a discrimination rate of 100% in both the training and prediction sets. This study demonstrated that FTIR spectroscopy coupled with K-nearest neighbors could be successfully applied to determine the geographical origins of M. tenacissima samples, thereby providing reliable authentication in a rapid, cheap and noninvasive way.

A Framework for Sentiment Analysis Implementation of Indonesian Language Tweet on Twitter

NASA Astrophysics Data System (ADS)

Asniar; Aditya, B. R.

2017-01-01

Sentiment analysis is the process of understanding, extracting, and processing the textual data automatically to obtain information. Sentiment analysis can be used to see opinion on an issue and identify a response to something. Millions of digital data are still not used to be able to provide any information that has usefulness, especially for government. Sentiment analysis in government is used to monitor the work programs of the government such as the Government of Bandung City through social media data. The analysis can be used quickly as a tool to see the public response to the work programs, so the next strategic steps can be taken. This paper adopts Support Vector Machine as a supervised algorithm for sentiment analysis. It presents a framework for sentiment analysis implementation of Indonesian language tweet on twitter for Work Programs of Government of Bandung City. The results of this paper can be a reference for decision making in local government.

Object recognition of real targets using modelled SAR images

NASA Astrophysics Data System (ADS)

Zherdev, D. A.

2017-12-01

In this work the problem of recognition is studied using SAR images. The algorithm of recognition is based on the computation of conjugation indices with vectors of class. The support subspaces for each class are constructed by exception of the most and the less correlated vectors in a class. In the study we examine the ability of a significant feature vector size reduce that leads to recognition time decrease. The images of targets form the feature vectors that are transformed using pre-trained convolutional neural network (CNN).

iVAR: a program for imputing missing data in multivariate time series using vector autoregressive models.

PubMed

Liu, Siwei; Molenaar, Peter C M

2014-12-01

This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.

Multitasking 3-D forward modeling using high-order finite difference methods on the Cray X-MP/416

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

Terki-Hassaine, O.; Leiss, E.L.

1988-01-01

The CRAY X-MP/416 was used to multitask 3-D forward modeling by the high-order finite difference method. Flowtrace analysis reveals that the most expensive operation in the unitasked program is a matrix vector multiplication. The in-core and out-of-core versions of a reentrant subroutine can perform any fraction of the matrix vector multiplication independently, a pattern compatible with multitasking. The matrix vector multiplication routine can be distributed over two to four processors. The rest of the program utilizes the microtasking feature that lets the system treat independent iterations of DO-loops as subtasks to be performed by any available processor. The availability ofmore » the Solid-State Storage Device (SSD) meant the I/O wait time was virtually zero. A performance study determined a theoretical speedup, taking into account the multitasking overhead. Multitasking programs utilizing both macrotasking and microtasking features obtained actual speedups that were approximately 80% of the ideal speedup.« less

The Performance of the NAS HSPs in 1st Half of 1994

NASA Technical Reports Server (NTRS)

Bergeron, Robert J.; Walter, Howard (Technical Monitor)

1995-01-01

During the first six months of 1994, the NAS (National Airspace System) 16-CPU Y-MP C90 Von Neumann (VN) delivered an average throughput of 4.045 GFLOPS while the ACSF (Aeronautics Consolidated Supercomputer Facility) 8-CPU Y-MP C90 Eagle averaged 1.658 GFLOPS. The VN rate represents a machine efficiency of 26.3% whereas the Eagle rate corresponds to a machine efficiency of 21.6%. VN displayed a greater efficiency than Eagle primarily because the stronger workload demand for its CPU cycles allowed it to devote more time to user programs and less time to idle. An additional factor increasing VN efficiency was the ability of the UNICOS 8.0 Operating System to deliver a larger fraction of CPU time to user programs. Although measurements indicate increasing vector length for both workloads, insufficient vector lengths continue to hinder HSP (High Speed Processor) performance. To improve HSP performance, NAS should continue to encourage the HSP users to modify their codes to increase program vector length.

Current procedures of the integrated urban vector-mosquito control as an example in Cotonou (Benin, West Africa) and Wrocław area (Poland).

PubMed

Rydzanicz, Katarzyna; Lonc, Elzbieta; Becker, Norbert

2009-01-01

Current strategy of Integrated Vector Management (IVM) comprises the general approach of environmentally friendly control measures. With regard to mosquitoes it includes first of all application of microbial insecticides based on Bacillus thuringiensis israelensis (Bti) and B. sphaericus (Bs) delta-endotoxins as well as the reduction of breeding habitats and natural enemy augmentation. It can be achieved thorough implementation of the interdisciplinary program, i. e., understanding of mosquito vector ecology, the appropriate vector-diseases (e. g., malariometric) measurements and training of local personnel responsible for mosquito abatement activities, as well as community involvement. Biocontrol methods as an alternative to chemical insecticides result from the sustainability development concept, growing awareness of environmental pollution and the development of insecticide-resistant strains of vector-mosquito populations in many parts of the world. Although sustainable trends are usually considered in terms of the monetary and training resources within countries, environmental concerns are actually more limiting factors for the duration of an otherwise successful vector control effort. In order to meet these new needs, increasing efforts have been made in search of and application of natural enemies, such as parasites, bacterial pathogens and predators which may control populations of insect vectors. The biological control agent based on the bacterial toxins Bti and Bs has been used in the Wrocław's University and Municipal Mosquito Control Programs since 1998. In West-Africa biocontrol appears to be an effective and safe tool to combat malaria in addition to bed-nets, residual indoor spraying and appropriate diagnosis and treatment of malaria parasites which are the major tools in the WHO Roll Back Malaria Program. IVM studies carried out 2005-2008 in Cotonou (Benin) as well those in Wrocław Irrigated Fields during the last years include the following major steps: 1. Mapping of all breeding sites in the project area and recording data in a geographical information system (GIS/relational database). All districts, streets and houses are numbered for quick reference during the operation; 2. Studying mosquito vector bionomics, migration and vectorial capacity in the project area, before, during and after the routine Bti treatments; 3. Assessment of the optimum for effective larvicide insecticide dosages at major breeding sites against the different target mosquito species; 4. Implementation of the microbial control agents in the integrated routine program. Adaptation of the application equipment to the local situation, training of the field staff, and routine treatments; 5. Conducting surveillance of vector-disease (e. g., malariometric) parameters in the control and experimental area before, during, and after the application of biocontrol agents.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Sparse kernel methods for high-dimensional survival data.

PubMed

Evers, Ludger; Messow, Claudia-Martina

2008-07-15

Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Cox's proportional hazards model. As the partial likelihood of the proportional hazards model only depends on the covariates through inner products, it can be 'kernelized'. The kernelized proportional hazards model however yields a solution that is dense, i.e. the solution depends on all observations. One of the key features of an SVM is that it yields a sparse solution, depending only on a small fraction of the training data. We propose two methods. One is based on a geometric idea, where-akin to support vector classification-the margin between the failed observation and the observations currently at risk is maximised. The other approach is based on obtaining a sparse model by adding observations one after another akin to the Import Vector Machine (IVM). Data examples studied suggest that both methods can outperform competing approaches. Software is available under the GNU Public License as an R package and can be obtained from the first author's website http://www.maths.bris.ac.uk/~maxle/software.html.

Intelligent Design of Metal Oxide Gas Sensor Arrays Using Reciprocal Kernel Support Vector Regression

NASA Astrophysics Data System (ADS)

Dougherty, Andrew W.

Metal oxides are a staple of the sensor industry. The combination of their sensitivity to a number of gases, and the electrical nature of their sensing mechanism, make the particularly attractive in solid state devices. The high temperature stability of the ceramic material also make them ideal for detecting combustion byproducts where exhaust temperatures can be high. However, problems do exist with metal oxide sensors. They are not very selective as they all tend to be sensitive to a number of reduction and oxidation reactions on the oxide's surface. This makes sensors with large numbers of sensors interesting to study as a method for introducing orthogonality to the system. Also, the sensors tend to suffer from long term drift for a number of reasons. In this thesis I will develop a system for intelligently modeling metal oxide sensors and determining their suitability for use in large arrays designed to analyze exhaust gas streams. It will introduce prior knowledge of the metal oxide sensors' response mechanisms in order to produce a response function for each sensor from sparse training data. The system will use the same technique to model and remove any long term drift from the sensor response. It will also provide an efficient means for determining the orthogonality of the sensor to determine whether they are useful in gas sensing arrays. The system is based on least squares support vector regression using the reciprocal kernel. The reciprocal kernel is introduced along with a method of optimizing the free parameters of the reciprocal kernel support vector machine. The reciprocal kernel is shown to be simpler and to perform better than an earlier kernel, the modified reciprocal kernel. Least squares support vector regression is chosen as it uses all of the training points and an emphasis was placed throughout this research for extracting the maximum information from very sparse data. The reciprocal kernel is shown to be effective in modeling the sensor responses in the time, gas and temperature domains, and the dual representation of the support vector regression solution is shown to provide insight into the sensor's sensitivity and potential orthogonality. Finally, the dual weights of the support vector regression solution to the sensor's response are suggested as a fitness function for a genetic algorithm, or some other method for efficiently searching large parameter spaces.

OpenClimateGIS - A Web Service Providing Climate Model Data in Commonly Used Geospatial Formats

NASA Astrophysics Data System (ADS)

Erickson, T. A.; Koziol, B. W.; Rood, R. B.

2011-12-01

The goal of the OpenClimateGIS project is to make climate model datasets readily available in commonly used, modern geospatial formats used by GIS software, browser-based mapping tools, and virtual globes.The climate modeling community typically stores climate data in multidimensional gridded formats capable of efficiently storing large volumes of data (such as netCDF, grib) while the geospatial community typically uses flexible vector and raster formats that are capable of storing small volumes of data (relative to the multidimensional gridded formats). OpenClimateGIS seeks to address this difference in data formats by clipping climate data to user-specified vector geometries (i.e. areas of interest) and translating the gridded data on-the-fly into multiple vector formats. The OpenClimateGIS system does not store climate data archives locally, but rather works in conjunction with external climate archives that expose climate data via the OPeNDAP protocol. OpenClimateGIS provides a RESTful API web service for accessing climate data resources via HTTP, allowing a wide range of applications to access the climate data.The OpenClimateGIS system has been developed using open source development practices and the source code is publicly available. The project integrates libraries from several other open source projects (including Django, PostGIS, numpy, Shapely, and netcdf4-python).OpenClimateGIS development is supported by a grant from NOAA's Climate Program Office.

Protection of Military Personnel Against Vector-Borne Diseases: A Review of Collaborative Work of the Australian and US Military Over the Last 30 Years.

PubMed

Frances, Stephen P; Edstein, Michael D; Debboun, Mustapha; Shanks, G Dennis

2016-01-01

Australian and US military medical services have collaborated since World War II to minimize vector-borne diseases such as malaria, dengue, and scrub typhus. In this review, collaboration over the last 30 years is discussed. The collaborative projects and exchange scientist programs have resulted in mutually beneficial outcomes in the fields of drug development and personal protection measures against vector-borne diseases.

Estimation of the laser cutting operating cost by support vector regression methodology

NASA Astrophysics Data System (ADS)

Jović, Srđan; Radović, Aleksandar; Šarkoćević, Živče; Petković, Dalibor; Alizamir, Meysam

2016-09-01

Laser cutting is a popular manufacturing process utilized to cut various types of materials economically. The operating cost is affected by laser power, cutting speed, assist gas pressure, nozzle diameter and focus point position as well as the workpiece material. In this article, the process factors investigated were: laser power, cutting speed, air pressure and focal point position. The aim of this work is to relate the operating cost to the process parameters mentioned above. CO2 laser cutting of stainless steel of medical grade AISI316L has been investigated. The main goal was to analyze the operating cost through the laser power, cutting speed, air pressure, focal point position and material thickness. Since the laser operating cost is a complex, non-linear task, soft computing optimization algorithms can be used. Intelligent soft computing scheme support vector regression (SVR) was implemented. The performance of the proposed estimator was confirmed with the simulation results. The SVR results are then compared with artificial neural network and genetic programing. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR compared to other soft computing methodologies. The new optimization methods benefit from the soft computing capabilities of global optimization and multiobjective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion.

Support vector machines for nuclear reactor state estimation

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

Zavaljevski, N.; Gross, K. C.

2000-02-14

Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformedmore » into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.« less

Pre-operative prediction of surgical morbidity in children: comparison of five statistical models.

PubMed

Cooper, Jennifer N; Wei, Lai; Fernandez, Soledad A; Minneci, Peter C; Deans, Katherine J

2015-02-01

The accurate prediction of surgical risk is important to patients and physicians. Logistic regression (LR) models are typically used to estimate these risks. However, in the fields of data mining and machine-learning, many alternative classification and prediction algorithms have been developed. This study aimed to compare the performance of LR to several data mining algorithms for predicting 30-day surgical morbidity in children. We used the 2012 National Surgical Quality Improvement Program-Pediatric dataset to compare the performance of (1) a LR model that assumed linearity and additivity (simple LR model) (2) a LR model incorporating restricted cubic splines and interactions (flexible LR model) (3) a support vector machine, (4) a random forest and (5) boosted classification trees for predicting surgical morbidity. The ensemble-based methods showed significantly higher accuracy, sensitivity, specificity, PPV, and NPV than the simple LR model. However, none of the models performed better than the flexible LR model in terms of the aforementioned measures or in model calibration or discrimination. Support vector machines, random forests, and boosted classification trees do not show better performance than LR for predicting pediatric surgical morbidity. After further validation, the flexible LR model derived in this study could be used to assist with clinical decision-making based on patient-specific surgical risks. Copyright © 2014 Elsevier Ltd. All rights reserved.

A communication-avoiding, hybrid-parallel, rank-revealing orthogonalization method.

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

Hoemmen, Mark

2010-11-01

Orthogonalization consumes much of the run time of many iterative methods for solving sparse linear systems and eigenvalue problems. Commonly used algorithms, such as variants of Gram-Schmidt or Householder QR, have performance dominated by communication. Here, 'communication' includes both data movement between the CPU and memory, and messages between processors in parallel. Our Tall Skinny QR (TSQR) family of algorithms requires asymptotically fewer messages between processors and data movement between CPU and memory than typical orthogonalization methods, yet achieves the same accuracy as Householder QR factorization. Furthermore, in block orthogonalizations, TSQR is faster and more accurate than existing approaches formore » orthogonalizing the vectors within each block ('normalization'). TSQR's rank-revealing capability also makes it useful for detecting deflation in block iterative methods, for which existing approaches sacrifice performance, accuracy, or both. We have implemented a version of TSQR that exploits both distributed-memory and shared-memory parallelism, and supports real and complex arithmetic. Our implementation is optimized for the case of orthogonalizing a small number (5-20) of very long vectors. The shared-memory parallel component uses Intel's Threading Building Blocks, though its modular design supports other shared-memory programming models as well, including computation on the GPU. Our implementation achieves speedups of 2 times or more over competing orthogonalizations. It is available now in the development branch of the Trilinos software package, and will be included in the 10.8 release.« less

Vectoring of parallel synthetic jets: A parametric study

NASA Astrophysics Data System (ADS)

Berk, Tim; Gomit, Guillaume; Ganapathisubramani, Bharathram

2016-11-01

The vectoring of a pair of parallel synthetic jets can be described using five dimensionless parameters: the aspect ratio of the slots, the Strouhal number, the Reynolds number, the phase difference between the jets and the spacing between the slots. In the present study, the influence of the latter four on the vectoring behaviour of the jets is examined experimentally using particle image velocimetry. Time-averaged velocity maps are used to study the variations in vectoring behaviour for a parametric sweep of each of the four parameters independently. A topological map is constructed for the full four-dimensional parameter space. The vectoring behaviour is described both qualitatively and quantitatively. A vectoring mechanism is proposed, based on measured vortex positions. We acknowledge the financial support from the European Research Council (ERC Grant Agreement No. 277472).

Determination of Anti-Adeno-Associated Virus Vector Neutralizing Antibody Titer with an In Vitro Reporter System

PubMed Central

Meliani, Amine; Leborgne, Christian; Triffault, Sabrina; Jeanson-Leh, Laurence; Veron, Philippe

2015-01-01

Abstract Adeno-associated virus (AAV) vectors are a platform of choice for in vivo gene transfer applications. However, neutralizing antibodies (NAb) to AAV can be found in humans and some animal species as a result of exposure to the wild-type virus, and high-titer NAb develop following AAV vector administration. In some conditions, anti-AAV NAb can block transduction with AAV vectors even when present at low titers, thus requiring prescreening before vector administration. Here we describe an improved in vitro, cell-based assay for the determination of NAb titer in serum or plasma samples. The assay is easy to setup and sensitive and, depending on the purpose, can be validated to support clinical development of gene therapy products based on AAV vectors. PMID:25819687

Matrix Multiplication Algorithm Selection with Support Vector Machines

DTIC Science & Technology

2015-05-01

libraries that could intelligently choose the optimal algorithm for a particular set of inputs. Users would be oblivious to the underlying algorithmic...SAT.” J. Artif . Intell. Res.(JAIR), vol. 32, pp. 565–606, 2008. [9] M. G. Lagoudakis and M. L. Littman, “Algorithm selection using reinforcement...Artificial Intelligence , vol. 21, no. 05, pp. 961–976, 2007. [15] C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM

Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set.

PubMed

Zhang, Jinshui; Yuan, Zhoumiqi; Shuai, Guanyuan; Pan, Yaozhong; Zhu, Xiufang

2017-04-26

This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD), to determine optimal parameters for support vector data description (SVDD) model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM) method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient ( C ) and kernel width ( s ), in mapping homogeneous specific land cover.

Vectoring of parallel synthetic jets

NASA Astrophysics Data System (ADS)

Berk, Tim; Ganapathisubramani, Bharathram; Gomit, Guillaume

2015-11-01

A pair of parallel synthetic jets can be vectored by applying a phase difference between the two driving signals. The resulting jet can be merged or bifurcated and either vectored towards the actuator leading in phase or the actuator lagging in phase. In the present study, the influence of phase difference and Strouhal number on the vectoring behaviour is examined experimentally. Phase-locked vorticity fields, measured using Particle Image Velocimetry (PIV), are used to track vortex pairs. The physical mechanisms that explain the diversity in vectoring behaviour are observed based on the vortex trajectories. For a fixed phase difference, the vectoring behaviour is shown to be primarily influenced by pinch-off time of vortex rings generated by the synthetic jets. Beyond a certain formation number, the pinch-off timescale becomes invariant. In this region, the vectoring behaviour is determined by the distance between subsequent vortex rings. We acknowledge the financial support from the European Research Council (ERC grant agreement no. 277472).

Evaluation of spray droplet spectrum of sprayers used for vector control

USDA-ARS?s Scientific Manuscript database

Droplet spectra data were collected from spray equipment intended for use in vector control by the US Department of Defense pest management programs to determine if they produce droplets in the ultra-low volume (ULV) spectrum. Droplets generated by 26 sprayers utilizing water + non-ionic surfactant...

Power line identification of millimeter wave radar based on PCA-GS-SVM

NASA Astrophysics Data System (ADS)

Fang, Fang; Zhang, Guifeng; Cheng, Yansheng

2017-12-01

Aiming at the problem that the existing detection method can not effectively solve the security of UAV's ultra low altitude flight caused by power line, a power line recognition method based on grid search (GS) and the principal component analysis and support vector machine (PCA-SVM) is proposed. Firstly, the candidate line of Hough transform is reduced by PCA, and the main feature of candidate line is extracted. Then, upport vector machine (SVM is) optimized by grid search method (GS). Finally, using support vector machine classifier optimized parameters to classify the candidate line. MATLAB simulation results show that this method can effectively identify the power line and noise, and has high recognition accuracy and algorithm efficiency.

Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification

PubMed Central

Li, Pengfei; Jiang, Yongying; Xiang, Jiawei

2014-01-01

To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples. PMID:24688361

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.

Hope, Interpreter Self-efficacy, and Social Impacts: Assessment of the NNOCCI Training

NASA Astrophysics Data System (ADS)

Fraser, J.; Swim, J.

2012-12-01

Conservation educators at informal science learning centers are well-positioned to teach climate science and motivate action but have resisted the topic. Our research demonstrates their resist is due to self-doubt about climate science facts and the belief they will encounter negative audience feedback. Further, this self-doubt and self-silencing is emotional taxing. As a result we have developed a National Network for Ocean Climate Change Interpretation's (NNOCCI) program that addresses educators' needs for technical training and emotional scaffolding to help them fully engage with this work. The evaluation of this program sought to understand how to support educators interested in promoting public literacy on climate change through engagement with a structured training program aimed at increased the efficacy of interpreters through teaching strategic framing strategies. The program engaged educator dyads from informal science learning sites to attend an online and in-person program that initiated a new community of practice focused on sharing techniques and tools for ocean climate change interpretation. The presentation will summarize a model for embedded assessment across all aspects of a program and how social vectors, based upon educators' interpersonal and professional relationships, impact the understanding of an educator's work across their life-world. This summary will be followed by results from qualitative front-end research that demonstrated the psychologically complex emotional conditions that describe the experience of being an environmental educator. The project evaluators will then present results from their focus groups and social network analysis to demonstrate how training impacted in-group relationships, skill development, and the layered social education strategies that help communities engage with the content. Results demonstrated that skill training increased educator's hope--in the form of increased perceived agency and plans for educational objectives. Subsequent to the program, educators experienced socially supportive feedback from colleagues and peers and increased actions to engage the public in productive discussions about climate change at informal science learning venues. The front-end and formative assessment of this program suggests new strategies for measuring interpreter training, and a way of thinking holistically about an educator's impact in their community. The results challenge the concept that interpretation is limited to the workplace and suggest that the increased likelihood of effectiveness in interpretation across all social vectors is more likely to result in changed public understanding of climate science in ways that will promote public action toward remediation strategies.Emotions before and after study circlet; Personal hope scale was rescaled to range from 1 "strongly disagree"; 4 "strongly agree"; Distress, Anxiety vs. hopeful and Energized vs. Overwhelmed range from 1 "not at all" to 4 "very much."

Earth observation in support of malaria control and epidemiology: MALAREO monitoring approaches.

PubMed

Franke, Jonas; Gebreslasie, Michael; Bauwens, Ides; Deleu, Julie; Siegert, Florian

2015-06-03

Malaria affects about half of the world's population, with the vast majority of cases occuring in Africa. National malaria control programmes aim to reduce the burden of malaria and its negative, socioeconomic effects by using various control strategies (e.g. vector control, environmental management and case tracking). Vector control is the most effective transmission prevention strategy, while environmental factors are the key parameters affecting transmission. Geographic information systems (GIS), earth observation (EO) and spatial modelling are increasingly being recognised as valuable tools for effective management and malaria vector control. Issues previously inhibiting the use of EO in epidemiology and malaria control such as poor satellite sensor performance, high costs and long turnaround times, have since been resolved through modern technology. The core goal of this study was to develop and implement the capabilities of EO data for national malaria control programmes in South Africa, Swaziland and Mozambique. High- and very high resolution (HR and VHR) land cover and wetland maps were generated for the identification of potential vector habitats and human activities, as well as geoinformation on distance to wetlands for malaria risk modelling, population density maps, habitat foci maps and VHR household maps. These products were further used for modelling malaria incidence and the analysis of environmental factors that favour vector breeding. Geoproducts were also transferred to the staff of national malaria control programmes in seven African countries to demonstrate how EO data and GIS can support vector control strategy planning and monitoring. The transferred EO products support better epidemiological understanding of environmental factors related to malaria transmission, and allow for spatio-temporal targeting of malaria control interventions, thereby improving the cost-effectiveness of interventions.

Seminal quality prediction using data mining methods.

PubMed

Sahoo, Anoop J; Kumar, Yugal

2014-01-01

Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of fertility rate. In this paper, eight feature selection methods are applied on fertility dataset to find out a set of good features. The investigational results shows that childish diseases (0.079) and high fever features (0.057) has less impact on fertility rate while age (0.8685), season (0.843), surgical intervention (0.7683), alcohol consumption (0.5992), smoking habit (0.575), number of hours spent on setting (0.4366) and accident (0.5973) features have more impact. It is also observed that feature selection methods increase the accuracy of above mentioned techniques (multilayer perceptron 92%, support vector machine 91%, SVM+PSO 94%, Navie Bayes (Kernel) 89% and decision tree 89%) as compared to without feature selection methods (multilayer perceptron 86%, support vector machine 86%, SVM+PSO 85%, Navie Bayes (Kernel) 83% and decision tree 84%) which shows the applicability of feature selection methods in prediction. This paper lightens the application of artificial techniques in medical domain. From this paper, it can be concluded that data mining methods can be used to predict a person with or without disease based on environmental and lifestyle parameters/features rather than undergoing various medical test. In this paper, five data mining techniques are used to predict the fertility rate and among which SVM+PSO provide more accurate results than support vector machine and decision tree.

A trace display and editing program for data from fluorescence based sequencing machines.

PubMed

Gleeson, T; Hillier, L

1991-12-11

'Ted' (Trace editor) is a graphical editor for sequence and trace data from automated fluorescence sequencing machines. It provides facilities for viewing sequence and trace data (in top or bottom strand orientation), for editing the base sequence, for automated or manual trimming of the head (vector) and tail (uncertain data) from the sequence, for vertical and horizontal trace scaling, for keeping a history of sequence editing, and for output of the edited sequence. Ted has been used extensively in the C.elegans genome sequencing project, both as a stand-alone program and integrated into the Staden sequence assembly package, and has greatly aided in the efficiency and accuracy of sequence editing. It runs in the X windows environment on Sun workstations and is available from the authors. Ted currently supports sequence and trace data from the ABI 373A and Pharmacia A.L.F. sequencers.

A Collaborative Framework for Distributed Privacy-Preserving Support Vector Machine Learning

PubMed Central

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates “privacy-insensitive” intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner. PMID:23304414

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

PubMed

Alcaide-Leon, P; Dufort, P; Geraldo, A F; Alshafai, L; Maralani, P J; Spears, J; Bharatha, A

2017-06-01

Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma. Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15. The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 ( P = .021), reader 2 ( P = .035), and reader 3 ( P = .007). Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma. © 2017 by American Journal of Neuroradiology.

Innovative dengue vector control interventions in Latin America: what do they cost?

PubMed Central

Basso, César; Beltrán-Ayala, Efraín; Mitchell-Foster, Kendra; Cortés, Sebastián; Manrique-Saide, Pablo; Guillermo-May, Guillermo; Carvalho de Lima, Edilmar

2016-01-01

Background Five studies were conducted in Fortaleza (Brazil), Girardot (Colombia), Machala (Ecuador), Acapulco (Mexico), and Salto (Uruguay) to assess dengue vector control interventions tailored to the context. The studies involved the community explicitly in the implementation, and focused on the most productive breeding places for Aedes aegypti. This article reports the cost analysis of these interventions. Methods We conducted the costing from the perspective of the vector control program. We collected data on quantities and unit costs of the resources used to deliver the interventions. Comparable information was requested for the routine activities. Cost items were classified, analyzed descriptively, and aggregated to calculate total costs, costs per house reached, and incremental costs. Results Cost per house of the interventions were $18.89 (Fortaleza), $21.86 (Girardot), $30.61 (Machala), $39.47 (Acapulco), and $6.98 (Salto). Intervention components that focused mainly on changes to the established vector control programs seem affordable; cost savings were identified in Salto (−21%) and the clean patio component in Machala (−12%). An incremental cost of 10% was estimated in Fortaleza. On the other hand, there were also completely new components that would require sizeable financial efforts (installing insecticide-treated nets in Girardot and Acapulco costs $16.97 and $24.96 per house, respectively). Conclusions The interventions are promising, seem affordable and may improve the cost profile of the established vector control programs. The costs of the new components could be considerable, and should be assessed in relation to the benefits in reduced dengue burden. PMID:26924235

Reduction of solar vector magnetograph data using a microMSP array processor

NASA Technical Reports Server (NTRS)

Kineke, Jack

1990-01-01

The processing of raw data obtained by the solar vector magnetograph at NASA-Marshall requires extensive arithmetic operations on large arrays of real numbers. The objectives of this summer faculty fellowship study are to: (1) learn the programming language of the MicroMSP Array Processor and adapt some existing data reduction routines to exploit its capabilities; and (2) identify other applications and/or existing programs which lend themselves to array processor utilization which can be developed by undergraduate student programmers under the provisions of project JOVE.

A vector-dyadic development of the equations of motion for N-coupled rigid bodies and point masses

NASA Technical Reports Server (NTRS)

Frisch, H. P.

1974-01-01

The equations of motion are derived, in vector-dyadic format, for a topological tree of coupled rigid bodies, point masses, and symmetrical momentum wheels. These equations were programmed, and form the basis for the general-purpose digital computer program N-BOD. A complete derivation of the equations of motion is included along with a description of the methods used for kinematics, constraint elimination, and for the inclusion of nongyroscope forces and torques acting external or internal to the system.

LFSPMC: Linear feature selection program using the probability of misclassification

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.; Marion, B. P.

1975-01-01

The computational procedure and associated computer program for a linear feature selection technique are presented. The technique assumes that: a finite number, m, of classes exists; each class is described by an n-dimensional multivariate normal density function of its measurement vectors; the mean vector and covariance matrix for each density function are known (or can be estimated); and the a priori probability for each class is known. The technique produces a single linear combination of the original measurements which minimizes the one-dimensional probability of misclassification defined by the transformed densities.

Development of Computer Program TWTVA for Calculation of 3-D Electron Trajectories in Coupled-Cavity TWTs

DTIC Science & Technology

1976-03-10

ferrule corner radius, which is typically not more than about 20). The radial mesh numbers are denoted NQR for the rf vector potential and Njyro for...the magnetic vector poten- tial matrices. Allowing for tne guard rows, the matrices run from -1 to NQR + 1, and -1 to Nj^ + 1. When the program...CR * 5» an(^ only one row ^or NCR up to 10, which covers most cases; as stated ir. the introduction, we do not expect NQR ever to exceed 20. For n

Preparation for a first-in-man lentivirus trial in patients with cystic fibrosis

PubMed Central

Alton, Eric W F W; Beekman, Jeffery M; Boyd, A Christopher; Brand, June; Carlon, Marianne S; Connolly, Mary M; Chan, Mario; Conlon, Sinead; Davidson, Heather E; Davies, Jane C; Davies, Lee A; Dekkers, Johanna F; Doherty, Ann; Gea-Sorli, Sabrina; Gill, Deborah R; Griesenbach, Uta; Hasegawa, Mamoru; Higgins, Tracy E; Hironaka, Takashi; Hyndman, Laura; McLachlan, Gerry; Inoue, Makoto; Hyde, Stephen C; Innes, J Alastair; Maher, Toby M; Moran, Caroline; Meng, Cuixiang; Paul-Smith, Michael C; Pringle, Ian A; Pytel, Kamila M; Rodriguez-Martinez, Andrea; Schmidt, Alexander C; Stevenson, Barbara J; Sumner-Jones, Stephanie G; Toshner, Richard; Tsugumine, Shu; Wasowicz, Marguerite W; Zhu, Jie

2017-01-01

We have recently shown that non-viral gene therapy can stabilise the decline of lung function in patients with cystic fibrosis (CF). However, the effect was modest, and more potent gene transfer agents are still required. Fuson protein (F)/Hemagglutinin/Neuraminidase protein (HN)-pseudotyped lentiviral vectors are more efficient for lung gene transfer than non-viral vectors in preclinical models. In preparation for a first-in-man CF trial using the lentiviral vector, we have undertaken key translational preclinical studies. Regulatory-compliant vectors carrying a range of promoter/enhancer elements were assessed in mice and human air–liquid interface (ALI) cultures to select the lead candidate; cystic fibrosis transmembrane conductance receptor (CFTR) expression and function were assessed in CF models using this lead candidate vector. Toxicity was assessed and ‘benchmarked’ against the leading non-viral formulation recently used in a Phase IIb clinical trial. Integration site profiles were mapped and transduction efficiency determined to inform clinical trial dose-ranging. The impact of pre-existing and acquired immunity against the vector and vector stability in several clinically relevant delivery devices was assessed. A hybrid promoter hybrid cytosine guanine dinucleotide (CpG)- free CMV enhancer/elongation factor 1 alpha promoter (hCEF) consisting of the elongation factor 1α promoter and the cytomegalovirus enhancer was most efficacious in both murine lungs and human ALI cultures (both at least 2-log orders above background). The efficacy (at least 14% of airway cells transduced), toxicity and integration site profile supports further progression towards clinical trial and pre-existing and acquired immune responses do not interfere with vector efficacy. The lead rSIV.F/HN candidate expresses functional CFTR and the vector retains 90–100% transduction efficiency in clinically relevant delivery devices. The data support the progression of the F/HN-pseudotyped lentiviral vector into a first-in-man CF trial in 2017. PMID:27852956

Comparative field trial of alternative vector control strategies for non-domiciliated Triatoma dimidiata.

PubMed

Ferral, Jhibran; Chavez-Nuñez, Leysi; Euan-Garcia, Maria; Ramirez-Sierra, Maria Jesus; Najera-Vazquez, M Rosario; Dumonteil, Eric

2010-01-01

Chagas disease is a major vector-borne disease, and regional initiatives based on insecticide spraying have successfully controlled domiciliated vectors in many regions. Non-domiciliated vectors remain responsible for a significant transmission risk, and their control is a challenge. We performed a proof-of-concept field trial to test alternative strategies in rural Yucatan, Mexico. Follow-up of house infestation for two seasons following the interventions confirmed that insecticide spraying should be performed annually for the effective control of Triatoma dimidiata; however, it also confirmed that insect screens or long-lasting impregnated curtains may represent good alternative strategies for the sustained control of these vectors. Ecosystemic peridomicile management would be an excellent complementary strategy to improve the cost-effectiveness of interventions. Because these strategies would also be effective against other vector-borne diseases, such as malaria or dengue, they could be integrated within a multi-disease control program.

Recognition and Classification of Road Condition on the Basis of Friction Force by Using a Mobile Robot

NASA Astrophysics Data System (ADS)

Watanabe, Tatsuhito; Katsura, Seiichiro

A person operating a mobile robot in a remote environment receives realistic visual feedback about the condition of the road on which the robot is moving. The categorization of the road condition is necessary to evaluate the conditions for safe and comfortable driving. For this purpose, the mobile robot should be capable of recognizing and classifying the condition of the road surfaces. This paper proposes a method for recognizing the type of road surfaces on the basis of the friction between the mobile robot and the road surfaces. This friction is estimated by a disturbance observer, and a support vector machine is used to classify the surfaces. The support vector machine identifies the type of the road surface using feature vector, which is determined using the arithmetic average and variance derived from the torque values. Further, these feature vectors are mapped onto a higher dimensional space by using a kernel function. The validity of the proposed method is confirmed by experimental results.

Protein Kinase Classification with 2866 Hidden Markov Models and One Support Vector Machine

NASA Technical Reports Server (NTRS)

Weber, Ryan; New, Michael H.; Fonda, Mark (Technical Monitor)

2002-01-01

The main application considered in this paper is predicting true kinases from randomly permuted kinases that share the same length and amino acid distributions as the true kinases. Numerous methods already exist for this classification task, such as HMMs, motif-matchers, and sequence comparison algorithms. We build on some of these efforts by creating a vector from the output of thousands of structurally based HMMs, created offline with Pfam-A seed alignments using SAM-T99, which then must be combined into an overall classification for the protein. Then we use a Support Vector Machine for classifying this large ensemble Pfam-Vector, with a polynomial and chisquared kernel. In particular, the chi-squared kernel SVM performs better than the HMMs and better than the BLAST pairwise comparisons, when predicting true from false kinases in some respects, but no one algorithm is best for all purposes or in all instances so we consider the particular strengths and weaknesses of each.

Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

DTIC Science & Technology

2014-03-27

and machine learning for a range of research including such topics as medical imaging [10] and handwriting recognition [11]. The type of feature...1989. [11] C. Bahlmann, B. Haasdonk, and H. Burkhardt, “Online handwriting recognition with support vector machines-a kernel approach,” in Eighth...International Workshop on Frontiers in Handwriting Recognition, pp. 49–54, IEEE, 2002. [12] C. Cortes and V. Vapnik, “Support-vector networks,” Machine

Algorithm for detection the QRS complexes based on support vector machine

NASA Astrophysics Data System (ADS)

Van, G. V.; Podmasteryev, K. V.

2017-11-01

The efficiency of computer ECG analysis depends on the accurate detection of QRS-complexes. This paper presents an algorithm for QRS complex detection based of support vector machine (SVM). The proposed algorithm is evaluated on annotated standard databases such as MIT-BIH Arrhythmia database. The QRS detector obtained a sensitivity Se = 98.32% and specificity Sp = 95.46% for MIT-BIH Arrhythmia database. This algorithm can be used as the basis for the software to diagnose electrical activity of the heart.

PMICALC: an R code-based software for estimating post-mortem interval (PMI) compatible with Windows, Mac and Linux operating systems.

PubMed

Muñoz-Barús, José I; Rodríguez-Calvo, María Sol; Suárez-Peñaranda, José M; Vieira, Duarte N; Cadarso-Suárez, Carmen; Febrero-Bande, Manuel

2010-01-30

In legal medicine the correct determination of the time of death is of utmost importance. Recent advances in estimating post-mortem interval (PMI) have made use of vitreous humour chemistry in conjunction with Linear Regression, but the results are questionable. In this paper we present PMICALC, an R code-based freeware package which estimates PMI in cadavers of recent death by measuring the concentrations of potassium ([K+]), hypoxanthine ([Hx]) and urea ([U]) in the vitreous humor using two different regression models: Additive Models (AM) and Support Vector Machine (SVM), which offer more flexibility than the previously used Linear Regression. The results from both models are better than those published to date and can give numerical expression of PMI with confidence intervals and graphic support within 20 min. The program also takes into account the cause of death. 2009 Elsevier Ireland Ltd. All rights reserved.

Libsharp - spherical harmonic transforms revisited

NASA Astrophysics Data System (ADS)

Reinecke, M.; Seljebotn, D. S.

2013-06-01

We present libsharp, a code library for spherical harmonic transforms (SHTs), which evolved from the libpsht library and addresses several of its shortcomings, such as adding MPI support for distributed memory systems and SHTs of fields with arbitrary spin, but also supporting new developments in CPU instruction sets like the Advanced Vector Extensions (AVX) or fused multiply-accumulate (FMA) instructions. The library is implemented in portable C99 and provides an interface that can be easily accessed from other programming languages such as C++, Fortran, Python, etc. Generally, libsharp's performance is at least on par with that of its predecessor; however, significant improvements were made to the algorithms for scalar SHTs, which are roughly twice as fast when using the same CPU capabilities. The library is available at http://sourceforge.net/projects/libsharp/ under the terms of the GNU General Public License.

DOC II 32-bit digital optical computer: optoelectronic hardware and software

NASA Astrophysics Data System (ADS)

Stone, Richard V.; Zeise, Frederick F.; Guilfoyle, Peter S.

1991-12-01

This paper describes current electronic hardware subsystems and software code which support OptiComp's 32-bit general purpose digital optical computer (DOC II). The reader is referred to earlier papers presented in this section for a thorough discussion of theory and application regarding DOC II. The primary optoelectronic subsystems include the drive electronics for the multichannel acousto-optic modulators, the avalanche photodiode amplifier, as well as threshold circuitry, and the memory subsystems. This device utilizes a single optical Boolean vector matrix multiplier and its VME based host controller interface in performing various higher level primitives. OptiComp Corporation wishes to acknowledge the financial support of the Office of Naval Research, the National Aeronautics and Space Administration, the Rome Air Development Center, and the Strategic Defense Initiative Office for the funding of this program under contracts N00014-87-C-0077, N00014-89-C-0266 and N00014-89-C- 0225.

A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.

PubMed

Yan, Kang K; Zhao, Hongyu; Pang, Herbert

2017-12-06

High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

Evolutionary programming-based univector field navigation method for past mobile robots.

PubMed

Kim, Y J; Kim, J H; Kwon, D S

2001-01-01

Most of navigation techniques with obstacle avoidance do not consider the robot orientation at the target position. These techniques deal with the robot position only and are independent of its orientation and velocity. To solve these problems this paper proposes a novel univector field method for fast mobile robot navigation which introduces a normalized two dimensional vector field. The method provides fast moving robots with the desired posture at the target position and obstacle avoidance. To obtain the sub-optimal vector field, a function approximator is used and trained by evolutionary programming. Two kinds of vector fields are trained, one for the final posture acquisition and the other for obstacle avoidance. Computer simulations and real experiments are carried out for a fast moving mobile robot to demonstrate the effectiveness of the proposed scheme.

VISMapper: ultra-fast exhaustive cartography of viral insertion sites for gene therapy.

PubMed

Juanes, José M; Gallego, Asunción; Tárraga, Joaquín; Chaves, Felipe J; Marín-Garcia, Pablo; Medina, Ignacio; Arnau, Vicente; Dopazo, Joaquín

2017-09-20

The possibility of integrating viral vectors to become a persistent part of the host genome makes them a crucial element of clinical gene therapy. However, viral integration has associated risks, such as the unintentional activation of oncogenes that can result in cancer. Therefore, the analysis of integration sites of retroviral vectors is a crucial step in developing safer vectors for therapeutic use. Here we present VISMapper, a vector integration site analysis web server, to analyze next-generation sequencing data for retroviral vector integration sites. VISMapper can be found at: http://vismapper.babelomics.org . Because it uses novel mapping algorithms VISMapper is remarkably faster than previous available programs. It also provides a useful graphical interface to analyze the integration sites found in the genomic context.

Mathematical solution of multilevel fractional programming problem with fuzzy goal programming approach

NASA Astrophysics Data System (ADS)

Lachhwani, Kailash; Poonia, Mahaveer Prasad

2012-08-01

In this paper, we show a procedure for solving multilevel fractional programming problems in a large hierarchical decentralized organization using fuzzy goal programming approach. In the proposed method, the tolerance membership functions for the fuzzily described numerator and denominator part of the objective functions of all levels as well as the control vectors of the higher level decision makers are respectively defined by determining individual optimal solutions of each of the level decision makers. A possible relaxation of the higher level decision is considered for avoiding decision deadlock due to the conflicting nature of objective functions. Then, fuzzy goal programming approach is used for achieving the highest degree of each of the membership goal by minimizing negative deviational variables. We also provide sensitivity analysis with variation of tolerance values on decision vectors to show how the solution is sensitive to the change of tolerance values with the help of a numerical example.

Increasing the computational efficient of digital cross correlation by a vectorization method

NASA Astrophysics Data System (ADS)

Chang, Ching-Yuan; Ma, Chien-Ching

2017-08-01

This study presents a vectorization method for use in MATLAB programming aimed at increasing the computational efficiency of digital cross correlation in sound and images, resulting in a speedup of 6.387 and 36.044 times compared with performance values obtained from looped expression. This work bridges the gap between matrix operations and loop iteration, preserving flexibility and efficiency in program testing. This paper uses numerical simulation to verify the speedup of the proposed vectorization method as well as experiments to measure the quantitative transient displacement response subjected to dynamic impact loading. The experiment involved the use of a high speed camera as well as a fiber optic system to measure the transient displacement in a cantilever beam under impact from a steel ball. Experimental measurement data obtained from the two methods are in excellent agreement in both the time and frequency domain, with discrepancies of only 0.68%. Numerical and experiment results demonstrate the efficacy of the proposed vectorization method with regard to computational speed in signal processing and high precision in the correlation algorithm. We also present the source code with which to build MATLAB-executable functions on Windows as well as Linux platforms, and provide a series of examples to demonstrate the application of the proposed vectorization method.

A linear programming manual

NASA Technical Reports Server (NTRS)

Tuey, R. C.

1972-01-01

Computer solutions of linear programming problems are outlined. Information covers vector spaces, convex sets, and matrix algebra elements for solving simultaneous linear equations. Dual problems, reduced cost analysis, ranges, and error analysis are illustrated.

Vectorized multigrid Poisson solver for the CDC CYBER 205

NASA Technical Reports Server (NTRS)

Barkai, D.; Brandt, M. A.

1984-01-01

The full multigrid (FMG) method is applied to the two dimensional Poisson equation with Dirichlet boundary conditions. This has been chosen as a relatively simple test case for examining the efficiency of fully vectorizing of the multigrid method. Data structure and programming considerations and techniques are discussed, accompanied by performance details.

The use of digital spaceborne SAR data for the delineation of surface features indicative of malaria vector breeding habitats

NASA Technical Reports Server (NTRS)

Imhoff, M. L.; Vermillion, C. H.; Khan, F. A.

1984-01-01

An investigation to examine the utility of spaceborne radar image data to malaria vector control programs is described. Specific tasks involve an analysis of radar illumination geometry vs information content, the synergy of radar and multispectral data mergers, and automated information extraction techniques.

Evaluation of the discrete vortex wake cross flow model using vector computers. Part 2: User's manual for DIVORCE

NASA Technical Reports Server (NTRS)

Deffenbaugh, F. D.; Vitz, J. F.

1979-01-01

The users manual for the Discrete Vortex Cross flow Evaluator (DIVORCE) computer program is presented. DIVORCE was developed in FORTRAN 4 for the DCD 6600 and CDC 7600 machines. Optimal calls to a NASA vector subroutine package are provided for use with the CDC 7600.

F-15B ACTIVE - First supersonic yaw vectoring flight

NASA Technical Reports Server (NTRS)

1996-01-01

On Wednesday, April 24, 1996, the F-15 Advanced Control Technology for Integrated Vehicles (ACTIVE) aircraft achieved its first supersonic yaw vectoring flight at Dryden Flight Research Center, Edwards, California. ACTIVE is a joint NASA, U.S. Air Force, McDonnell Douglas Aerospace (MDA) and Pratt & Whitney (P&W) program. The team will assess performance and technology benefits during flight test operations. Current plans call for approximately 60 flights totaling 100 hours. 'Reaching this milestone is very rewarding. We hope to set some more records before we're through,' stated Roger W. Bursey, P&W's pitch-yaw balance beam nozzle (PYBBN) program manager. A pair of P&W PYBBNs vectored (horizontally side-to-side, pitch is up and down) the thrust for the MDA manufactured F-15 research aircraft. Power to reach supersonic speeds was provided by two high-performance F100-PW-229 engines that were modified with the multi-directional thrust vectoring nozzles. The new concept should lead to significant increases in performance of both civil and military aircraft flying at subsonic and supersonic speeds.

Vector-Based Data Services for NASA Earth Science

NASA Astrophysics Data System (ADS)

Rodriguez, J.; Roberts, J. T.; Ruvane, K.; Cechini, M. F.; Thompson, C. K.; Boller, R. A.; Baynes, K.

2016-12-01

Vector data sources offer opportunities for mapping and visualizing science data in a way that allows for more customizable rendering and deeper data analysis than traditional raster images, and popular formats like GeoJSON and Mapbox Vector Tiles allow diverse types of geospatial data to be served in a high-performance and easily consumed-package. Vector data is especially suited to highly dynamic mapping applications and visualization of complex datasets, while growing levels of support for vector formats and features in open-source mapping clients has made utilizing them easier and more powerful than ever. NASA's Global Imagery Browse Services (GIBS) is working to make NASA data more easily and conveniently accessible than ever by serving vector datasets via GeoJSON, Mapbox Vector Tiles, and raster images. This presentation will review these output formats, the services, including WFS, WMS, and WMTS, that can be used to access the data, and some ways in which vector sources can be utilized in popular open-source mapping clients like OpenLayers. Lessons learned from GIBS' recent move towards serving vector will be discussed, as well as how to use GIBS open source software to create, configure, and serve vector data sources using Mapserver and the GIBS OnEarth Apache module.

Evidence that explains absence of a latent period for Xylella fastidiosa in its sharpshooter vectors

USDA-ARS?s Scientific Manuscript database

The glassy-winged sharpshooter (GWSS), Homalodisca vitripennis (Germar), and other sharpshooter (Cicadelline) leafhoppers transmit Xylella fastidiosa (Xf), the causative agent of Pierce’s disease of grapevine and other scorch diseases. Past research has supported that vectors have virtually no late...

Statistical learning algorithms for identifying contrasting tillage practices with landsat thematic mapper data

USDA-ARS?s Scientific Manuscript database

Tillage management practices have direct impact on water holding capacity, evaporation, carbon sequestration, and water quality. This study examines the feasibility of two statistical learning algorithms, such as Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM), for cla...

Application of Classification Models to Pharyngeal High-Resolution Manometry

ERIC Educational Resources Information Center

Mielens, Jason D.; Hoffman, Matthew R.; Ciucci, Michelle R.; McCulloch, Timothy M.; Jiang, Jack J.

2012-01-01

Purpose: The authors present 3 methods of performing pattern recognition on spatiotemporal plots produced by pharyngeal high-resolution manometry (HRM). Method: Classification models, including the artificial neural networks (ANNs) multilayer perceptron (MLP) and learning vector quantization (LVQ), as well as support vector machines (SVM), were…

A Language-Independent Approach to Automatic Text Difficulty Assessment for Second-Language Learners

DTIC Science & Technology

2013-08-01

best-suited for regression. Our baseline uses z-normalized shallow length features and TF -LOG weighted vectors on bag-of-words for Arabic, Dari...length features and TF -LOG weighted vectors on bag-of-words for Arabic, Dari, English and Pashto. We compare Support Vector Machines and the Margin...football, whereas they are much less common in documents about opera). We used TF -LOG weighted word frequencies on bag-of-words for each document

Extraction of inland Nypa fruticans (Nipa Palm) using Support Vector Machine

NASA Astrophysics Data System (ADS)

Alberto, R. T.; Serrano, S. C.; Damian, G. B.; Camaso, E. E.; Biagtan, A. R.; Panuyas, N. Z.; Quibuyen, J. S.

2017-09-01

Mangroves are considered as one of the major habitats in coastal ecosystem, providing a lot of economic and ecological services in human society. Nypa fruticans (Nipa palm) is one of the important species of mangroves because of its versatility and uniqueness as halophytic palm. However, nipas are not only adaptable in saline areas, they can also managed to thrive away from the coastline depending on the favorable soil types available in the area. Because of this, mapping of this species are not limited alone in the near shore areas, but in areas where this species are present as well. The extraction process of Nypa fruticans were carried out using the available LiDAR data. Support Vector Machine (SVM) classification process was used to extract nipas in inland areas. The SVM classification process in mapping Nypa fruticans produced high accuracy of 95+%. The Support Vector Machine classification process to extract inland nipas was proven to be effective by utilizing different terrain derivatives from LiDAR data.

Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression

NASA Astrophysics Data System (ADS)

Fei, Cheng-Wei; Bai, Guang-Chen

2014-12-01

To improve the computational precision and efficiency of probabilistic design for mechanical dynamic assembly like the blade-tip radial running clearance (BTRRC) of gas turbine, a distribution collaborative probabilistic design method-based support vector machine of regression (SR)(called as DCSRM) is proposed by integrating distribution collaborative response surface method and support vector machine regression model. The mathematical model of DCSRM is established and the probabilistic design idea of DCSRM is introduced. The dynamic assembly probabilistic design of aeroengine high-pressure turbine (HPT) BTRRC is accomplished to verify the proposed DCSRM. The analysis results reveal that the optimal static blade-tip clearance of HPT is gained for designing BTRRC, and improving the performance and reliability of aeroengine. The comparison of methods shows that the DCSRM has high computational accuracy and high computational efficiency in BTRRC probabilistic analysis. The present research offers an effective way for the reliability design of mechanical dynamic assembly and enriches mechanical reliability theory and method.

Using Support Vector Machines to Automatically Extract Open Water Signatures from POLDER Multi-Angle Data Over Boreal Regions

NASA Technical Reports Server (NTRS)

Pierce, J.; Diaz-Barrios, M.; Pinzon, J.; Ustin, S. L.; Shih, P.; Tournois, S.; Zarco-Tejada, P. J.; Vanderbilt, V. C.; Perry, G. L.; Brass, James A. (Technical Monitor)

2002-01-01

This study used Support Vector Machines to classify multiangle POLDER data. Boreal wetland ecosystems cover an estimated 90 x 10(exp 6) ha, about 36% of global wetlands, and are a major source of trace gases emissions to the atmosphere. Four to 20 percent of the global emission of methane to the atmosphere comes from wetlands north of 4 degrees N latitude. Large uncertainties in emissions exist because of large spatial and temporal variation in the production and consumption of methane. Accurate knowledge of the areal extent of open water and inundated vegetation is critical to estimating magnitudes of trace gas emissions. Improvements in land cover mapping have been sought using physical-modeling approaches, neural networks, and active microwave, examples that demonstrate the difficulties of separating open water, inundated vegetation and dry upland vegetation. Here we examine the feasibility of using a support vector machine to classify POLDER data representing open water, inundated vegetation and dry upland vegetation.

Color image segmentation with support vector machines: applications to road signs detection.

PubMed

Cyganek, Bogusław

2008-08-01

In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the road signs recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space. By this a better data encapsulation with a linear hypersphere can be usually achieved. Moreover, the classifier does not try to capture the whole distribution of the input data which is often difficult to achieve. Instead, the characteristic data samples, called support vectors, are selected which allow construction of the tightest hypersphere that encloses majority of the input data. Then classification of a test data simply consists in a measurement of its distance to a centre of the found hypersphere. The experimental results show high accuracy and speed of the proposed method.

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%).

Sparse Solutions for Single Class SVMs: A Bi-Criterion Approach

NASA Technical Reports Server (NTRS)

Das, Santanu; Oza, Nikunj C.

2011-01-01

In this paper we propose an innovative learning algorithm - a variation of One-class nu Support Vector Machines (SVMs) learning algorithm to produce sparser solutions with much reduced computational complexities. The proposed technique returns an approximate solution, nearly as good as the solution set obtained by the classical approach, by minimizing the original risk function along with a regularization term. We introduce a bi-criterion optimization that helps guide the search towards the optimal set in much reduced time. The outcome of the proposed learning technique was compared with the benchmark one-class Support Vector machines algorithm which more often leads to solutions with redundant support vectors. Through out the analysis, the problem size for both optimization routines was kept consistent. We have tested the proposed algorithm on a variety of data sources under different conditions to demonstrate the effectiveness. In all cases the proposed algorithm closely preserves the accuracy of standard one-class nu SVMs while reducing both training time and test time by several factors.

Lie Symmetry Analysis of the Inhomogeneous Toda Lattice Equation via Semi-Discrete Exterior Calculus

NASA Astrophysics Data System (ADS)

Liu, Jiang; Wang, Deng-Shan; Yin, Yan-Bin

2017-06-01

In this work, the Lie point symmetries of the inhomogeneous Toda lattice equation are obtained by semi-discrete exterior calculus, which is a semi-discrete version of Harrison and Estabrook’s geometric approach. A four-dimensional Lie algebra and its one-, two- and three-dimensional subalgebras are given. Two similarity reductions of the inhomogeneous Toda lattice equation are obtained by using the symmetry vectors. Supported by National Natural Science Foundation of China under Grant Nos. 11375030, 11472315, and Department of Science and Technology of Henan Province under Grant No. 162300410223 and Beijing Finance Funds of Natural Science Program for Excellent Talents under Grant No. 2014000026833ZK19

Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

NASA Astrophysics Data System (ADS)

Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

2017-10-01

In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks.

PubMed

Hsieh, Chung-Ho; Lu, Ruey-Hwa; Lee, Nai-Hsin; Chiu, Wen-Ta; Hsu, Min-Huei; Li, Yu-Chuan Jack

2011-01-01

Diagnosing acute appendicitis clinically is still difficult. We developed random forests, support vector machines, and artificial neural network models to diagnose acute appendicitis. Between January 2006 and December 2008, patients who had a consultation session with surgeons for suspected acute appendicitis were enrolled. Seventy-five percent of the data set was used to construct models including random forest, support vector machines, artificial neural networks, and logistic regression. Twenty-five percent of the data set was withheld to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate performance, which was compared with that of the Alvarado score. Data from a total of 180 patients were collected, 135 used for training and 45 for testing. The mean age of patients was 39.4 years (range, 16-85). Final diagnosis revealed 115 patients with and 65 without appendicitis. The AUC of random forest, support vector machines, artificial neural networks, logistic regression, and Alvarado was 0.98, 0.96, 0.91, 0.87, and 0.77, respectively. The sensitivity, specificity, positive, and negative predictive values of random forest were 94%, 100%, 100%, and 87%, respectively. Random forest performed better than artificial neural networks, logistic regression, and Alvarado. We demonstrated that random forest can predict acute appendicitis with good accuracy and, deployed appropriately, can be an effective tool in clinical decision making. Copyright © 2011 Mosby, Inc. All rights reserved.

MIST: An Open Source Environmental Modelling Programming Language Incorporating Easy to Use Data Parallelism.

NASA Astrophysics Data System (ADS)

Bellerby, Tim

2014-05-01

Model Integration System (MIST) is open-source environmental modelling programming language that directly incorporates data parallelism. The language is designed to enable straightforward programming structures, such as nested loops and conditional statements to be directly translated into sequences of whole-array (or more generally whole data-structure) operations. MIST thus enables the programmer to use well-understood constructs, directly relating to the mathematical structure of the model, without having to explicitly vectorize code or worry about details of parallelization. A range of common modelling operations are supported by dedicated language structures operating on cell neighbourhoods rather than individual cells (e.g.: the 3x3 local neighbourhood needed to implement an averaging image filter can be simply accessed from within a simple loop traversing all image pixels). This facility hides details of inter-process communication behind more mathematically relevant descriptions of model dynamics. The MIST automatic vectorization/parallelization process serves both to distribute work among available nodes and separately to control storage requirements for intermediate expressions - enabling operations on very large domains for which memory availability may be an issue. MIST is designed to facilitate efficient interpreter based implementations. A prototype open source interpreter is available, coded in standard FORTRAN 95, with tools to rapidly integrate existing FORTRAN 77 or 95 code libraries. The language is formally specified and thus not limited to FORTRAN implementation or to an interpreter-based approach. A MIST to FORTRAN compiler is under development and volunteers are sought to create an ANSI-C implementation. Parallel processing is currently implemented using OpenMP. However, parallelization code is fully modularised and could be replaced with implementations using other libraries. GPU implementation is potentially possible.

Quality Control Specialist | Center for Cancer Research

Cancer.gov

Within the Leidos Biomedical Research Inc.’s Clinical Research Directorate, the Clinical Monitoring Research Program (CMRP) provides high-quality comprehensive and strategic operational support to the high-profile domestic and international clinical research initiatives of the National Cancer Institute (NCI), National Institute of Allergy and Infectious Diseases (NIAID), Clinical Center (CC), National Institute of Heart, Lung and Blood Institute (NHLBI), National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), National Center for Advancing Translational Sciences (NCATS), National Institute of Neurological Disorders and Stroke (NINDS), and the National Institute of Mental Health (NIMH). Since its inception in 2001, CMRP’s ability to provide rapid responses, high-quality solutions, and to recruit and retain experts with a variety of backgrounds to meet the growing research portfolios of NCI, NIAID, CC, NHLBI, NIAMS, NCATS, NINDS, and NIMH has led to the considerable expansion of the program and its repertoire of support services. CMRP’s support services are strategically aligned with the program’s mission to provide comprehensive, dedicated support to assist National Institutes of Health researchers in providing the highest quality of clinical research in compliance with applicable regulations and guidelines, maintaining data integrity, and protecting human subjects. For the scientific advancement of clinical research, CMRP services include comprehensive clinical trials, regulatory, pharmacovigilance, protocol navigation and development, and programmatic and project management support for facilitating the conduct of 400+ Phase I, II, and III domestic and international trials on a yearly basis. These trials investigate the prevention, diagnosis, treatment of, and therapies for cancer, influenza, HIV, and other infectious diseases and viruses such as hepatitis C, tuberculosis, malaria, and Ebola virus; heart, lung, and blood diseases and conditions; parasitic infections; rheumatic and inflammatory diseases; and rare and neglected diseases. CMRP’s collaborative approach to clinical research and the expertise and dedication of staff to the continuation and success of the program’s mission has contributed to improving the overall standards of public health on a global scale. The Clinical Monitoring Research Program (CMRP) provides analytical experience to work on innovative T-cell therapy for cancer treatment, and responsible for development and execution of analytical assays for generation lot release assays and product CoAs in support of the National Cancer Institute’s (NCI’s), Center for Cancer Research (CCR), Surgery Branch (SB). KEY ROLES/RESPONSIBILITIES-THIS POSITION IS CONTINGENT UPON FUNDING APPROVAL Collaborates on the development of templates and processes to enhance the performance of the program Provides organizational and document support including preparation of documents (spreadsheets, web pages, written narrative, meeting minutes) and timely communication with committee and working group members through email contact and websites Establishes, implements and maintains standardized processes and assess performance to make recommendations for improvement Prepares technical reports, abstracts, presentations and program correspondence concerning assigned projects through research and analysis of information relevant to government policy, regulations and other relevant data Monitors all assigned programs for compliance Facilitates communication through all levels of staff by functioning as a liaison between internal departments, senior management, and the customer Attends weekly meetings to discuss upcoming events, tasks, special projects and implementation plans Develops and implements procedures/programs to ensure effective and efficient business and operational processes Identifies potential bottlenecks in the upcoming development process and works with all team members and senior management to resolve them Analyzes and tracks initiatives and contracts Coordinates and reviews daily operations and logistics, including purchasing and shipping of miscellaneous equipment, lab and office supplies to ensure compliance with appropriate government regulations Aids in measuring, monitoring, problem solving, and reporting of research, operational and business issues Reviews operational requirements for development of or changes to staffing Perform all tasks associated with the manufacture of clinical product (vector or cell) Cross-trains on SOPs and procedures required to support both vector and cell production Follows batch records and SOPs Executes GMP runs in close collaboration with Process Development and Quality Assurance and Quality Control groups Assists in the development of SOPs, batch records, deviations and change controls Troubleshoots processing and equipment issues Participates in investigations regarding out of specifications/trend (OOS/OOT) results; addresses and manages deviations to manufacturing procedures

Clinical Trials Using Anti-CD19/CD28/CD3zeta CAR Gammaretroviral Vector-transduced Autologous T Lymphocytes KTE-C19

Cancer.gov

NCI supports clinical trials that test new and more effective ways to treat cancer. Find clinical trials studying anti-cd19/cd28/cd3zeta car gammaretroviral vector-transduced autologous t lymphocytes kte-c19.

Elucidating the Potential of Plant Rhabdoviruses as Vector Expressions Systems

USDA-ARS?s Scientific Manuscript database

Maize fine streak virus (MFSV) is a member of the genus Nucleorhabdovirus that is transmitted by the leafhopper Graminella nigrifons. The virus replicates in both its maize host and its insect vector. To determine whether Drosophila S2 cells support the production of full-length MFSV proteins, we ...

Tilt changes of short duration

USGS Publications Warehouse

McHugh, Stuart

1976-01-01

Section I of this report contains a classification scheme for short period tilt data. For convenience, all fluctuations in the local tilt field of less than 24 hours duration will be designated SP (i.e., short period) tilt events. Three basic categories of waveshape appearance are defined, and the rules for naming the waveforms are outlined. Examples from tilt observations at four central California sites are provided. Section II contains some coseismic tilt data. Fourteen earthquakes in central California, ranging in magnitude from 2.9 to 5.2, were chosen for study on four tiltmeters within 10 source dimensions of the epicenters. The raw records from each of the four tiltmeters at the times of the earthquakes were photographed and are presented in this section. Section III contains documentation of computer programs used in the analysis of the short period tilt data. Program VECTOR computes the difference vector of a tilt event and displays the sequence of events as a head-to-tail vector plot. Program ONSTSP 1) requires two component digitized tilt data as input, 2) scales and plots the data, and 3) computes and displays the amplitude, azimuth, and normalized derivative of the tilt amplitude. Program SHARPS computes the onset sharpness, (i.e., the normalized derivative of the tilt amplitude at the onset of the tilt event) as a function of source-station distance from a model of creep-related tilt changes. Program DSPLAY plots the digitized data.

Chagas disease: national survey of seroprevalence in children under five years of age conducted in 2008.

PubMed

Russomando, Graciela; Cousiño, Blanca; Sanchez, Zunilda; Franco, Laura X; Nara, Eva M; Chena, Lilian; Martínez, Magaly; Galeano, María E; Benitez, Lucio

2017-05-01

Since the early 1990s, programs to control Chagas disease in South America have focused on eradicating domiciliary Triatoma infestans, the main vector. Seroprevalence studies of the chagasic infection are included as part of the vector control programs; they are essential to assess the impact of vector control measures and to monitor the prevention of vector transmission. To assess the interruption of domiciliary vector transmission of Chagas disease by T. infestans in Paraguay by evaluating the current state of transmission in rural areas. A survey of seroprevalence of Chagas disease was carried out in a representative sample group of Paraguayans aged one to five years living in rural areas of Paraguay in 2008. Blood samples collected on filter paper from 12,776 children were tested using an enzyme-linked immunosorbent assay. Children whose serology was positive or undetermined (n = 41) were recalled to donate a whole blood sample for retesting. Their homes were inspected for current triatomine infestation. Blood samples from their respective mothers were also collected and tested to check possible transmission of the disease by a congenital route. A seroprevalence rate of 0.24% for Trypanosoma cruzi infection was detected in children under five years of age among the country's rural population. Our findings indicate that T. cruzi was transmitted to these children vertically. The total number of infected children, aged one to five years living in these departments, was estimated at 1,691 cases with an annual incidence of congenital transmission of 338 cases per year. We determined the impact of vector control in the transmission of T. cruzi, following uninterrupted vector control measures employed since 1999 in contiguous T. infestans-endemic areas of Paraguay, and this allowed us to estimate the degree of risk of congenital transmission in the country.

Program documentation. Program description and user information for the hydraulics/auxiliary power unit (HYDRA) computer program. [for the space shuttle orbiter

NASA Technical Reports Server (NTRS)

Redwine, W. J.

1979-01-01

A timeline containing altitude, control surface deflection rates and angles, hinge moment loads, thrust vector control gimbal rates, and main throttle settings is used to derive the model. The timeline is constructed from the output of one or more trajectory simulation programs.

NBOD2- PROGRAM TO DERIVE AND SOLVE EQUATIONS OF MOTION FOR COUPLED N-BODY SYSTEMS

NASA Technical Reports Server (NTRS)

Frisch, H. P.

1994-01-01

The analysis of the dynamic characteristics of a complex system, such as a spacecraft or a robot, is usually best accomplished through the study of a simulation model. The simulation model must have the same dynamic characteristics as the complex system, while lending itself to mathematical quantification. The NBOD2 computer program was developed to aid in the analysis of spacecraft attitude dynamics. NBOD2 is a very general program that may be applied to a large class of problems involving coupled N-body systems. NBOD2 provides the dynamics analyst with the capability to automatically derive and numerically solve the equations of motion for any system that can be modeled as a topological tree of coupled rigid bodies, flexible bodies, point masses, and symmetrical momentum wheels. NBOD2 uses a topological tree model of the dynamic system to derive the vector-dyadic equations of motion for the system. The user builds this topological tree model by using rigid and flexible bodies, point masses, and symmetrical momentum wheels with appropriate connections. To insure that the relative motion between contiguous bodies is kinematically constrained, NBOD2 assumes that contiguous rigid and flexible bodies are connected by physically reliable 0, 1, 2, and 3-degrees-of-freedom gimbals. These gimbals prohibit relative translational motion, while permitting up to 3 degrees of relative rotational freedom at hinge points. Point masses may have 0, 1, 2, or 3-degrees of relative translational freedom, and symmetric momentum wheels may have a single degree of rotational freedom relative to the body in which they are imbedded. Flexible bodies may possess several degrees of vibrational freedom in addition to the degrees of freedom associated with the connection gimbals. Data concerning the natural modes and vibrations of the flexible bodies must be supplied by the user. NBOD2 combines the best features of the discrete-body approach and the nested body approach to reduce the topological tree to a complete set of nonlinear equations of motion in vector-dyadic form for the system being analyzed. NBOD2 can then numerically solve the equations of motion. Input to NBOD2 consists of a user-supplied description of the system to be modeled. The NBOD2 system includes an interactive, tutorial, input support program to aid the NBOD2 user in preparing input data. Output from NBOD2 consists of a listing of the complete set of nonlinear equations of motion in vector-dyadic form and any userspecified set of system state variables. The NBOD2 program is written in FORTRAN 77 for batch execution and has been implemented on a DEC VAX-11/780 computer. The NBOD2 program was developed in 1978 and last updated in 1982.

An efficient and portable SIMD algorithm for charge/current deposition in Particle-In-Cell codes

DOE PAGES

Vincenti, H.; Lobet, M.; Lehe, R.; ...

2016-09-19

In current computer architectures, data movement (from die to network) is by far the most energy consuming part of an algorithm (≈20pJ/word on-die to ≈10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction registers that are able to process multiple data with one arithmetic operator in one clock cycle. SIMD registermore » length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular data structure that takes into account memory alignment constraints and avoids gather/scat;ter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide data registers). Results show a factor of ×2 to ×2.5 speed-up in double precision for particle shape factor of orders 1–3. The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi:http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries:  OpenMP > 4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector data registers capable of performing one single instruction on multiple data during one clock cycle. Data register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).« less

An efficient and portable SIMD algorithm for charge/current deposition in Particle-In-Cell codes

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

Vincenti, H.; Lobet, M.; Lehe, R.

In current computer architectures, data movement (from die to network) is by far the most energy consuming part of an algorithm (≈20pJ/word on-die to ≈10,000 pJ/word on the network). To increase memory locality at the hardware level and reduce energy consumption related to data movement, future exascale computers tend to use many-core processors on each compute nodes that will have a reduced clock speed to allow for efficient cooling. To compensate for frequency decrease, machine vendors are making use of long SIMD instruction registers that are able to process multiple data with one arithmetic operator in one clock cycle. SIMD registermore » length is expected to double every four years. As a consequence, Particle-In-Cell (PIC) codes will have to achieve good vectorization to fully take advantage of these upcoming architectures. In this paper, we present a new algorithm that allows for efficient and portable SIMD vectorization of current/charge deposition routines that are, along with the field gathering routines, among the most time consuming parts of the PIC algorithm. Our new algorithm uses a particular data structure that takes into account memory alignment constraints and avoids gather/scat;ter instructions that can significantly affect vectorization performances on current CPUs. The new algorithm was successfully implemented in the 3D skeleton PIC code PICSAR and tested on Haswell Xeon processors (AVX2-256 bits wide data registers). Results show a factor of ×2 to ×2.5 speed-up in double precision for particle shape factor of orders 1–3. The new algorithm can be applied as is on future KNL (Knights Landing) architectures that will include AVX-512 instruction sets with 512 bits register lengths (8 doubles/16 singles). Program summary Program Title: vec_deposition Program Files doi:http://dx.doi.org/10.17632/nh77fv9k8c.1 Licensing provisions: BSD 3-Clause Programming language: Fortran 90 External routines/libraries:  OpenMP > 4.0 Nature of problem: Exascale architectures will have many-core processors per node with long vector data registers capable of performing one single instruction on multiple data during one clock cycle. Data register lengths are expected to double every four years and this pushes for new portable solutions for efficiently vectorizing Particle-In-Cell codes on these future many-core architectures. One of the main hotspot routines of the PIC algorithm is the current/charge deposition for which there is no efficient and portable vector algorithm. Solution method: Here we provide an efficient and portable vector algorithm of current/charge deposition routines that uses a new data structure, which significantly reduces gather/scatter operations. Vectorization is controlled using OpenMP 4.0 compiler directives for vectorization which ensures portability across different architectures. Restrictions: Here we do not provide the full PIC algorithm with an executable but only vector routines for current/charge deposition. These scalar/vector routines can be used as library routines in your 3D Particle-In-Cell code. However, to get the best performances out of vector routines you have to satisfy the two following requirements: (1) Your code should implement particle tiling (as explained in the manuscript) to allow for maximized cache reuse and reduce memory accesses that can hinder vector performances. The routines can be used directly on each particle tile. (2) You should compile your code with a Fortran 90 compiler (e.g Intel, gnu or cray) and provide proper alignment flags and compiler alignment directives (more details in README file).« less

Computation of transonic potential flow about 3 dimensional inlets, ducts, and bodies

NASA Technical Reports Server (NTRS)

Reyhner, T. A.

1982-01-01

An analysis was developed and a computer code, P465 Version A, written for the prediction of transonic potential flow about three dimensional objects including inlet, duct, and body geometries. Finite differences and line relaxation are used to solve the complete potential flow equation. The coordinate system used for the calculations is independent of body geometry. Cylindrical coordinates are used for the computer code. The analysis is programmed in extended FORTRAN 4 for the CYBER 203 vector computer. The programming of the analysis is oriented toward taking advantage of the vector processing capabilities of this computer. Comparisons of computed results with experimental measurements are presented to verify the analysis. Descriptions of program input and output formats are also presented.

Solution of a large hydrodynamic problem using the STAR-100 computer

NASA Technical Reports Server (NTRS)

Weilmuenster, K. J.; Howser, L. M.

1976-01-01

A representative hydrodynamics problem, the shock initiated flow over a flat plate, was used for exploring data organizations and program structures needed to exploit the STAR-100 vector processing computer. A brief description of the problem is followed by a discussion of how each portion of the computational process was vectorized. Finally, timings of different portions of the program are compared with equivalent operations on serial machines. The speed up of the STAR-100 over the CDC 6600 program is shown to increase as the problem size increases. All computations were carried out on a CDC 6600 and a CDC STAR 100, with code written in FORTRAN for the 6600 and in STAR FORTRAN for the STAR 100.

Adaptation and optimization of a line-by-line radiative transfer program for the STAR-100 (STARSMART)

NASA Technical Reports Server (NTRS)

Rarig, P. L.

1980-01-01

A program to calculate upwelling infrared radiation was modified to operate efficiently on the STAR-100. The modified software processes specific test cases significantly faster than the initial STAR-100 code. For example, a midlatitude summer atmospheric model is executed in less than 2% of the time originally required on the STAR-100. Furthermore, the optimized program performs extra operations to save the calculated absorption coefficients. Some of the advantages and pitfalls of virtual memory and vector processing are discussed along with strategies used to avoid loss of accuracy and computing power. Results from the vectorized code, in terms of speed, cost, and relative error with respect to serial code solutions are encouraging.

[Challenges of the medical entomology for the surveillance in public health in Colombia: reflections on the state of malaria].

PubMed

Brochero, Helena; Quiñones, Martha L

2008-03-01

The relevance of the medical entomology was considered with respect to current framework of malaria control programs in Colombia. A responsibility is indicated for balancing control efforts along with providing information on the malaria vectors. This knowledge must be acquired in order to focus the related activities that are required. The malaria control program must be based on results of local entomological surveillance, and the data must be in a form to give practical answers to questions regarding the control program. Difficulties in undertaking the required studies are described, particularly regarding the taxonomic identification of Colombian Anopheles in Colombia and which of these can be incriminated as malaria vectors.

Myria: Scalable Analytics as a Service

NASA Astrophysics Data System (ADS)

Howe, B.; Halperin, D.; Whitaker, A.

2014-12-01

At the UW eScience Institute, we're working to empower non-experts, especially in the sciences, to write and use data-parallel algorithms. To this end, we are building Myria, a web-based platform for scalable analytics and data-parallel programming. Myria's internal model of computation is the relational algebra extended with iteration, such that every program is inherently data-parallel, just as every query in a database is inherently data-parallel. But unlike databases, iteration is a first class concept, allowing us to express machine learning tasks, graph traversal tasks, and more. Programs can be expressed in a number of languages and can be executed on a number of execution environments, but we emphasize a particular language called MyriaL that supports both imperative and declarative styles and a particular execution engine called MyriaX that uses an in-memory column-oriented representation and asynchronous iteration. We deliver Myria over the web as a service, providing an editor, performance analysis tools, and catalog browsing features in a single environment. We find that this web-based "delivery vector" is critical in reaching non-experts: they are insulated from irrelevant effort technical work associated with installation, configuration, and resource management. The MyriaX backend, one of several execution runtimes we support, is a main-memory, column-oriented, RDBMS-on-the-worker system that supports cyclic data flows as a first-class citizen and has been shown to outperform competitive systems on 100-machine cluster sizes. I will describe the Myria system, give a demo, and present some new results in large-scale oceanographic microbiology.

VEST: Abstract Vector Calculus Simplification in Mathematica

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

J. Squire, J. Burby and H. Qin

2013-03-12

We present a new package, VEST (Vector Einstein Summation Tools), that performs abstract vector calculus computations in Mathematica. Through the use of index notation, VEST is able to reduce scalar and vector expressions of a very general type using a systematic canonicalization procedure. In addition, utilizing properties of the Levi-Civita symbol, the program can derive types of multi-term vector identities that are not recognized by canonicalization, subsequently applying these to simplify large expressions. In a companion paper [1], we employ VEST in the automation of the calculation of Lagrangians for the single particle guiding center system in plasma physics, amore » computation which illustrates its ability to handle very large expressions. VEST has been designed to be simple and intuitive to use, both for basic checking of work and more involved computations. __________________________________________________« less

VEST: Abstract vector calculus simplification in Mathematica

NASA Astrophysics Data System (ADS)

Squire, J.; Burby, J.; Qin, H.

2014-01-01

We present a new package, VEST (Vector Einstein Summation Tools), that performs abstract vector calculus computations in Mathematica. Through the use of index notation, VEST is able to reduce three-dimensional scalar and vector expressions of a very general type to a well defined standard form. In addition, utilizing properties of the Levi-Civita symbol, the program can derive types of multi-term vector identities that are not recognized by reduction, subsequently applying these to simplify large expressions. In a companion paper Burby et al. (2013) [12], we employ VEST in the automation of the calculation of high-order Lagrangians for the single particle guiding center system in plasma physics, a computation which illustrates its ability to handle very large expressions. VEST has been designed to be simple and intuitive to use, both for basic checking of work and more involved computations.

Artificial Potential Field Controllers for Robust Communications in a Network of Swarm Robots

DTIC Science & Technology

2005-05-18

vectors are less than 90◦ apart. Algorithm 1 The Algorithm for generating a feasible set of vectors P ← set of high priority vectors Csum ← [( LOS1 +R1...the 46 C program was finished reading and writing the values to the serial line it would delete the timing file. Only after the timing file had been... deleted would the base station write new values for the wheel velocities. The timing file kept both the Linux PC and the base station synchronized so

Generalizations of Tikhonov's regularized method of least squares to non-Euclidean vector norms

NASA Astrophysics Data System (ADS)

Volkov, V. V.; Erokhin, V. I.; Kakaev, V. V.; Onufrei, A. Yu.

2017-09-01

Tikhonov's regularized method of least squares and its generalizations to non-Euclidean norms, including polyhedral, are considered. The regularized method of least squares is reduced to mathematical programming problems obtained by "instrumental" generalizations of the Tikhonov lemma on the minimal (in a certain norm) solution of a system of linear algebraic equations with respect to an unknown matrix. Further studies are needed for problems concerning the development of methods and algorithms for solving reduced mathematical programming problems in which the objective functions and admissible domains are constructed using polyhedral vector norms.

Divide and Recombine for Large Complex Data

DTIC Science & Technology

2017-12-01

Empirical Methods in Natural Language Processing , October 2014 Keywords Enter keywords for the publication. URL Enter the URL...low-latency data processing systems. Declarative Languages for Interactive Visualization: The Reactive Vega Stack Another thread of XDATA research...for array processing operations embedded in the R programming language . Vector virtual machines work well for long vectors. One of the most

Vectorization and parallelization of the finite strip method for dynamic Mindlin plate problems

NASA Technical Reports Server (NTRS)

Chen, Hsin-Chu; He, Ai-Fang

1993-01-01

The finite strip method is a semi-analytical finite element process which allows for a discrete analysis of certain types of physical problems by discretizing the domain of the problem into finite strips. This method decomposes a single large problem into m smaller independent subproblems when m harmonic functions are employed, thus yielding natural parallelism at a very high level. In this paper we address vectorization and parallelization strategies for the dynamic analysis of simply-supported Mindlin plate bending problems and show how to prevent potential conflicts in memory access during the assemblage process. The vector and parallel implementations of this method and the performance results of a test problem under scalar, vector, and vector-concurrent execution modes on the Alliant FX/80 are also presented.

Multiaxis Thrust-Vectoring Characteristics of a Model Representative of the F-18 High-Alpha Research Vehicle at Angles of Attack From 0 deg to 70 deg

NASA Technical Reports Server (NTRS)

Asbury, Scott C.; Capone, Francis J.

1995-01-01

An investigation was conducted in the Langley 16-Foot Transonic Tunnel to determine the multiaxis thrust-vectoring characteristics of the F-18 High-Alpha Research Vehicle (HARV). A wingtip supported, partially metric, 0.10-scale jet-effects model of an F-18 prototype aircraft was modified with hardware to simulate the thrust-vectoring control system of the HARV. Testing was conducted at free-stream Mach numbers ranging from 0.30 to 0.70, at angles of attack from O' to 70', and at nozzle pressure ratios from 1.0 to approximately 5.0. Results indicate that the thrust-vectoring control system of the HARV can successfully generate multiaxis thrust-vectoring forces and moments. During vectoring, resultant thrust vector angles were always less than the corresponding geometric vane deflection angle and were accompanied by large thrust losses. Significant external flow effects that were dependent on Mach number and angle of attack were noted during vectoring operation. Comparisons of the aerodynamic and propulsive control capabilities of the HARV configuration indicate that substantial gains in controllability are provided by the multiaxis thrust-vectoring control system.

Mitochondrial DNA Detects a Complex Evolutionary History with Pleistocene Epoch Divergence for the Neotropical Malaria Vector Anopheles nuneztovari Sensu Lato

PubMed Central

Scarpassa, Vera Margarete; Conn, Jan E.

2011-01-01

Cryptic species and lineages characterize Anopheles nuneztovari s.l. Gabaldón, an important malaria vector in South America. We investigated the phylogeographic structure across the range of this species with cytochrome oxidase subunit I (COI) mitochondrial DNA sequences to estimate the number of clades and levels of divergence. Bayesian and maximum-likelihood phylogenetic analyses detected four groups distributed in two major monophyletic clades (I and II). Samples from the Amazon Basin were clustered in clade I, as were subclades II-A and II-B, whereas those from Bolivia/Colombia/Venezuela were restricted to one basal subclade (II-C). These data, together with a statistical parsimony network, confirm results of previous studies that An. nuneztovari is a species complex consisting of at least two cryptic taxa, one occurring in Colombia and Venezuela and the another occurring in the Amazon Basin. These data also suggest that additional incipient species may exist in the Amazon Basin. Divergence time and expansion tests suggested that these groups separated and expanded in the Pleistocene Epoch. In addition, the COI sequences clearly separated An. nuneztovari s.l. from the closely related species An. dunhami Causey, and three new records are reported for An. dunhami in Amazonian Brazil. These findings are relevant for vector control programs in areas where both species occur. Our analyses support dynamic geologic and landscape changes in northern South America, and infer particularly active divergence during the Pleistocene Epoch for New World anophelines. PMID:22049039

General Quantum Meet-in-the-Middle Search Algorithm Based on Target Solution of Fixed Weight

NASA Astrophysics Data System (ADS)

Fu, Xiang-Qun; Bao, Wan-Su; Wang, Xiang; Shi, Jian-Hong

2016-10-01

Similar to the classical meet-in-the-middle algorithm, the storage and computation complexity are the key factors that decide the efficiency of the quantum meet-in-the-middle algorithm. Aiming at the target vector of fixed weight, based on the quantum meet-in-the-middle algorithm, the algorithm for searching all n-product vectors with the same weight is presented, whose complexity is better than the exhaustive search algorithm. And the algorithm can reduce the storage complexity of the quantum meet-in-the-middle search algorithm. Then based on the algorithm and the knapsack vector of the Chor-Rivest public-key crypto of fixed weight d, we present a general quantum meet-in-the-middle search algorithm based on the target solution of fixed weight, whose computational complexity is \\sumj = 0d {(O(\\sqrt {Cn - k + 1d - j }) + O(C_kj log C_k^j))} with Σd i =0 Ck i memory cost. And the optimal value of k is given. Compared to the quantum meet-in-the-middle search algorithm for knapsack problem and the quantum algorithm for searching a target solution of fixed weight, the computational complexity of the algorithm is lower. And its storage complexity is smaller than the quantum meet-in-the-middle-algorithm. Supported by the National Basic Research Program of China under Grant No. 2013CB338002 and the National Natural Science Foundation of China under Grant No. 61502526

Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

NASA Astrophysics Data System (ADS)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

Foamy Virus Vector Carries a Strong Insulator in Its Long Terminal Repeat Which Reduces Its Genotoxic Potential

PubMed Central

2017-01-01

ABSTRACT Strong viral enhancers in gammaretrovirus vectors have caused cellular proto-oncogene activation and leukemia, necessitating the use of cellular promoters in “enhancerless” self-inactivating integrating vectors. However, cellular promoters result in relatively low transgene expression, often leading to inadequate disease phenotype correction. Vectors derived from foamy virus, a nonpathogenic retrovirus, show higher preference for nongenic integrations than gammaretroviruses/lentiviruses and preferential integration near transcriptional start sites, like gammaretroviruses. We found that strong viral enhancers/promoters placed in foamy viral vectors caused extremely low immortalization of primary mouse hematopoietic stem/progenitor cells compared to analogous gammaretrovirus/lentivirus vectors carrying the same enhancers/promoters, an effect not explained solely by foamy virus' modest insertional site preference for nongenic regions compared to gammaretrovirus/lentivirus vectors. Using CRISPR/Cas9-mediated targeted insertion of analogous proviral sequences into the LMO2 gene and then measuring LMO2 expression, we demonstrate a sequence-specific effect of foamy virus, independent of insertional bias, contributing to reduced genotoxicity. We show that this effect is mediated by a 36-bp insulator located in the foamy virus long terminal repeat (LTR) that has high-affinity binding to the CCCTC-binding factor. Using our LMO2 activation assay, LMO2 expression was significantly increased when this insulator was removed from foamy virus and significantly reduced when the insulator was inserted into the lentiviral LTR. Our results elucidate a mechanism underlying the low genotoxicity of foamy virus, identify a novel insulator, and support the use of foamy virus as a vector for gene therapy, especially when strong enhancers/promoters are required. IMPORTANCE Understanding the genotoxic potential of viral vectors is important in designing safe and efficacious vectors for gene therapy. Self-inactivating vectors devoid of viral long-terminal-repeat enhancers have proven safe; however, transgene expression from cellular promoters is often insufficient for full phenotypic correction. Foamy virus is an attractive vector for gene therapy. We found foamy virus vectors to be remarkably less genotoxic, well below what was expected from their integration site preferences. We demonstrate that the foamy virus long terminal repeats contain an insulator element that binds CCCTC-binding factor and reduces its insertional genotoxicity. Our study elucidates a mechanism behind the low genotoxic potential of foamy virus, identifies a unique insulator, and supports the use of foamy virus as a vector for gene therapy. PMID:29046446

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

Predicting healthcare associated infections using patients' experiences

NASA Astrophysics Data System (ADS)

Pratt, Michael A.; Chu, Henry

2016-05-01

Healthcare associated infections (HAI) are a major threat to patient safety and are costly to health systems. Our goal is to predict the HAI performance of a hospital using the patients' experience responses as input. We use four classifiers, viz. random forest, naive Bayes, artificial feedforward neural networks, and the support vector machine, to perform the prediction of six types of HAI. The six types include blood stream, urinary tract, surgical site, and intestinal infections. Experiments show that the random forest and support vector machine perform well across the six types of HAI.

QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM).

PubMed

Qin, Zijian; Wang, Maolin; Yan, Aixia

2017-07-01

In this study, quantitative structure-activity relationship (QSAR) models using various descriptor sets and training/test set selection methods were explored to predict the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by using a multiple linear regression (MLR) and a support vector machine (SVM) method. 512 HCV NS3/4A protease inhibitors and their IC 50 values which were determined by the same FRET assay were collected from the reported literature to build a dataset. All the inhibitors were represented with selected nine global and 12 2D property-weighted autocorrelation descriptors calculated from the program CORINA Symphony. The dataset was divided into a training set and a test set by a random and a Kohonen's self-organizing map (SOM) method. The correlation coefficients (r 2 ) of training sets and test sets were 0.75 and 0.72 for the best MLR model, 0.87 and 0.85 for the best SVM model, respectively. In addition, a series of sub-dataset models were also developed. The performances of all the best sub-dataset models were better than those of the whole dataset models. We believe that the combination of the best sub- and whole dataset SVM models can be used as reliable lead designing tools for new NS3/4A protease inhibitors scaffolds in a drug discovery pipeline. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hadoop-Based Distributed System for Online Prediction of Air Pollution Based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Ghaemi, Z.; Farnaghi, M.; Alimohammadi, A.

2015-12-01

The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

Detection of distorted frames in retinal video-sequences via machine learning

NASA Astrophysics Data System (ADS)

Kolar, Radim; Liberdova, Ivana; Odstrcilik, Jan; Hracho, Michal; Tornow, Ralf P.

2017-07-01

This paper describes detection of distorted frames in retinal sequences based on set of global features extracted from each frame. The feature vector is consequently used in classification step, in which three types of classifiers are tested. The best classification accuracy 96% has been achieved with support vector machine approach.

Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences.

PubMed

An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Fang, Yu-Hong; Zhao, Yu-Jun; Zhang, Ming

2016-01-01

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

Study on vibration characteristics and fault diagnosis method of oil-immersed flat wave reactor in Arctic area converter station

NASA Astrophysics Data System (ADS)

Lai, Wenqing; Wang, Yuandong; Li, Wenpeng; Sun, Guang; Qu, Guomin; Cui, Shigang; Li, Mengke; Wang, Yongqiang

2017-10-01

Based on long term vibration monitoring of the No.2 oil-immersed fat wave reactor in the ±500kV converter station in East Mongolia, the vibration signals in normal state and in core loose fault state were saved. Through the time-frequency analysis of the signals, the vibration characteristics of the core loose fault were obtained, and a fault diagnosis method based on the dual tree complex wavelet (DT-CWT) and support vector machine (SVM) was proposed. The vibration signals were analyzed by DT-CWT, and the energy entropy of the vibration signals were taken as the feature vector; the support vector machine was used to train and test the feature vector, and the accurate identification of the core loose fault of the flat wave reactor was realized. Through the identification of many groups of normal and core loose fault state vibration signals, the diagnostic accuracy of the result reached 97.36%. The effectiveness and accuracy of the method in the fault diagnosis of the flat wave reactor core is verified.

Orthogonal vector algorithm to obtain the solar vector using the single-scattering Rayleigh model.

PubMed

Wang, Yinlong; Chu, Jinkui; Zhang, Ran; Shi, Chao

2018-02-01

Information obtained from a polarization pattern in the sky provides many animals like insects and birds with vital long-distance navigation cues. The solar vector can be derived from the polarization pattern using the single-scattering Rayleigh model. In this paper, an orthogonal vector algorithm, which utilizes the redundancy of the single-scattering Rayleigh model, is proposed. We use the intersection angles between the polarization vectors as the main criteria in our algorithm. The assumption that all polarization vectors can be considered coplanar is used to simplify the three-dimensional (3D) problem with respect to the polarization vectors in our simulation. The surface-normal vector of the plane, which is determined by the polarization vectors after translation, represents the solar vector. Unfortunately, the two-directionality of the polarization vectors makes the resulting solar vector ambiguous. One important result of this study is, however, that this apparent disadvantage has no effect on the complexity of the algorithm. Furthermore, two other universal least-squares algorithms were investigated and compared. A device was then constructed, which consists of five polarized-light sensors as well as a 3D attitude sensor. Both the simulation and experimental data indicate that the orthogonal vector algorithms, if used with a suitable threshold, perform equally well or better than the other two algorithms. Our experimental data reveal that if the intersection angles between the polarization vectors are close to 90°, the solar-vector angle deviations are small. The data also support the assumption of coplanarity. During the 51 min experiment, the mean of the measured solar-vector angle deviations was about 0.242°, as predicted by our theoretical model.

AN ADA LINEAR ALGEBRA PACKAGE MODELED AFTER HAL/S

NASA Technical Reports Server (NTRS)

Klumpp, A. R.

1994-01-01

This package extends the Ada programming language to include linear algebra capabilities similar to those of the HAL/S programming language. The package is designed for avionics applications such as Space Station flight software. In addition to the HAL/S built-in functions, the package incorporates the quaternion functions used in the Shuttle and Galileo projects, and routines from LINPAK that solve systems of equations involving general square matrices. Language conventions in this package follow those of HAL/S to the maximum extent practical and minimize the effort required for writing new avionics software and translating existent software into Ada. Valid numeric types in this package include scalar, vector, matrix, and quaternion declarations. (Quaternions are fourcomponent vectors used in representing motion between two coordinate frames). Single precision and double precision floating point arithmetic is available in addition to the standard double precision integer manipulation. Infix operators are used instead of function calls to define dot products, cross products, quaternion products, and mixed scalar-vector, scalar-matrix, and vector-matrix products. The package contains two generic programs: one for floating point, and one for integer. The actual component type is passed as a formal parameter to the generic linear algebra package. The procedures for solving systems of linear equations defined by general matrices include GEFA, GECO, GESL, and GIDI. The HAL/S functions include ABVAL, UNIT, TRACE, DET, INVERSE, TRANSPOSE, GET, PUT, FETCH, PLACE, and IDENTITY. This package is written in Ada (Version 1.2) for batch execution and is machine independent. The linear algebra software depends on nothing outside the Ada language except for a call to a square root function for floating point scalars (such as SQRT in the DEC VAX MATHLIB library). This program was developed in 1989, and is a copyrighted work with all copyright vested in NASA.

Feature selection using a one dimensional naïve Bayes’ classifier increases the accuracy of support vector machine classification of CDR3 repertoires

PubMed Central

Cinelli, Mattia; Sun, , Yuxin; Best, Katharine; Heather, James M.; Reich-Zeliger, Shlomit; Shifrut, Eric; Friedman, Nir; Shawe-Taylor, John; Chain, Benny

2017-01-01

Abstract Motivation: 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. Results: 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. Summary: 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. Availability and implementation: 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. Contact: b.chain@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28073756

SYVA: A program to analyze symmetry of molecules based on vector algebra

NASA Astrophysics Data System (ADS)

Gyevi-Nagy, László; Tasi, Gyula

2017-06-01

Symmetry is a useful concept in physics and chemistry. It can be used to find out some simple properties of a molecule or simplify complex calculations. In this paper a simple vector algebraic method is described to determine all symmetry elements of an arbitrary molecule. To carry out the symmetry analysis, a program has been written, which is also capable of generating the framework group of the molecule, revealing the symmetry properties of normal modes of vibration and symmetrizing the structure. To demonstrate the capabilities of the program, it is compared to other common widely used stand-alone symmetry analyzer (SYMMOL, Symmetrizer) and molecular modeling (NWChem, ORCA, MRCC) software. SYVA can generate input files for molecular modeling programs, e.g. Gaussian, using precisely symmetrized molecular structures. Possible applications are also demonstrated by integrating SYVA with the GAMESS and MRCC software.

Identification of Wolbachia Strains in Mosquito Disease Vectors

PubMed Central

Osei-Poku, Jewelna; Han, Calvin; Mbogo, Charles M.; Jiggins, Francis M.

2012-01-01

Wolbachia bacteria are common endosymbionts of insects, and some strains are known to protect their hosts against RNA viruses and other parasites. This has led to the suggestion that releasing Wolbachia-infected mosquitoes could prevent the transmission of arboviruses and other human parasites. We have identified Wolbachia in Kenyan populations of the yellow fever vector Aedes bromeliae and its relative Aedes metallicus, and in Mansonia uniformis and Mansonia africana, which are vectors of lymphatic filariasis. These Wolbachia strains cluster together on the bacterial phylogeny, and belong to bacterial clades that have recombined with other unrelated strains. These new Wolbachia strains may be affecting disease transmission rates of infected mosquito species, and could be transferred into other mosquito vectors as part of control programs. PMID:23185484

A gene cassette for adapting Escherichia coli strains as hosts for att-Int-mediated rearrangement and pL expression vectors.

PubMed

Balakrishnan, R; Bolten, B; Backman, K C

1994-01-28

A cassette of genes from bacteriophage lambda, when carried on a derivative of bacteriophage Mu, renders strains of Escherichia coli (and in principle other Mu-sensitive bacteria) capable of supporting lambda-based expression vectors, such as rearrangement vectors and pL vectors. The gene cassette contains a temperature-sensitive allele of the repressor gene, cIts857, and a shortened leftward operon comprising, oLpL, N, xis and int. Transfection and lysogenization of this cassette into various host bacteria is mediated by phage Mu functions. Examples of regulated expression of the gene encoding T4 DNA ligase are presented.

Training Extract, AFSC 113X0B, Flight Engineer, Helicopter Qualified.

DTIC Science & Technology

1982-12-01

TRAINING .................................................................. 1I0 SE. GENERAL rLIGHT RULES A 9 C t | 7. PeRFORM INSPECIIOIS 12 7A... TRAINING .................................................................. lIC 6E. GENERAL FLIGHT RULES A a C...ME"BERS PERFORMING I-A I PROGRAM GENERATED VECTOR IMEMBERS/ NO TYPE VECTOR MEAN - SC DESCRIPTION I TN. SEP J.6 2.J TRAINING EMPHASIS RATINSS I IIOS

In Practice: Using the "Developing Competency" Vector to Prepare Students for Competent Academic Major Exploration

ERIC Educational Resources Information Center

Galilee-Belfer, Mika

2012-01-01

Though many programs for undecided students focus on the "developing purpose" vector, the author argues that putting purpose before competency is putting the cart before the horse. In this article, she shares practical strategies she has used to help her students at the University of Arizona reach competence in understanding the academic world.…

Pest Management Practices for the Military: Novel Field Studies to Develop Methods to Protect Deployed Troops from Mosquito, Filth/Biting Flies, and Sand Fly Vectors

USDA-ARS?s Scientific Manuscript database

New techniques that we developed to protect deployed military troops from the threat of vector-borne diseases and are also applicable for use by civilian mosquito control program use are described. Techniques illustrated included (1) novel military personal protection methods, (2) barrier treatments...

Use of vectors in sequence analysis.

PubMed

Ishikawa, T; Yamamoto, K; Yoshikura, H

1987-10-01

Applications of the vector diagram, a new type of representation of protein structure, in homology search of various proteins including oncogene products are presented. The method takes account of various kinds of information concerning the properties of amino acids, such as Chou and Fasman's probability data. The method can detect conformational similarities of proteins which may not be detected by the conventional programs.

Self-Organizing-Map Program for Analyzing Multivariate Data

NASA Technical Reports Server (NTRS)

Li, P. Peggy; Jacob, Joseph C.; Block, Gary L.; Braverman, Amy J.

2005-01-01

SOM_VIS is a computer program for analysis and display of multidimensional sets of Earth-image data typified by the data acquired by the Multi-angle Imaging Spectro-Radiometer [MISR (a spaceborne instrument)]. In SOM_VIS, an enhanced self-organizing-map (SOM) algorithm is first used to project a multidimensional set of data into a nonuniform three-dimensional lattice structure. The lattice structure is mapped to a color space to obtain a color map for an image. The Voronoi cell-refinement algorithm is used to map the SOM lattice structure to various levels of color resolution. The final result is a false-color image in which similar colors represent similar characteristics across all its data dimensions. SOM_VIS provides a control panel for selection of a subset of suitably preprocessed MISR radiance data, and a control panel for choosing parameters to run SOM training. SOM_VIS also includes a component for displaying the false-color SOM image, a color map for the trained SOM lattice, a plot showing an original input vector in 36 dimensions of a selected pixel from the SOM image, the SOM vector that represents the input vector, and the Euclidean distance between the two vectors.

Dryden/Edwards 1994 Thrust-Vectoring Aircraft Fleet - F-18 HARV, X-31, F-16 MATV

NASA Technical Reports Server (NTRS)

1994-01-01

The three thrust-vectoring aircraft at Edwards, California, each capable of flying at extreme angles of attack, cruise over the California desert in formation during flight in March 1994. They are, from left, NASA's F-18 High Alpha Research Vehicle (HARV), flown by the NASA Dryden Flight Research Center; the X-31, flown by the X-31 International Test Organization (ITO) at Dryden; and the Air Force F-16 Multi-Axis Thrust Vectoring (MATV) aircraft. All three aircraft were flown in different programs and were developed independently. The NASA F-18 HARV was a testbed to produce aerodynamic data at high angles of attack to validate computer codes and wind tunnel research. The X-31 was used to study thrust vectoring to enhance close-in air combat maneuvering, while the F-16 MATV was a demonstration of how thrust vectoring could be applied to operational aircraft.

Sorting on STAR. [CDC computer algorithm timing comparison

NASA Technical Reports Server (NTRS)

Stone, H. S.

1978-01-01

Timing comparisons are given for three sorting algorithms written for the CDC STAR computer. One algorithm is Hoare's (1962) Quicksort, which is the fastest or nearly the fastest sorting algorithm for most computers. A second algorithm is a vector version of Quicksort that takes advantage of the STAR's vector operations. The third algorithm is an adaptation of Batcher's (1968) sorting algorithm, which makes especially good use of vector operations but has a complexity of N(log N)-squared as compared with a complexity of N log N for the Quicksort algorithms. In spite of its worse complexity, Batcher's sorting algorithm is competitive with the serial version of Quicksort for vectors up to the largest that can be treated by STAR. Vector Quicksort outperforms the other two algorithms and is generally preferred. These results indicate that unusual instruction sets can introduce biases in program execution time that counter results predicted by worst-case asymptotic complexity analysis.

Approaches to utilize mesenchymal progenitor cells as cellular vehicles.

PubMed

Pereboeva, L; Komarova, S; Mikheeva, G; Krasnykh, V; Curiel, D T

2003-01-01

Mammalian cells represent a novel vector approach for gene delivery that overcomes major drawbacks of viral and nonviral vectors and couples cell therapy with gene delivery. A variety of cell types have been tested in this regard, confirming that the ideal cellular vector system for ex vivo gene therapy has to comply with stringent criteria and is yet to be found. Several properties of mesenchymal progenitor cells (MPCs), such as easy access and simple isolation and propagation procedures, make these cells attractive candidates as cellular vehicles. In the current work, we evaluated the potential utility of MPCs as cellular vectors with the intent to use them in the cancer therapy context. When conventional adenoviral (Ad) vectors were used for MPC transduction, the highest transduction efficiency of MPCs was 40%. We demonstrated that Ad primary-binding receptors were poorly expressed on MPCs, while the secondary Ad receptors and integrins presented in sufficient amounts. By employing Ad vectors with incorporated integrin-binding motifs (Ad5lucRGD), MPC transduction was augmented tenfold, achieving efficient genetic loading of MPCs with reporter and anticancer genes. MPCs expressing thymidine kinase were able to exert a bystander killing effect on the cancer cell line SKOV3ip1 in vitro. In addition, we found that MPCs were able to support Ad replication, and thus can be used as cell vectors to deliver oncolytic viruses. Our results show that MPCs can foster expression of suicide genes or support replication of adenoviruses as potential anticancer therapeutic payloads. These findings are consistent with the concept that MPCs possess key properties that ensure their employment as cellular vehicles and can be used to deliver either therapeutic genes or viruses to tumor sites.

An Overview of the NASA F-18 High Alpha Research Vehicle

NASA Technical Reports Server (NTRS)

Bowers, Albion H.; Pahle, Joseph W.; Wilson, R. Joseph; Flick, Bradley C.; Rood, Richard L.

1996-01-01

This paper gives an overview of the NASA F-18 High Alpha Research Vehicle. The three flight phases of the program are introduced, along with the specific goals and data examples taken during each phase. The aircraft configuration and systems needed to perform the disciplinary and inter-disciplinary research are discussed. The specific disciplines involved with the flight research are introduced, including aerodynamics, controls, propulsion, systems, and structures. Decisions that were made early in the planning of the aircraft project and the results of those decisions are briefly discussed. Each of the three flight phases corresponds to a particular aircraft configuration, and the research dictated the configuration to be flown. The first phase gathered data with the baseline F-18 configuration. The second phase was the thrust-vectoring phase. The third phase used a modified forebody with deployable nose strakes. Aircraft systems supporting these flights included extensive instrumentation systems, integrated research flight controls using flight control hardware and corresponding software, analog interface boxes to control forebody strakes, a thrust-vectoring system using external post-exit vanes around axisymmetric nozzles, a forebody vortex control system with strakes, and backup systems using battery-powered emergency systems and a spin recovery parachute.

Implementation of the Orbital Maneuvering Systems Engine and Thrust Vector Control for the European Service Module

NASA Technical Reports Server (NTRS)

Millard, Jon

2014-01-01

The European Space Agency (ESA) has entered into a partnership with the National Aeronautics and Space Administration (NASA) to develop and provide the Service Module (SM) for the Orion Multipurpose Crew Vehicle (MPCV) Program. The European Service Module (ESM) will provide main engine thrust by utilizing the Space Shuttle Program Orbital Maneuvering System Engine (OMS-E). Thrust Vector Control (TVC) of the OMS-E will be provided by the Orbital Maneuvering System (OMS) TVC, also used during the Space Shuttle Program. NASA will be providing the OMS-E and OMS TVC to ESA as Government Furnished Equipment (GFE) to integrate into the ESM. This presentation will describe the OMS-E and OMS TVC and discuss the implementation of the hardware for the ESM.

Survey of new vector computers: The CRAY 1S from CRAY research; the CYBER 205 from CDC and the parallel computer from ICL - architecture and programming

NASA Technical Reports Server (NTRS)

Gentzsch, W.

1982-01-01

Problems which can arise with vector and parallel computers are discussed in a user oriented context. Emphasis is placed on the algorithms used and the programming techniques adopted. Three recently developed supercomputers are examined and typical application examples are given in CRAY FORTRAN, CYBER 205 FORTRAN and DAP (distributed array processor) FORTRAN. The systems performance is compared. The addition of parts of two N x N arrays is considered. The influence of the architecture on the algorithms and programming language is demonstrated. Numerical analysis of magnetohydrodynamic differential equations by an explicit difference method is illustrated, showing very good results for all three systems. The prognosis for supercomputer development is assessed.

Polynomial interpretation of multipole vectors

NASA Astrophysics Data System (ADS)

Katz, Gabriel; Weeks, Jeff

2004-09-01

Copi, Huterer, Starkman, and Schwarz introduced multipole vectors in a tensor context and used them to demonstrate that the first-year Wilkinson microwave anisotropy probe (WMAP) quadrupole and octopole planes align at roughly the 99.9% confidence level. In the present article, the language of polynomials provides a new and independent derivation of the multipole vector concept. Bézout’s theorem supports an elementary proof that the multipole vectors exist and are unique (up to rescaling). The constructive nature of the proof leads to a fast, practical algorithm for computing multipole vectors. We illustrate the algorithm by finding exact solutions for some simple toy examples and numerical solutions for the first-year WMAP quadrupole and octopole. We then apply our algorithm to Monte Carlo skies to independently reconfirm the estimate that the WMAP quadrupole and octopole planes align at the 99.9% level.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

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

Miltiadis Alamaniotis; Vivek Agarwal

This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are thenmore » inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.« less

Incremental classification learning for anomaly detection in medical images

NASA Astrophysics Data System (ADS)

Giritharan, Balathasan; Yuan, Xiaohui; Liu, Jianguo

2009-02-01

Computer-aided diagnosis usually screens thousands of instances to find only a few positive cases that indicate probable presence of disease.The amount of patient data increases consistently all the time. In diagnosis of new instances, disagreement occurs between a CAD system and physicians, which suggests inaccurate classifiers. Intuitively, misclassified instances and the previously acquired data should be used to retrain the classifier. This, however, is very time consuming and, in some cases where dataset is too large, becomes infeasible. In addition, among the patient data, only a small percentile shows positive sign, which is known as imbalanced data.We present an incremental Support Vector Machines(SVM) as a solution for the class imbalance problem in classification of anomaly in medical images. The support vectors provide a concise representation of the distribution of the training data. Here we use bootstrapping to identify potential candidate support vectors for future iterations. Experiments were conducted using images from endoscopy videos, and the sensitivity and specificity were close to that of SVM trained using all samples available at a given incremental step with significantly improved efficiency in training the classifier.

Agricultural mapping using Support Vector Machine-Based Endmember Extraction (SVM-BEE)

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

Archibald, Richard K; Filippi, Anthony M; Bhaduri, Budhendra L

Extracting endmembers from remotely sensed images of vegetated areas can present difficulties. In this research, we applied a recently developed endmember-extraction algorithm based on Support Vector Machines (SVMs) to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically and effectively estimate endmembers; synthetic data and a geologicmore » scene were previously analyzed. Here we compared the efficacies of the SVM-BEE and N-FINDR algorithms in extracting endmembers from a predominantly agricultural scene. SVM-BEE was able to estimate vegetation and other endmembers for all classes in the image, which N-FINDR failed to do. Classifications based on SVM-BEE endmembers were markedly more accurate compared with those based on N-FINDR endmembers.« less

Exploring the capabilities of support vector machines in detecting silent data corruptions

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

Subasi, Omer; Di, Sheng; Bautista-Gomez, Leonardo

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. Here in this paper, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based onmore » different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.« less

Exploring the capabilities of support vector machines in detecting silent data corruptions

DOE PAGES

Subasi, Omer; Di, Sheng; Bautista-Gomez, Leonardo; ...

2018-02-01

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. Here in this paper, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based onmore » different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.« less

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

NASA Astrophysics Data System (ADS)

Su, Lihong

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.

Clifford support vector machines for classification, regression, and recurrence.

PubMed

Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

2010-11-01

This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.

Application of support vector machines for copper potential mapping in Kerman region, Iran

NASA Astrophysics Data System (ADS)

Shabankareh, Mahdi; Hezarkhani, Ardeshir

2017-04-01

The first step in systematic exploration studies is mineral potential mapping, which involves classification of the study area to favorable and unfavorable parts. Support vector machines (SVM) are designed for supervised classification based on statistical learning theory. This method named support vector classification (SVC). This paper describes SVC model, which combine exploration data in the regional-scale for copper potential mapping in Kerman copper bearing belt in south of Iran. Data layers or evidential maps were in six datasets namely lithology, tectonic, airborne geophysics, ferric alteration, hydroxide alteration and geochemistry. The SVC modeling result selected 2220 pixels as favorable zones, approximately 25 percent of the study area. Besides, 66 out of 86 copper indices, approximately 78.6% of all, were located in favorable zones. Other main goal of this study was to determine how each input affects favorable output. For this purpose, the histogram of each normalized input data to its favorable output was drawn. The histograms of each input dataset for favorable output showed that each information layer had a certain pattern. These patterns of SVC results could be considered as regional copper exploration characteristics.

Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain).

PubMed

García Nieto, P J; Alonso Fernández, J R; de Cos Juez, F J; Sánchez Lasheras, F; Díaz Muñiz, C

2013-04-01

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational waters. As a result, anticipate its presence is a matter of importance to prevent risks. The aim of this study is to use a hybrid approach based on support vector regression (SVR) in combination with genetic algorithms (GAs), known as a genetic algorithm support vector regression (GA-SVR) model, in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). The GA-SVR approach is aimed at highly nonlinear biological problems with sharp peaks and the tests carried out proved its high performance. Some physical-chemical parameters have been considered along with the biological ones. The results obtained are two-fold. In the first place, the significance of each biological and physical-chemical variable on the cyanotoxins presence in the reservoir is determined with success. Finally, a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. Copyright © 2013 Elsevier Inc. All rights reserved.

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents

NASA Astrophysics Data System (ADS)

Valizadeh, Maryam; Sohrabi, Mahmoud Reza

2018-03-01

In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several synthetic mixtures were analyzed for validating the proposed methods. At first, neural network time series, which one type of network from the artificial neural network was employed and its efficiency was evaluated. Afterwards, the radial basis network was applied as another neural network. Results showed that the performance of this method is suitable for predicting. Finally, support vector regression was proposed to construct the Zilomole prediction model. Also, root mean square error (RMSE) and mean recovery (%) were calculated for SVR method. Moreover, the proposed methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them. Also, the effect of interferences was investigated in spike solutions.

Bundles over nearly-Kahler homogeneous spaces in heterotic string theory

NASA Astrophysics Data System (ADS)

Klaput, Michael; Lukas, Andre; Matti, Cyril

2011-09-01

We construct heterotic vacua based on six-dimensional nearly-Kahler homogeneous manifolds and non-trivial vector bundles thereon. Our examples are based on three specific group coset spaces. It is shown how to construct line bundles over these spaces, compute their properties and build up vector bundles consistent with supersymmetry and anomaly cancelation. It turns out that the most interesting coset is SU(3)/U(1)2. This space supports a large number of vector bundles which lead to consistent heterotic vacua, some of them with three chiral families.

Implementation of a Personal Computer Based Parameter Estimation Program

DTIC Science & Technology

1992-03-01

if necessary and identify by biock nunrbet) FEILD GROUP SUBGROUP Il’arunietar uetinkatlUln 19 ABSTRACT (continue on reverse it necessary and identity...model constant ix L,M,N X,Y,Z moment components Lp: •sbc.’.• T’ = sb C . r, - 2 V C, , L, = _sb 2 C 2V C L8,=qsbC 1 , Lw Scale of the turbulence M Vector ...u,v,w X,Y,Z velocity components V Vector velocity V Magnitude of velocity vector w9 Z velocity due to gust X.. x-distance to normal acclerometer X.P x

Behaviour of mathematics and physics students in solving problem of Vector-Physics context

NASA Astrophysics Data System (ADS)

Sardi; Rizal, M.; Mansyur, J.

2018-04-01

This research aimed to describe behaviors of mathematics and physics students in solving problem of the vector concept in physics context. The subjects of the research were students who enrolled in Mathematics Education Study Program and Physics Education Study Program of FKIP Universitas Tadulako. The selected participants were students who received the highest score in vector fundamental concept test in each study program. The data were collected through thinking-aloud activity followed by an interview. The steps of data analysis included data reduction, display, and conclusion drawing. The credibility of the data was tested using a triangulation method. Based on the data analysis, it can be concluded that the two groups of students did not show fundamental differences in problem-solving behavior, especially in the steps of understanding the problem (identifying, collecting and analyzing facts and information), planning (looking for alternative strategies) and conducting the alternative strategy. The two groups were differ only in the evaluation aspect. In contrast to Physics students who evaluated their answer, mathematics students did not conducted an evaluation activity on their work. However, the difference was not caused by the differences in background knowledge.

Minimizing the RMS surface distortions from gravity loadings of the 34-m HA-DEC antenna for deep space missions

NASA Technical Reports Server (NTRS)

Katow, S. M.

1979-01-01

The computer analysis of the 34-m HA-DEC antenna by the IDEAS program provided the rms distortions of the surface panels support points for full gravity loadings in the three directions of the basic coordinate system of the computer model. The rms distortions for the gravity vector not in line with any of the three basic directions were solved and contour plotted starting from three surface panels setting declination angle. By inspections of the plots, it was concluded that the setting or rigging angle of -15 degrees declination minimized the rms distortions for sky coverage of plus or minus 22 declination angles to 10 degrees of ground mask.

Parametric Model of an Aerospike Rocket Engine

NASA Technical Reports Server (NTRS)

Korte, J. J.

2000-01-01

A suite of computer codes was assembled to simulate the performance of an aerospike engine and to generate the engine input for the Program to Optimize Simulated Trajectories. First an engine simulator module was developed that predicts the aerospike engine performance for a given mixture ratio, power level, thrust vectoring level, and altitude. This module was then used to rapidly generate the aerospike engine performance tables for axial thrust, normal thrust, pitching moment, and specific thrust. Parametric engine geometry was defined for use with the engine simulator module. The parametric model was also integrated into the iSIGHTI multidisciplinary framework so that alternate designs could be determined. The computer codes were used to support in-house conceptual studies of reusable launch vehicle designs.

Parametric Model of an Aerospike Rocket Engine

NASA Technical Reports Server (NTRS)

Korte, J. J.

2000-01-01

A suite of computer codes was assembled to simulate the performance of an aerospike engine and to generate the engine input for the Program to Optimize Simulated Trajectories. First an engine simulator module was developed that predicts the aerospike engine performance for a given mixture ratio, power level, thrust vectoring level, and altitude. This module was then used to rapidly generate the aerospike engine performance tables for axial thrust, normal thrust, pitching moment, and specific thrust. Parametric engine geometry was defined for use with the engine simulator module. The parametric model was also integrated into the iSIGHT multidisciplinary framework so that alternate designs could be determined. The computer codes were used to support in-house conceptual studies of reusable launch vehicle designs.

A Mathematical and Sociological Analysis of Google Search Algorithm

DTIC Science & Technology

2013-01-16

through the collective intelligence of the web to determine a page’s importance. Let v be a vector of RN with N ≥ 8 billion. Any unit vector in RN is...scrolled up by some artifical hits. Aknowledgment: The authors would like to thank Dr. John Lavery for his encouragement and support which enable them to

Automated Creation of Labeled Pointcloud Datasets in Support of Machine-Learning Based Perception

DTIC Science & Technology

2017-12-01

computationally intensive 3D vector math and took more than ten seconds to segment a single LIDAR frame from the HDL-32e with the Dell XPS15 9650’s Intel...Core i7 CPU. Depth Clustering avoids the computationally intensive 3D vector math of Euclidean Clustering-based DON segmentation and, instead

EMMA: An Extensible Mammalian Modular Assembly Toolkit for the Rapid Design and Production of Diverse Expression Vectors.

PubMed

Martella, Andrea; Matjusaitis, Mantas; Auxillos, Jamie; Pollard, Steven M; Cai, Yizhi

2017-07-21

Mammalian plasmid expression vectors are critical reagents underpinning many facets of research across biology, biomedical research, and the biotechnology industry. Traditional cloning methods often require laborious manual design and assembly of plasmids using tailored sequential cloning steps. This process can be protracted, complicated, expensive, and error-prone. New tools and strategies that facilitate the efficient design and production of bespoke vectors would help relieve a current bottleneck for researchers. To address this, we have developed an extensible mammalian modular assembly kit (EMMA). This enables rapid and efficient modular assembly of mammalian expression vectors in a one-tube, one-step golden-gate cloning reaction, using a standardized library of compatible genetic parts. The high modularity, flexibility, and extensibility of EMMA provide a simple method for the production of functionally diverse mammalian expression vectors. We demonstrate the value of this toolkit by constructing and validating a range of representative vectors, such as transient and stable expression vectors (transposon based vectors), targeting vectors, inducible systems, polycistronic expression cassettes, fusion proteins, and fluorescent reporters. The method also supports simple assembly combinatorial libraries and hierarchical assembly for production of larger multigenetic cargos. In summary, EMMA is compatible with automated production, and novel genetic parts can be easily incorporated, providing new opportunities for mammalian synthetic biology.

Effects of Cucumber mosaic virus infection on vector and non-vector herbivores of squash.

PubMed

Mauck, Kerry E; De Moraes, Consuelo M; Mescher, Mark C

2010-11-01

Plant chemicals mediating interactions with insect herbivores seem a likely target for manipulation by insectvectored plant pathogens. Yet, little is currently known about the chemical ecology of insect-vectored diseases or their effects on the ecology of vector and nonvector insects. We recently reported that a widespread plant pathogen, Cucumber mosaic virus (CMV), greatly reduces the quality of host-plants (squash) for aphid vectors, but that aphids are nevertheless attracted to the odors of infected plants-which exhibit elevated emissions of a volatile blend otherwise similar to the odor of healthy plants. This finding suggests that exaggerating existing host-location cues can be a viable vector attraction strategy for pathogens that otherwise reduce host quality for vectors. Here we report additional data regarding the effects of CMV infection on plant interactions with a common nonvector herbivore, the squash bug, Anasa tristis, which is a pest in this system. We found that adult A. tristis females preferred to oviposit on healthy plants in the field, and that healthy plants supported higher populations of nymphs. Collectively, our recent findings suggest that CMV-induced changes in host plant chemistry influence the behavior of both vector and non-vector herbivores, with significant implications both for disease spread and for broader community-level interactions.

Scorebox extraction from mobile sports videos using Support Vector Machines

NASA Astrophysics Data System (ADS)

Kim, Wonjun; Park, Jimin; Kim, Changick

2008-08-01

Scorebox plays an important role in understanding contents of sports videos. However, the tiny scorebox may give the small-display-viewers uncomfortable experience in grasping the game situation. In this paper, we propose a novel framework to extract the scorebox from sports video frames. We first extract candidates by using accumulated intensity and edge information after short learning period. Since there are various types of scoreboxes inserted in sports videos, multiple attributes need to be used for efficient extraction. Based on those attributes, the optimal information gain is computed and top three ranked attributes in terms of information gain are selected as a three-dimensional feature vector for Support Vector Machines (SVM) to distinguish the scorebox from other candidates, such as logos and advertisement boards. The proposed method is tested on various videos of sports games and experimental results show the efficiency and robustness of our proposed method.

Discontinuity Detection in the Shield Metal Arc Welding Process

PubMed Central

Cocota, José Alberto Naves; Garcia, Gabriel Carvalho; da Costa, Adilson Rodrigues; de Lima, Milton Sérgio Fernandes; Rocha, Filipe Augusto Santos; Freitas, Gustavo Medeiros

2017-01-01

This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors—a microphone and piezoelectric—that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system’s high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries. PMID:28489045

Discontinuity Detection in the Shield Metal Arc Welding Process.

PubMed

Cocota, José Alberto Naves; Garcia, Gabriel Carvalho; da Costa, Adilson Rodrigues; de Lima, Milton Sérgio Fernandes; Rocha, Filipe Augusto Santos; Freitas, Gustavo Medeiros

2017-05-10

This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors-a microphone and piezoelectric-that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system's high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries.

Analysis of Particle Content of Recombinant Adeno-Associated Virus Serotype 8 Vectors by Ion-Exchange Chromatography

PubMed Central

Lock, Martin; Alvira, Mauricio R.

2012-01-01

Abstract Advances in adeno-associated virus (AAV)-mediated gene therapy have brought the possibility of commercial manufacturing of AAV vectors one step closer. To realize this prospect, a parallel effort with the goal of ever-increasing sophistication for AAV vector production technology and supporting assays will be required. Among the important release assays for a clinical gene therapy product, those monitoring potentially hazardous contaminants are most critical for patient safety. A prominent contaminant in many AAV vector preparations is vector particles lacking a genome, which can substantially increase the dose of AAV capsid proteins and lead to possible unwanted immunological consequences. Current methods to determine empty particle content suffer from inconsistency, are adversely affected by contaminants, or are not applicable to all serotypes. Here we describe the development of an ion-exchange chromatography-based assay that permits the rapid separation and relative quantification of AAV8 empty and full vector particles through the application of shallow gradients and a strong anion-exchange monolith chromatography medium. PMID:22428980

Vector-borne diseases in Haiti: a review.

PubMed

Ben-Chetrit, Eli; Schwartz, Eli

2015-01-01

Haiti lies on the western third of the island of Hispaniola in the Caribbean, and is one of the poorest nations in the Western hemisphere. Haiti attracts a lot of medical attention and support due to severe natural disasters followed by disastrous health consequences. Vector-borne infections are still prevalent there with some unique aspects comparing it to Latin American countries and other Caribbean islands. Although vector-borne viral diseases such as dengue and recently chikungunya can be found in many of the Caribbean islands, including Haiti, there is an apparent distinction of the vector-borne parasitic diseases. Contrary to neighboring Carribbean islands, Haiti is highly endemic for malaria, lymphatic filariasis and mansonellosis. Affected by repeat natural disasters, poverty and lack of adequate infrastructure, control of transmission within Haiti and prevention of dissemination of vector-borne pathogens to other regions is challenging. In this review we summarize some aspects concerning diseases caused by vector-borne pathogens in Haiti. Copyright © 2015 Elsevier Ltd. All rights reserved.

FRAGSTATS: spatial pattern analysis program for quantifying landscape structure.

Treesearch

Kevin McGarigal; Barbara J. Marks

1995-01-01

This report describes a program, FRAGSTATS, developed to quantify landscape structure. FRAGSTATS offers a comprehensive choice of landscape metrics and was designed to be as versatile as possible. The program is almost completely automated and thus requires little technical training. Two separate versions of FRAGSTATS exist: one for vector images and one for raster...

The Arthropod-Borne Animal Diseases Research Laboratory: Research Program: Update and Current Status

USDA-ARS?s Scientific Manuscript database

The ABADRL has three 5-year project plans under two ARS National Research Programs. One project plan under the Animal Health National Program is entitled “Countermeasures to control and eradicate Rift Valley fever (RVF)”. Research objectives in this plan are 1) to determine the vector competence of ...

Improving the Transduction of Bone Marrow–Derived Cells with an Integrase-Defective Lentiviral Vector

PubMed Central

Pay, S. Louise; Qi, Xiaoping; Willard, Jeffrey F.; Godoy, Juliana; Sankhavaram, Kavya; Horton, Ranier; Mitter, Sayak K.; Quigley, Judith L.; Chang, Lung-Ji; Grant, Maria B.; Boulton, Michael E.

2018-01-01

In lentiviral vector (LV) applications where transient transgene expression is sufficient, integrase-defective lentiviral vectors (IDLVs) are beneficial for reducing the potential for off-target effects associated with insertional mutagenesis. It was previously demonstrated that human RPE65 mRNA expression from an integrating lentiviral vector (ILV) induces endogenous Rpe65 and Cralbp mRNA expression in murine bone marrow–derived cells (BMDCs), initiating programming of the cells to retinal pigment epithelium (RPE)-like cells. These cells regenerate RPE in retinal degeneration models when injected systemically. As transient expression of RPE65 is sufficient to activate endogenous RPE-associated genes for programming BMDCs, use of an ILV is an unnecessary risk. In this study, an IDLV expressing RPE65 (IDLV3-RPE65) was generated. Transduction with IDLV3-RPE65 is less efficient than the integrating vector (ILV3-RPE65). Therefore, IDLV3-RPE65 transduction was enhanced with a combination of preloading 20 × -concentrated viral supernatant on RetroNectin at a multiplicity of infection of 50 and transduction of BMDCs by low-speed centrifugation. RPE65 mRNA levels increased from ∼12-fold to ∼25-fold (p < 0.05) after modification of the IDLV3-RPE65 transduction protocol, achieving expression similar to the ∼27-fold (p < 0.05) increase observed with ILV3-RPE65. Additionally, the study shows that the same preparation of RetroNectin can be used to coat up to three wells with no reduction in transduction. Critically, IDLV3-RPE65 transduction initiates endogenous Rpe65 mRNA expression in murine BMDCs and Cralbp/CRALBP mRNA in both murine and human BMDCs, similar to expression observed in ILV3-RPE65-transduced cells. Systemic administration of ILV3-RPE65 or IDLV3-RPE65 programmed BMDCs in a mouse model of retinal degeneration is sufficient to retain visual function and reduce retinal degeneration compared to mice receiving no treatment or naïve BMDC. It is concluded that IDLV3-RPE65 is appropriate for programming BMDCs to RPE-like cells. PMID:29160102

Assessment of the relationship of government spending on social assistance programs with Brazilian macroeconomic variables

NASA Astrophysics Data System (ADS)

de Senna, Viviane; Souza, Adriano Mendonça

2016-11-01

Since the 1988 Federal Constitution social assistance has become a duty of the State and a right to everyone, guaranteeing the population a dignified life. To ensure these rights federal government has created programs that can supply the main needs of people in extreme poverty. Among the programs that provide social assistance to the population, the best known are the ;Bolsa Família; Program - PBF and the Continuous Cash Benefit - Continuous Cash Benefit - BPC. This research's main purpose is to analyze the relationship between the main macroeconomic variables and the Federal government spending on social welfare policy in the period from January 2004 to August 2014. The used methodologies are the Vector auto regression model - VAR and Error Correction Vector - VEC. The conclusion, was that there is a meaningful relationship between macroeconomic variables and social assistance programs. This indicates that if the government takes a more abrupt resolution in changing the existing programs it will result in fluctuations in the main macroeconomic variables interfering with the stability of Brazilian domestic economy up to twelve months.

Localization of U(1) gauge vector field on flat branes with five-dimension (asymptotic) AdS5 spacetime

NASA Astrophysics Data System (ADS)

Zhao, Zhen-Hua; Xie, Qun-Ying

2018-05-01

In order to localize U(1) gauge vector field on Randall-Sundrum-like braneworld model with infinite extra dimension, we propose a new kind of non-minimal coupling between the U(1) gauge field and the gravity. We propose three kinds of coupling methods and they all support the localization of zero mode. In addition, one of them can support the localization of massive modes. Moreover, the massive tachyonic modes can be excluded. And our method can be used not only in the thin braneword models but also in the thick ones.

Support vector machine multiuser receiver for DS-CDMA signals in multipath channels.

PubMed

Chen, S; Samingan, A K; Hanzo, L

2001-01-01

The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.

Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

NASA Astrophysics Data System (ADS)

Shastri, Niket; Pathak, Kamlesh

2018-05-01

The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

Implementation of support vector machine for classification of speech marked hijaiyah letters based on Mel frequency cepstrum coefficient feature extraction

NASA Astrophysics Data System (ADS)

Adhi Pradana, Wisnu; Adiwijaya; Novia Wisesty, Untari

2018-03-01

Support Vector Machine or commonly called SVM is one method that can be used to process the classification of a data. SVM classifies data from 2 different classes with hyperplane. In this study, the system was built using SVM to develop Arabic Speech Recognition. In the development of the system, there are 2 kinds of speakers that have been tested that is dependent speakers and independent speakers. The results from this system is an accuracy of 85.32% for speaker dependent and 61.16% for independent speakers.

What makes community engagement effective?: Lessons from the Eliminate Dengue Program in Queensland Australia.

PubMed

Kolopack, Pamela A; Parsons, Janet A; Lavery, James V

2015-04-01

Worldwide, more than 40% of the population is at risk from dengue and recent estimates suggest that up to 390 million dengue infections are acquired every year. The Eliminate Dengue (ED) Program is investigating the use of Wolbachia-infected, transmission-compromised, mosquitoes to reduce dengue transmission. Previous introductions of genetically-modified strategies for dengue vector control have generated controversy internationally by inadequately engaging host communities. Community Engagement (CE) was a key component of the ED Program's initial open release trials in Queensland Australia. Their approach to CE was perceived as effective by the ED team's senior leadership, members of its CE team, and by its funders, but if and why this was the case was unclear. We conducted a qualitative case study of the ED Program's approach to CE to identify and critically examine its components, and to explain whether and how these efforts contributed to the support received by stakeholders. In-depth semi-structured interviews were conducted with 24 participants with a range of experiences and perspectives related to the ED Program's CE activities. Our analytic approach combined techniques of grounded theory and qualitative description. The ED Program's approach to CE reflected four foundational features: 1) enabling conditions; 2) leadership; 3) core commitments and guiding values; and 4) formative social science research. These foundations informed five key operational practices: 1) building the CE team; 2) integrating CE into management practices; 3) discerning the community of stakeholders; 4) establishing and maintaining a presence in the community; and 5) socializing the technology and research strategy. We also demonstrate how these practices contributed to stakeholders' willingness to support the trials. Our case study has identified, and explained the functional relationships among, the critical features of the ED Program's approach to CE. It has also illuminated how these features were meaningful to stakeholders and contributed to garnering support within the host communities for the open-release trials. Our findings reveal how translating ethical intentions into effective action is more socially complex than is currently reflected in the CE literature. Because our case study delineates the critical features of the ED Program's approach to CE, it can serve as a framework for other programs to follow when designing their own strategies. And because the findings outline a theory of change for CE, it can also serve as a starting point for developing an evaluation framework for CE.

Under-Expression of Chemosensory Genes in Domiciliary Bugs of the Chagas Disease Vector Triatoma brasiliensis

PubMed Central

Marchant, Axelle; Mougel, Florence; Jacquin-Joly, Emmanuelle; Costa, Jane; Almeida, Carlos Eduardo; Harry, Myriam

2016-01-01

Background In Latin America, the bloodsucking bugs Triatominae are vectors of Trypanosoma cruzi, the parasite that causes Chagas disease. Chemical elimination programs have been launched to control Chagas disease vectors. However, the disease persists because native vectors from sylvatic habitats are able to (re)colonize houses—a process called domiciliation. Triatoma brasiliensis is one example. Because the chemosensory system allows insects to interact with their environment and plays a key role in insect adaption, we conducted a descriptive and comparative study of the chemosensory transcriptome of T. brasiliensis samples from different ecotopes. Methodology/Principal Finding In a reference transcriptome built using de novo assembly, we found transcripts encoding 27 odorant-binding proteins (OBPs), 17 chemosensory proteins (CSPs), 3 odorant receptors (ORs), 5 transient receptor potential channel (TRPs), 1 sensory neuron membrane protein (SNMPs), 25 takeout proteins, 72 cytochrome P450s, 5 gluthatione S-transferases, and 49 cuticular proteins. Using protein phylogenies, we showed that most of the OBPs and CSPs for T. brasiliensis had well supported orthologs in the kissing bug Rhodnius prolixus. We also showed a higher number of these genes within the bloodsucking bugs and more generally within all Hemipterans compared to the other species in the super-order Paraneoptera. Using both DESeq2 and EdgeR software, we performed differential expression analyses between samples of T. brasiliensis, taking into account their environment (sylvatic, peridomiciliary and domiciliary) and sex. We also searched clusters of co-expressed contigs using HTSCluster. Among differentially expressed (DE) contigs, most were under-expressed in the chemosensory organs of the domiciliary bugs compared to the other samples and in females compared to males. We clearly identified DE genes that play a role in the chemosensory system. Conclusion/Significance Chemosensory genes could be good candidates for genes that contribute to adaptation or plastic rearrangement to an anthropogenic system. The domiciliary environment probably includes less diversity of xenobiotics and probably has more stable abiotic parameters than do sylvatic and peridomiciliary environments. This could explain why both detoxification and cuticle protein genes are less expressed in domiciliary bugs. Understanding the molecular basis for how vectors adapt to human dwellings may reveal new tools to control disease vectors; for example, by disrupting chemical communication. PMID:27792774

Under-Expression of Chemosensory Genes in Domiciliary Bugs of the Chagas Disease Vector Triatoma brasiliensis.

PubMed

Marchant, Axelle; Mougel, Florence; Jacquin-Joly, Emmanuelle; Costa, Jane; Almeida, Carlos Eduardo; Harry, Myriam

2016-10-01

In Latin America, the bloodsucking bugs Triatominae are vectors of Trypanosoma cruzi, the parasite that causes Chagas disease. Chemical elimination programs have been launched to control Chagas disease vectors. However, the disease persists because native vectors from sylvatic habitats are able to (re)colonize houses-a process called domiciliation. Triatoma brasiliensis is one example. Because the chemosensory system allows insects to interact with their environment and plays a key role in insect adaption, we conducted a descriptive and comparative study of the chemosensory transcriptome of T. brasiliensis samples from different ecotopes. In a reference transcriptome built using de novo assembly, we found transcripts encoding 27 odorant-binding proteins (OBPs), 17 chemosensory proteins (CSPs), 3 odorant receptors (ORs), 5 transient receptor potential channel (TRPs), 1 sensory neuron membrane protein (SNMPs), 25 takeout proteins, 72 cytochrome P450s, 5 gluthatione S-transferases, and 49 cuticular proteins. Using protein phylogenies, we showed that most of the OBPs and CSPs for T. brasiliensis had well supported orthologs in the kissing bug Rhodnius prolixus. We also showed a higher number of these genes within the bloodsucking bugs and more generally within all Hemipterans compared to the other species in the super-order Paraneoptera. Using both DESeq2 and EdgeR software, we performed differential expression analyses between samples of T. brasiliensis, taking into account their environment (sylvatic, peridomiciliary and domiciliary) and sex. We also searched clusters of co-expressed contigs using HTSCluster. Among differentially expressed (DE) contigs, most were under-expressed in the chemosensory organs of the domiciliary bugs compared to the other samples and in females compared to males. We clearly identified DE genes that play a role in the chemosensory system. Chemosensory genes could be good candidates for genes that contribute to adaptation or plastic rearrangement to an anthropogenic system. The domiciliary environment probably includes less diversity of xenobiotics and probably has more stable abiotic parameters than do sylvatic and peridomiciliary environments. This could explain why both detoxification and cuticle protein genes are less expressed in domiciliary bugs. Understanding the molecular basis for how vectors adapt to human dwellings may reveal new tools to control disease vectors; for example, by disrupting chemical communication.

The NRL Program on Electroactive Polymers.

DTIC Science & Technology

1980-09-15

cell of a point in an aggregate involves selecting the smallest cell formed by planes perpendicularly bisecting all the point to neighbor vectors . Such...plane perpendicular to the interatomic vector is located nearer the smaller atom by bisecting the distance between the sur- faces of spheres whose...density waves (and consequent novel excitations such as solitons (6)). The physical structure as well as the chemical bonding of such polymeric

Analysis Test of Understanding of Vectors with the Three-Parameter Logistic Model of Item Response Theory and Item Response Curves Technique

ERIC Educational Resources Information Center

Rakkapao, Suttida; Prasitpong, Singha; Arayathanitkul, Kwan

2016-01-01

This study investigated the multiple-choice test of understanding of vectors (TUV), by applying item response theory (IRT). The difficulty, discriminatory, and guessing parameters of the TUV items were fit with the three-parameter logistic model of IRT, using the parscale program. The TUV ability is an ability parameter, here estimated assuming…

Planning actions in robot automated operations

NASA Technical Reports Server (NTRS)

Das, A.

1988-01-01

Action planning in robot automated operations requires intelligent task level programming. Invoking intelligence necessiates a typical blackboard based architecture, where, a plan is a vector between the start frame and the goal frame. This vector is composed of partially ordered bases. A partial ordering of bases presents good and bad sides in action planning. Partial ordering demands the use of a temporal data base management system.

Wolbachia Effects on Rift Valley Virus Infection in Culex tarsalis Mosquitoes

DTIC Science & Technology

2017-04-25

Vector-Borne Disease Section, Division of Communicable Diseases, Center 14" for Infectious Diseases, California Department of Public Health ...Wolbachia. 44" 45" Author Summary 46" An integrated vector management program utilizes several practices, including pesticide 47" application and source...mosquitoes and can block pathogen transmission to humans . 51" Additionally, Wolbachia is maternally-inherited, allowing it to spread quickly through

Clearance of Escherichia coli After Intravenous Inoculation in Broiler Breeder Pullets Fed Skip a day, Every Day in the feeder and Every Day on the Litter

USDA-ARS?s Scientific Manuscript database

The effect of feeding programs on the time of clearance of Escherichia coli in broiler breeder pullets was investigated. Broiler breeder pullets from a single grandparent flock were in ovo-vaccinated at 19 d of incubation with a vector HVT (vHVT) vector HVT + Infectious bursal disease (IBD) vaccine....

Digital photogrammetry at the U.S. Geological Survey

USGS Publications Warehouse

Greve, Clifford W.

1995-01-01

The U.S. Geological Survey is converting its primary map production and revision operations to use digital photogrammetric techniques. The primary source of data for these operations is the digital orthophoto quadrangle derived from National Aerial Photography Program images. These digital orthophotos are used on workstations that permit comparison of existing vector and raster data with the orthophoto and interactive collection and revision of the vector data.

Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model

NASA Astrophysics Data System (ADS)

Muduli, Pradyut; Das, Sarat

2014-06-01

This paper discusses the evaluation of liquefaction potential of soil based on standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). The liquefaction classification accuracy (94.19%) of the developed liquefaction index (LI) model is found to be better than that of available artificial neural network (ANN) model (88.37%) and at par with the available support vector machine (SVM) model (94.19%) on the basis of the testing data. Further, an empirical equation is presented using MGGP to approximate the unknown limit state function representing the cyclic resistance ratio (CRR) of soil based on developed LI model. Using an independent database of 227 cases, the overall rates of successful prediction of occurrence of liquefaction and non-liquefaction are found to be 87, 86, and 84% by the developed MGGP based model, available ANN and the statistical models, respectively, on the basis of calculated factor of safety (F s) against the liquefaction occurrence.

DataWarrior: an open-source program for chemistry aware data visualization and analysis.

PubMed

Sander, Thomas; Freyss, Joel; von Korff, Modest; Rufener, Christian

2015-02-23

Drug discovery projects in the pharmaceutical industry accumulate thousands of chemical structures and ten-thousands of data points from a dozen or more biological and pharmacological assays. A sufficient interpretation of the data requires understanding, which molecular families are present, which structural motifs correlate with measured properties, and which tiny structural changes cause large property changes. Data visualization and analysis software with sufficient chemical intelligence to support chemists in this task is rare. In an attempt to contribute to filling the gap, we released our in-house developed chemistry aware data analysis program DataWarrior for free public use. This paper gives an overview of DataWarrior's functionality and architecture. Exemplarily, a new unsupervised, 2-dimensional scaling algorithm is presented, which employs vector-based or nonvector-based descriptors to visualize the chemical or pharmacophore space of even large data sets. DataWarrior uses this method to interactively explore chemical space, activity landscapes, and activity cliffs.

Entomological Opportunities and Challenges for Sustainable Viticulture in a Global Market.

PubMed

Daane, Kent M; Vincent, Charles; Isaacs, Rufus; Ioriatti, Claudio

2018-01-07

Viticulture has experienced dramatic global growth in acreage and value. As the international exchange of goods has increased, so too has the market demand for sustainably produced products. Both elements redefine the entomological challenges posed to viticulture and have stimulated significant advances in arthropod pest control programs. Vineyard managers on all continents are increasingly combating invasive species, resulting in the adoption of novel insecticides, semiochemicals, and molecular tools to support sustainable viticulture. At the local level, vineyard management practices consider factors such as the surrounding natural ecosystem, risk to fish populations, and air quality. Coordinated multinational responses to pest invasion have been highly effective and have, for example, resulted in eradication of the moth Lobesia botrana from California vineyards, a pest found in 2009 and eradicated by 2016. At the global level, the shared pests and solutions for their suppression will play an increasing role in delivering internationally sensitive pest management programs that respond to invasive pests, climate change, novel vector and pathogen relationships, and pesticide restrictions.

Computational approaches for the classification of seed storage proteins.

PubMed

Radhika, V; Rao, V Sree Hari

2015-07-01

Seed storage proteins comprise a major part of the protein content of the seed and have an important role on the quality of the seed. These storage proteins are important because they determine the total protein content and have an effect on the nutritional quality and functional properties for food processing. Transgenic plants are being used to develop improved lines for incorporation into plant breeding programs and the nutrient composition of seeds is a major target of molecular breeding programs. Hence, classification of these proteins is crucial for the development of superior varieties with improved nutritional quality. In this study we have applied machine learning algorithms for classification of seed storage proteins. We have presented an algorithm based on nearest neighbor approach for classification of seed storage proteins and compared its performance with decision tree J48, multilayer perceptron neural (MLP) network and support vector machine (SVM) libSVM. The model based on our algorithm has been able to give higher classification accuracy in comparison to the other methods.

Energy-exchange collisions of dark-bright-bright vector solitons.

PubMed

Radhakrishnan, R; Manikandan, N; Aravinthan, K

2015-12-01

We find a dark component guiding the practically interesting bright-bright vector one-soliton to two different parametric domains giving rise to different physical situations by constructing a more general form of three-component dark-bright-bright mixed vector one-soliton solution of the generalized Manakov model with nine free real parameters. Moreover our main investigation of the collision dynamics of such mixed vector solitons by constructing the multisoliton solution of the generalized Manakov model with the help of Hirota technique reveals that the dark-bright-bright vector two-soliton supports energy-exchange collision dynamics. In particular the dark component preserves its initial form and the energy-exchange collision property of the bright-bright vector two-soliton solution of the Manakov model during collision. In addition the interactions between bound state dark-bright-bright vector solitons reveal oscillations in their amplitudes. A similar kind of breathing effect was also experimentally observed in the Bose-Einstein condensates. Some possible ways are theoretically suggested not only to control this breathing effect but also to manage the beating, bouncing, jumping, and attraction effects in the collision dynamics of dark-bright-bright vector solitons. The role of multiple free parameters in our solution is examined to define polarization vector, envelope speed, envelope width, envelope amplitude, grayness, and complex modulation of our solution. It is interesting to note that the polarization vector of our mixed vector one-soliton evolves in sphere or hyperboloid depending upon the initial parametric choices.

Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System.

PubMed

Jaya, T; Dheeba, J; Singh, N Albert

2015-12-01

Diabetic retinopathy is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. This paper presents an expert decision-making system designed using a fuzzy support vector machine (FSVM) classifier to detect hard exudates in fundus images. The optic discs in the colour fundus images are segmented to avoid false alarms using morphological operations and based on circular Hough transform. To discriminate between the exudates and the non-exudates pixels, colour and texture features are extracted from the images. These features are given as input to the FSVM classifier. The classifier analysed 200 retinal images collected from diabetic retinopathy screening programmes. The tests made on the retinal images show that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and features sets, the area under the receiver operating characteristic curve reached 0.9606, which corresponds to a sensitivity of 94.1% with a specificity of 90.0%. The results suggest that detecting hard exudates using FSVM contribute to computer-assisted detection of diabetic retinopathy and as a decision support system for ophthalmologists.

Repeat Transduction in the Mouse Lung by Using Adeno-Associated Virus Vectors with Different Serotypes

PubMed Central

Halbert, Christine L.; Rutledge, Elizabeth A.; Allen, James M.; Russell, David W.; Miller, A. Dusty

2000-01-01

Vectors derived from adeno-associated virus type 2 (AAV2) promote gene transfer and expression in the lung; however, we have found that while gene expression can persist for at least 8 months in mice, it was reduced dramatically in rabbits over a period of 2 months. The efficiency and persistence of AAV2-mediated gene expression in the human lung have yet to be determined, but it seems likely that readministration will be necessary over the lifetime of an individual. Unfortunately, we have found that transduction by a second administration of an AAV2 vector is blocked, presumably due to neutralizing antibodies generated in response to the primary vector exposure. Here, we have explored the use of AAV2 vectors pseudotyped with capsid proteins from AAV serotypes 2, 3, and 6 for readministration in the mouse lung. We found that an AAV6 vector transduced airway epithelial and alveolar cells in the lung at rates that were at least as high as those of AAV2 pseudotype vectors, while transduction rates mediated by AAV3 were much lower. AAV6 pseudotype vector transduction was unaffected by prior administration of an AAV2 or AAV3 vector, and transduction by an AAV2 pseudotype vector was unaffected by prior AAV6 vector administration, showing that cross-reactive neutralizing antibodies against AAV2 and AAV6 are not generated in mice. Interestingly, while prior administration of an AAV2 vector completely blocked transduction by a second AAV2 pseudotype vector, prior administration of an AAV6 vector only partially inhibited transduction by a second administration of an AAV6 pseudotype vector. Analysis of sera obtained from mice and humans showed that AAV6 is less immunogenic than AAV2, which helps explain this finding. These results support the development of AAV6 vectors for lung gene therapy both alone and in combination with AAV2 vectors. PMID:10627564

Return of epidemic dengue in the United States: implications for the public health practitioner.

PubMed

Bouri, Nidhi; Sell, Tara Kirk; Franco, Crystal; Adalja, Amesh A; Henderson, D A; Hynes, Noreen A

2012-01-01

Conditions that facilitate sustained dengue transmission exist in the United States, and outbreaks have occurred during the past decade in Texas, Hawaii, and Florida. More outbreaks can also be expected in years to come. To combat dengue, medical and public health practitioners in areas with mosquito vectors that are competent to transmit the virus must be aware of the threat of reemergent dengue, and the need for early reporting and control to reduce the impact of dengue outbreaks. Comprehensive dengue control includes human and vector surveillance, vector management programs, and community engagement efforts. Public health, medical, and vector-control communities must collaborate to prevent and control disease spread. Policy makers should understand the role of mosquito abatement and community engagement in the prevention and control of the disease.

Vaccination strategies for SIR vector-transmitted diseases.

PubMed

Cruz-Pacheco, Gustavo; Esteva, Lourdes; Vargas, Cristobal

2014-08-01

Vector-borne diseases are one of the major public health problems in the world with the fastest spreading rate. Control measures have been focused on vector control, with poor results in most cases. Vaccines should help to reduce the diseases incidence, but vaccination strategies should also be defined. In this work, we propose a vector-transmitted SIR disease model with age-structured population subject to a vaccination program. We find an expression for the age-dependent basic reproductive number R(0), and we show that the disease-free equilibrium is locally stable for R(0) ≤ 1, and a unique endemic equilibrium exists for R(0) > 1. We apply the theoretical results to public data to evaluate vaccination strategies, immunization levels, and optimal age of vaccination for dengue disease.

Effective Vectorization with OpenMP 4.5

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

Huber, Joseph N.; Hernandez, Oscar R.; Lopez, Matthew Graham

This paper describes how the Single Instruction Multiple Data (SIMD) model and its extensions in OpenMP work, and how these are implemented in different compilers. Modern processors are highly parallel computational machines which often include multiple processors capable of executing several instructions in parallel. Understanding SIMD and executing instructions in parallel allows the processor to achieve higher performance without increasing the power required to run it. SIMD instructions can significantly reduce the runtime of code by executing a single operation on large groups of data. The SIMD model is so integral to the processor s potential performance that, if SIMDmore » is not utilized, less than half of the processor is ever actually used. Unfortunately, using SIMD instructions is a challenge in higher level languages because most programming languages do not have a way to describe them. Most compilers are capable of vectorizing code by using the SIMD instructions, but there are many code features important for SIMD vectorization that the compiler cannot determine at compile time. OpenMP attempts to solve this by extending the C++/C and Fortran programming languages with compiler directives that express SIMD parallelism. OpenMP is used to pass hints to the compiler about the code to be executed in SIMD. This is a key resource for making optimized code, but it does not change whether or not the code can use SIMD operations. However, in many cases critical functions are limited by a poor understanding of how SIMD instructions are actually implemented, as SIMD can be implemented through vector instructions or simultaneous multi-threading (SMT). We have found that it is often the case that code cannot be vectorized, or is vectorized poorly, because the programmer does not have sufficient knowledge of how SIMD instructions work.« less

Attacking the mosquito on multiple fronts: Insights from the Vector Control Optimization Model (VCOM) for malaria elimination.

PubMed

Kiware, Samson S; Chitnis, Nakul; Tatarsky, Allison; Wu, Sean; Castellanos, Héctor Manuel Sánchez; Gosling, Roly; Smith, David; Marshall, John M

2017-01-01

Despite great achievements by insecticide-treated nets (ITNs) and indoor residual spraying (IRS) in reducing malaria transmission, it is unlikely these tools will be sufficient to eliminate malaria transmission on their own in many settings today. Fortunately, field experiments indicate that there are many promising vector control interventions that can be used to complement ITNs and/or IRS by targeting a wide range of biological and environmental mosquito resources. The majority of these experiments were performed to test a single vector control intervention in isolation; however, there is growing evidence and consensus that effective vector control with the goal of malaria elimination will require a combination of interventions. We have developed a model of mosquito population dynamic to describe the mosquito life and feeding cycles and to optimize the impact of vector control intervention combinations at suppressing mosquito populations. The model simulations were performed for the main three malaria vectors in sub-Saharan Africa, Anopheles gambiae s.s, An. arabiensis and An. funestus. We considered areas having low, moderate and high malaria transmission, corresponding to entomological inoculation rates of 10, 50 and 100 infective bites per person per year, respectively. In all settings, we considered baseline ITN coverage of 50% or 80% in addition to a range of other vector control tools to interrupt malaria transmission. The model was used to sweep through parameters space to select the best optimal intervention packages. Sample model simulations indicate that, starting with ITNs at a coverage of 50% (An. gambiae s.s. and An. funestus) or 80% (An. arabiensis) and adding interventions that do not require human participation (e.g. larviciding at 80% coverage, endectocide treated cattle at 50% coverage and attractive toxic sugar baits at 50% coverage) may be sufficient to suppress all the three species to an extent required to achieve local malaria elimination. The Vector Control Optimization Model (VCOM) is a computational tool to predict the impact of combined vector control interventions at the mosquito population level in a range of eco-epidemiological settings. The model predicts specific combinations of vector control tools to achieve local malaria elimination in a range of eco-epidemiological settings and can assist researchers and program decision-makers on the design of experimental or operational research to test vector control interventions. A corresponding graphical user interface is available for national malaria control programs and other end users.

Developmental Scientist | Center for Cancer Research

Cancer.gov

PROGRAM DESCRIPTION Within the Leidos Biomedical Research Inc.’s Clinical Research Directorate, the Clinical Monitoring Research Program (CMRP) provides high-quality comprehensive and strategic operational support to the high-profile domestic and international clinical research initiatives of the National Cancer Institute (NCI), National Institute of Allergy and Infectious Diseases (NIAID), Clinical Center (CC), National Institute of Heart, Lung and Blood Institute (NHLBI), National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), National Center for Advancing Translational Sciences (NCATS), National Institute of Neurological Disorders and Stroke (NINDS), and the National Institute of Mental Health (NIMH). Since its inception in 2001, CMRP’s ability to provide rapid responses, high-quality solutions, and to recruit and retain experts with a variety of backgrounds to meet the growing research portfolios of NCI, NIAID, CC, NHLBI, NIAMS, NCATS, NINDS, and NIMH has led to the considerable expansion of the program and its repertoire of support services. CMRP’s support services are strategically aligned with the program’s mission to provide comprehensive, dedicated support to assist National Institutes of Health researchers in providing the highest quality of clinical research in compliance with applicable regulations and guidelines, maintaining data integrity, and protecting human subjects. For the scientific advancement of clinical research, CMRP services include comprehensive clinical trials, regulatory, pharmacovigilance, protocol navigation and development, and programmatic and project management support for facilitating the conduct of 400+ Phase I, II, and III domestic and international trials on a yearly basis. These trials investigate the prevention, diagnosis, treatment of, and therapies for cancer, influenza, HIV, and other infectious diseases and viruses such as hepatitis C, tuberculosis, malaria, and Ebola virus; heart, lung, and blood diseases and conditions; parasitic infections; rheumatic and inflammatory diseases; and rare and neglected diseases. CMRP’s collaborative approach to clinical research and the expertise and dedication of staff to the continuation and success of the program’s mission has contributed to improving the overall standards of public health on a global scale. The Clinical Monitoring Research Program (CMRP) provides quality assurance and regulatory compliance support to the National Cancer Institute’s (NCI’s), Center for Cancer Research (CCR), Surgery Branch (SB). KEY ROLES/RESPONSIBILITIES - THIS POSITION IS CONTINGENT UPON FUNDING APPROVAL The Developmental Scientist will: Provide support and advisement to the development of the T Cell receptor gene therapy protocols. Establishes, implements and maintains standardized processes and assesses performance to make recommendations for improvement. Provides support and guidance to the cellular therapy or vector production facilities at the NIH Clinical Center engaged in the manufacture of patient-specific therapies. Manufactures cellular therapy products for human use. Develops and manufactures lentiviral and/or retroviral vectors. Prepares technical reports, abstracts, presentations and program correspondence concerning assigned projects through research and analysis of information relevant to government policy, regulations and other relevant data and monitor all assigned programs for compliance. Provides project management support with planning and development of project schedules and deliverables, tracking project milestones, managing timelines, preparing status reports and monitoring progress ensuring adherence to deadlines. Facilitates communication through all levels of staff by functioning as a liaison between internal departments, senior management, and the customer. Serves as a leader/mentor to administrative staff and prepares employee performance evaluations. Develops and implements procedures/programs to ensure effective and efficient business and operational processes. Identifies potential bottlenecks in upcoming development processes and works with team members and senior management for resolution. Analyzes and tracks initiatives and contracts. Coordinates and reviews daily operations and logistics, including purchasing and shipping of miscellaneous equipment, laboratory and office supplies to ensure compliance with appropriate government regulations. Coordinates the administrative, fiscal, contractual, and quality aspects of all projects. Ensures that internal budgets, schedules and performance requirements are met. Monitors workflow and timelines to ensure production operations are on schedule and adequate raw materials and supplies are available. Ensures all activities are in compliance with applicable federal regulations and guidelines and proper testing/validation activities have been scheduled and conducted. Regularly interacts with senior or executive management both internally and externally, on matters concerning several functional areas such as operations, quality control and quality assurance. Participates in planning facility or operations modifications, upgrades and renovations. Performs technical audits of outsourced contractors in conjunction with Quality Assurance and or Quality Control. Assists in the evaluation and selection of staff, planning and coordination of training, assigning of tasks and scheduling workloads and evaluating overall performance. This position is located in Bethesda, Maryland.

Decision Engines for Software Analysis Using Satisfiability Modulo Theories Solvers

NASA Technical Reports Server (NTRS)

Bjorner, Nikolaj

2010-01-01

The area of software analysis, testing and verification is now undergoing a revolution thanks to the use of automated and scalable support for logical methods. A well-recognized premise is that at the core of software analysis engines is invariably a component using logical formulas for describing states and transformations between system states. The process of using this information for discovering and checking program properties (including such important properties as safety and security) amounts to automatic theorem proving. In particular, theorem provers that directly support common software constructs offer a compelling basis. Such provers are commonly called satisfiability modulo theories (SMT) solvers. Z3 is a state-of-the-art SMT solver. It is developed at Microsoft Research. It can be used to check the satisfiability of logical formulas over one or more theories such as arithmetic, bit-vectors, lists, records and arrays. The talk describes some of the technology behind modern SMT solvers, including the solver Z3. Z3 is currently mainly targeted at solving problems that arise in software analysis and verification. It has been applied to various contexts, such as systems for dynamic symbolic simulation (Pex, SAGE, Vigilante), for program verification and extended static checking (Spec#/Boggie, VCC, HAVOC), for software model checking (Yogi, SLAM), model-based design (FORMULA), security protocol code (F7), program run-time analysis and invariant generation (VS3). We will describe how it integrates support for a variety of theories that arise naturally in the context of the applications. There are several new promising avenues and the talk will touch on some of these and the challenges related to SMT solvers. Proceedings

Chagas disease: national survey of seroprevalence in children under five years of age conducted in 2008

PubMed Central

Russomando, Graciela; Cousiño, Blanca; Sanchez, Zunilda; Franco, Laura X; Nara, Eva M; Chena, Lilian; Martínez, Magaly; Galeano, María E; Benitez, Lucio

2017-01-01

BACKGROUND Since the early 1990s, programs to control Chagas disease in South America have focused on eradicating domiciliary Triatoma infestans, the main vector. Seroprevalence studies of the chagasic infection are included as part of the vector control programs; they are essential to assess the impact of vector control measures and to monitor the prevention of vector transmission. OBJECTIVE To assess the interruption of domiciliary vector transmission of Chagas disease by T. infestans in Paraguay by evaluating the current state of transmission in rural areas. METHODS A survey of seroprevalence of Chagas disease was carried out in a representative sample group of Paraguayans aged one to five years living in rural areas of Paraguay in 2008. Blood samples collected on filter paper from 12,776 children were tested using an enzyme-linked immunosorbent assay. Children whose serology was positive or undetermined (n = 41) were recalled to donate a whole blood sample for retesting. Their homes were inspected for current triatomine infestation. Blood samples from their respective mothers were also collected and tested to check possible transmission of the disease by a congenital route. FINDINGS A seroprevalence rate of 0.24% for Trypanosoma cruzi infection was detected in children under five years of age among the country’s rural population. Our findings indicate that T. cruzi was transmitted to these children vertically. The total number of infected children, aged one to five years living in these departments, was estimated at 1,691 cases with an annual incidence of congenital transmission of 338 cases per year. MAIN CONCLUSION We determined the impact of vector control in the transmission of T. cruzi, following uninterrupted vector control measures employed since 1999 in contiguous T. infestans-endemic areas of Paraguay, and this allowed us to estimate the degree of risk of congenital transmission in the country. PMID:28443980

Peridomestic Aedes malayensis and Aedes albopictus are capable vectors of arboviruses in cities.

PubMed

Mendenhall, Ian H; Manuel, Menchie; Moorthy, Mahesh; Lee, Theodore T M; Low, Dolyce H W; Missé, Dorothée; Gubler, Duane J; Ellis, Brett R; Ooi, Eng Eong; Pompon, Julien

2017-06-01

Dengue and chikungunya are global re-emerging mosquito-borne diseases. In Singapore, sustained vector control coupled with household improvements reduced domestic mosquito populations for the past 45 years, particularly the primary vector Aedes aegypti. However, while disease incidence was low for the first 30 years following vector control implementation, outbreaks have re-emerged in the past 15 years. Epidemiological observations point to the importance of peridomestic infection in areas not targeted by control programs. We investigated the role of vectors in peri-domestic areas. We carried out entomological surveys to identify the Aedes species present in vegetated sites in highly populated areas and determine whether mosquitoes were present in open-air areas frequented by people. We compared vector competence of Aedes albopictus and Aedes malayensis with Ae. aegypti after oral infection with sympatric dengue serotype 2 and chikungunya viruses. Mosquito saliva was tested for the presence of infectious virus particles as a surrogate for transmission following oral infection. We identified Aedes albopictus and Aedes malayensis throughout Singapore and quantified their presence in forested and opened grassy areas. Both Ae. albopictus and Ae. malayensis can occupy sylvatic niches and were highly susceptible to both arboviruses. A majority of saliva of infected Ae. malayensis contained infectious particles for both viruses. Our study reveals the prevalence of competent vectors in peri-domestic areas, including Ae. malayensis for which we established the vector status. Epidemics can be driven by infection foci, which are epidemiologically enhanced in the context of low herd immunity, selective pressure on arbovirus transmission and the presence of infectious asymptomatic persons, all these conditions being present in Singapore. Learning from Singapore's vector control success that reduced domestic vector populations, but has not sustainably reduced arboviral incidence, we suggest including peri-domestic vectors in the scope of vector management.

Emergence and Prevalence of Human Vector-Borne Diseases in Sink Vector Populations

PubMed Central

Rascalou, Guilhem; Pontier, Dominique; Menu, Frédéric; Gourbière, Sébastien

2012-01-01

Vector-borne diseases represent a major public health concern in most tropical and subtropical areas, and an emerging threat for more developed countries. Our understanding of the ecology, evolution and control of these diseases relies predominantly on theory and data on pathogen transmission in large self-sustaining ‘source’ populations of vectors representative of highly endemic areas. However, there are numerous places where environmental conditions are less favourable to vector populations, but where immigration allows them to persist. We built an epidemiological model to investigate the dynamics of six major human vector borne-diseases in such non self-sustaining ‘sink’ vector populations. The model was parameterized through a review of the literature, and we performed extensive sensitivity analysis to look at the emergence and prevalence of the pathogen that could be encountered in these populations. Despite the low vector abundance in typical sink populations, all six human diseases were able to spread in 15–55% of cases after accidental introduction. The rate of spread was much more strongly influenced by vector longevity, immigration and feeding rates, than by transmission and virulence of the pathogen. Prevalence in humans remained lower than 5% for dengue, leishmaniasis and Japanese encephalitis, but substantially higher for diseases with longer duration of infection; malaria and the American and African trypanosomiasis. Vector-related parameters were again the key factors, although their influence was lower than on pathogen emergence. Our results emphasize the need for ecology and evolution to be thought in the context of metapopulations made of a mosaic of sink and source habitats, and to design vector control program not only targeting areas of high vector density, but working at a larger spatial scale. PMID:22629337

Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2011-04-21

In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

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.

User's guide to STIPPAN: A panel method program for slotted tunnel interference prediction

NASA Technical Reports Server (NTRS)

Kemp, W. B., Jr.

1985-01-01

Guidelines are presented for use of the computer program STIPPAN to simulate the subsonic flow in a slotted wind tunnel test section with a known model disturbance. Input data requirements are defined in detail and other aspects of the program usage are discussed in more general terms. The program is written for use in a CDC CYBER 200 class vector processing system.

Computer program for calculating and plotting fire direction and rate of spread.

Treesearch

James E. Eenigenburg

1987-01-01

Presents an analytical procedure that uses a FORTRAN 77 program to estimate fire direction and rate of spread. The program also calculates the variability of these parameters, both for subsections of the fire and for the fires as a whole. An option in the program allows users with a CALCOMP plotter to obtain a map of the fire with spread vectors.

Extraction and classification of 3D objects from volumetric CT data

NASA Astrophysics Data System (ADS)

Song, Samuel M.; Kwon, Junghyun; Ely, Austin; Enyeart, John; Johnson, Chad; Lee, Jongkyu; Kim, Namho; Boyd, Douglas P.

2016-05-01

We propose an Automatic Threat Detection (ATD) algorithm for Explosive Detection System (EDS) using our multistage Segmentation Carving (SC) followed by Support Vector Machine (SVM) classifier. The multi-stage Segmentation and Carving (SC) step extracts all suspect 3-D objects. The feature vector is then constructed for all extracted objects and the feature vector is classified by the Support Vector Machine (SVM) previously learned using a set of ground truth threat and benign objects. The learned SVM classifier has shown to be effective in classification of different types of threat materials. The proposed ATD algorithm robustly deals with CT data that are prone to artifacts due to scatter, beam hardening as well as other systematic idiosyncrasies of the CT data. Furthermore, the proposed ATD algorithm is amenable for including newly emerging threat materials as well as for accommodating data from newly developing sensor technologies. Efficacy of the proposed ATD algorithm with the SVM classifier is demonstrated by the Receiver Operating Characteristics (ROC) curve that relates Probability of Detection (PD) as a function of Probability of False Alarm (PFA). The tests performed using CT data of passenger bags shows excellent performance characteristics.

HAL/S programmer's guide. [space shuttle flight software language

NASA Technical Reports Server (NTRS)

Newbold, P. M.; Hotz, R. L.

1974-01-01

HAL/S is a programming language developed to satisfy the flight software requirements for the space shuttle program. The user's guide explains pertinent language operating procedures and described the various HAL/S facilities for manipulating integer, scalar, vector, and matrix data types.

Frankliniella occidentalis (Pergande) integrated pest management programs for fruiting vegetables in Florida

USDA-ARS?s Scientific Manuscript database

The spread of the western flower thrips Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae) resulted in the worldwide destabilization of established integrated pest management programs for many crops. Efforts to control the pest and the thrips-vectored tospoviruses with calendar applicat...

A method of evolving novel feature extraction algorithms for detecting buried objects in FLIR imagery using genetic programming

NASA Astrophysics Data System (ADS)

Paino, A.; Keller, J.; Popescu, M.; Stone, K.

2014-06-01

In this paper we present an approach that uses Genetic Programming (GP) to evolve novel feature extraction algorithms for greyscale images. Our motivation is to create an automated method of building new feature extraction algorithms for images that are competitive with commonly used human-engineered features, such as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). The evolved feature extraction algorithms are functions defined over the image space, and each produces a real-valued feature vector of variable length. Each evolved feature extractor breaks up the given image into a set of cells centered on every pixel, performs evolved operations on each cell, and then combines the results of those operations for every cell using an evolved operator. Using this method, the algorithm is flexible enough to reproduce both LBP and HOG features. The dataset we use to train and test our approach consists of a large number of pre-segmented image "chips" taken from a Forward Looking Infrared Imagery (FLIR) camera mounted on the hood of a moving vehicle. The goal is to classify each image chip as either containing or not containing a buried object. To this end, we define the fitness of a candidate solution as the cross-fold validation accuracy of the features generated by said candidate solution when used in conjunction with a Support Vector Machine (SVM) classifier. In order to validate our approach, we compare the classification accuracy of an SVM trained using our evolved features with the accuracy of an SVM trained using mainstream feature extraction algorithms, including LBP and HOG.

GIS data models for coal geology

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

McColloch, G.H. Jr.; Timberlake, K.J.; Oldham, A.V.

A variety of spatial data models can be applied to different aspects of coal geology. The simple vector data models found in various Computer Aided Drafting (CAD) programs are sometimes used for routine mapping and some simple analyses. However, more sophisticated applications that maintain the topological relationships between cartographic elements enhance analytical potential. Also, vector data models are best for producing various types of high quality, conventional maps. The raster data model is generally considered best for representing data that varies continuously over a geographic area, such as the thickness of a coal bed. Information is lost when contour linesmore » are threaded through raster grids for display, so volumes and tonnages are more accurately determined by working directly with raster data. Raster models are especially well suited to computationally simple surface-to-surface analysis, or overlay functions. Another data model, triangulated irregular networks (TINs) are superior at portraying visible surfaces because many TIN programs support break fines. Break lines locate sharp breaks in slope such as those generated by bodies of water or ridge crests. TINs also {open_quotes}honor{close_quotes} data points so that a surface generated from a set of points will be forced to pass through those points. TINs or grids generated from TINs, are particularly good at determining the intersections of surfaces such as coal seam outcrops and geologic unit boundaries. No single technique works best for all coal-related applications. The ability to use a variety of data models, and transform from one model to another is essential for obtaining optimum results in a timely manner.« less

The Value of Information in Decision-Analytic Modeling for Malaria Vector Control in East Africa.

PubMed

Kim, Dohyeong; Brown, Zachary; Anderson, Richard; Mutero, Clifford; Miranda, Marie Lynn; Wiener, Jonathan; Kramer, Randall

2017-02-01

Decision analysis tools and mathematical modeling are increasingly emphasized in malaria control programs worldwide to improve resource allocation and address ongoing challenges with sustainability. However, such tools require substantial scientific evidence, which is costly to acquire. The value of information (VOI) has been proposed as a metric for gauging the value of reduced model uncertainty. We apply this concept to an evidenced-based Malaria Decision Analysis Support Tool (MDAST) designed for application in East Africa. In developing MDAST, substantial gaps in the scientific evidence base were identified regarding insecticide resistance in malaria vector control and the effectiveness of alternative mosquito control approaches, including larviciding. We identify four entomological parameters in the model (two for insecticide resistance and two for larviciding) that involve high levels of uncertainty and to which outputs in MDAST are sensitive. We estimate and compare a VOI for combinations of these parameters in evaluating three policy alternatives relative to a status quo policy. We find having perfect information on the uncertain parameters could improve program net benefits by up to 5-21%, with the highest VOI associated with jointly eliminating uncertainty about reproductive speed of malaria-transmitting mosquitoes and initial efficacy of larviciding at reducing the emergence of new adult mosquitoes. Future research on parameter uncertainty in decision analysis of malaria control policy should investigate the VOI with respect to other aspects of malaria transmission (such as antimalarial resistance), the costs of reducing uncertainty in these parameters, and the extent to which imperfect information about these parameters can improve payoffs. © 2016 Society for Risk Analysis.

Research in computer science

NASA Technical Reports Server (NTRS)

Ortega, J. M.

1984-01-01

The research efforts of University of Virginia students under a NASA sponsored program are summarized and the status of the program is reported. The research includes: testing method evaluations for N version programming; a representation scheme for modeling three dimensional objects; fault tolerant protocols for real time local area networks; performance investigation of Cyber network; XFEM implementation; and vectorizing incomplete Cholesky conjugate gradients.

Multiple linear regression analysis

NASA Technical Reports Server (NTRS)

Edwards, T. R.

1980-01-01

Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.

Vector Fluxgate Magnetometer (VMAG) Development for DSX

DTIC Science & Technology

2010-06-03

AFRL-RV-HA-TR-2010-1056 Vector Fluxgate Magnetometer (VMAG) Development for DSX Mark B. Moldwin UCLA Institute of Geophysics... Fluxgate Magnetometer (VMAG) Development for DSX 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 62601F 6. AUTHOR(S) Mark B. Moldwin 5d. PROJECT...axis fluxgate magnetometer for the AFRL-mission. The instrument is designed to measure the medium-Earth orbit geomagnetic field with precision of 0.1